Aiming at the state master-slave synchronization problem of discrete-time output-coupled neural networks (OCNNs), this study proposes a novel control framework. Deviating from the existing studies, this study aims at the constraint that the output state cannot obtain all neuron state information, innovatively constructs full-dimensional observers in the master/slave system respectively to realize state reconstruction, which solves the problem of missing reference trajectory caused by incomplete state information of the master system, and realizes state synchronization for the first time under the output-coupled framework. A round-robin (RR) protocol is introduced to dynamically schedule the output-coupled communication among neurons to address the transmission efficiency bottleneck under limited bandwidth resources. To allocate communication resources more intelligently, a novel event-triggered (ET) mechanism is designed. Under this mechanism, the triggering threshold is constructed based on the last triggering instant and is updated according to the most current triggering instant. It significantly reduces overall resource consumption while ensuring control performance. Then, the joint Lyapunov function is constructed based on the designed observer-controller-protocol interaction dynamic model. It derived a quantitative relationship between the triggering frequency and the error decay boundary, thereby providing a quantifiable basis for precision tuning in network control. Finally, the effectiveness of the proposed method is verified by circuit simulation.

Defect inversion is a critical quantitative assessment technique to evaluate the integrity of energy transportation systems. In practice, non-stationary states and frequent situation switches in real-world industrial processes make it difficult to acquire ideal sensor signals with precise annotations, there by compromising accurate defect inversion. Simulation systems is a viable solution. However, the dramatic inter-domain disparity between simulated and real system poses challenges to existing studies. Moreover, intra-domain divergence within the real energy transportation systems is also unexplored. To address the above issues, an intra- and inter-domain interactive learning method is proposed for simulation-to-real (sim2real) defect inversion. Specifically, reality-aware feature stylization is proposed which diversifies simulated signals with real style variants, so as to alleviate the domain gap initially. Then, bi-level inter-domain alignment is proposed to not only penalize hard-to-align samples in abstract feature space, but also innovatively exploits intrinsic geometric relations of sensor signals to guide inter-domain gaps. Next, physical-informed intra-domain alignment is proposed where a similarity matrix is constructed to pursue the consistency between sensor signals and physical knowledge, and it is also served as pseudo-labels to constrain inter-domain adaptation, so that inter- and intra- domain adaptation interact with united strength to enhance information exchange. Finally, the proposed method is systematically validated on energy transportation experimental platform and competitive results are obtained, which shows its promise in real-world industrial processes.

This paper explores event-triggered optimal containment control for stochastic multiagent systems (MASs) with asymmetric state constraints. A practical fixed-time optimal containment controller is developed using the reinforcement learning (RL) algorithm under the identifier-critic-actor framework, where a Hamilton-Jacobi-Bellman (HJB) equation with a stochastic nonlinear term is formulated to optimize control performance. Then, a dynamic event-triggered optimal containment control scheme is presented, and the estimated neural networks (NNs) weights are introduced as the threshold function to reduce communication overhead. Additionally, an offset state variable related to asymmetric state constraints is designed. By applying state-space translation, the conversion from asymmetric to symmetric state constraints is achieved, and the complexity of controller design is reduced. The validity of the presented method is rendered by simulations.

The robustH∞fault-tolerant control problem for a class of nonlinear fractional-order systems described by T-S fuzzy models is investigated in this paper. A transformed system is firstly constructed according to the original system. Then, a fractional-order intermediate observer is proposed. It can obtain the state and actuator fault of the transformed system, which indirectly achieves the simultaneous estimation of states, faults and disturbances of the original system. Unlike the strategy of suppressing or decoupling the disturbances in traditional fault estimation methods of nonlinear fractional-order systems, this paper focuses on estimating the disturbances directly. This way not only avoids handling the disturbances or the dynamics of faults via theH∞method, but also does not have strict equation constraints required in the decoupling method. Additionally, an active fault-tolerant controller is designed that dynamically adjusts its actions based on the information obtained by the observer, thereby ensuring system stability under multiple faults and disturbances. Finally, the effectiveness of the proposed method is validated on two typical application circuits.

This paper investigates the problem of dynamic output feedback fault-tolerant control (DOF-FTC) for T-S fuzzy fractional-order systems (TSFFOSs) subject to multiple faults and mismatched disturbances. First, the system is preprocessed using augmentation and desingularization techniques. Then, by optimizing the structure of the traditional fractional-order intermediate observer, the joint estimation of states, faults and mismatched disturbances in the system is achieved. In particular, the estimation performance is significantly improved. More importantly, during the design of the observer, the reliance onH∞methods and decoupling methods for disturbance handling is eliminated. Furthermore, a novel DOF-FTC strategy is developed, which does not require state information. The designed controller can be adjusted online based on the estimation of faults and disturbances to ensure system stability. Finally, a permanent magnet synchronous motor model is employed to verify the effectiveness of the proposed method.

New results on the consensus of multi-agent systems via periodic dynamic event-triggered control are presented under cyber attack. Distinct from traditional Denial-of-Service (DoS) attack model frameworks, this study proposes the novel concept of DoS attack intensity for the first time, remedies the deficiencies of existing DoS attack model frameworks through the DoS attack intensity function, and analyzes the impact mechanism of different DoS attack intensities on multi-agent system consensus. Secondly, to reduce redundant data transmission and alleviate the communication burden during the delay phase caused by low-intensity DoS attacks, i.e., Degradation-of-Service (DeoS) attacks, a periodic dynamic event-triggered mechanism based on delayed state information is designed. Additionally, to derive conditions for multi-agent consensus, the Halanay-type inequality is generalized and some sufficient conditions for achieving multi-agent consensus under DeoS attacks are presented based on the generalized Halanay-type inequality. For the validation of the theoretical validity, the proposed approach is applied to single-link robotic arms systems. Through numerical simulations, it is verified that the single-link robotic arms systems can converge to an average consensus state when the system is subjected to DeoS attacks.

In this paper, we consider the islanded microgrid restoration with communication link faults, where faults refer to certain classes of data manipulation attacks. To compensate for the impact of faults in communication links on microgrid stability, a distributed adaptive event-triggered compensator with adaptive parameters has been designed. This avoids the use of global information and reduces communication redundancy. To achieve voltage and frequency recovery, an distributed secondary control strategy is proposed. Using Lyapunov stability theorem, it is proved that the proposed method is effective in counteracting the primary control bias that exists in islanded microgrids in the presence of communication link faults and limited computational resources. The main advantage of our method is the scalability and resilience. Finally, the paper presents the results of a simulation test of an islanded microgrid consisting of four DGs with faults in communication links. These results show that the proposed secondary control strategy is effective.

This study addresses the bipartite output consensus problem of heterogeneous uncertain multiagent systems (MASs) under hybrid event-triggered control mechanism. Initially, a fully distributed adaptive bipartite compensator is composed, which consists of time-varying coupling weights and hybrid event-triggered mechanism to estimate the state of the leader. The hybrid event-triggered mechanism includes the event-triggered mechanism for the leader and the edge-event triggered mechanism for all edges to reduce the information transmission among agents. Then, a novel distributed output feedback controller is put forward for uncertain system dynamics. With the aid of the proposed controller, the bipartite output consensus problem of heterogeneous uncertain MASs can be resolved. Furthermore, the above results can be extended to the case of switching topology. Lastly, the validity of the theoretical findings is confirmed through four simulation examples.

This article investigates a conflict-constrained control method for switched multiagent systems with nonaffine nonlinear faults. Existing studies on state constraints often assume that the reference signal always stays within the constraint set. However, in practice, constraints may be dynamically detected during system operation and conflict with predefined reference signals, causing brief violations of the constraint boundaries. When the reference signal cannot remain within the prescribed range, many backstepping control methods based on barrier Lyapunov function and nonlinear transformations become ineffective. To address this, a new safe reference signal is constructed by using a virtual circle approach, and a conflict-constrained control method is proposed. By combining a common Lyapunov function and the radial basis function neural network, the effects of switching behavior and nonaffine nonlinear faults can be effectively compensated. A shift function is also introduced in the coordinate transformation to further relax constraints on initial values. The proposed method is validated through multiple simulation experiments.

This research considers the observer-based adaptive fuzzy secure consensus control issue for high-order nonlinear multiagent systems (MASs) against communication delays and deception attacks under jointly connected switching topology. To address the challenges of time-varying communication delays, deception attacks, and leader-state inaccessibility in MASs with jointly connected switching topology, this article develops a distributed consensus observer with delay-attack resilience. The proposed observer simultaneously compensates for time-varying delays and counteracts deception attacks while reconstructing the leader's state information through a consensus observer. To handle system uncertainties with unknown nonlinear functions, a fuzzy logic system (FLS) is employed to approximate the unknown dynamics. A fuzzy state observer is subsequently constructed to reconstruct the unmeasurable states by utilizing accessible post-attack signals and applying adaptive approximation technology. With the help of the backstepping design scheme and the adding power integral method, an observer-based adaptive fuzzy secure consensus control approach is proposed so that the designed communication-delay-attack-related distributed consensus observer errors converge to zero exponentially. Furthermore, the proposed consensus control protocol ensures the stability of high-order MASs while achieving that tracking errors converge in the neighborhood of the origin. The validation through two simulation scenarios demonstrates the protocol's effectiveness.

This article explores predictor-based fractional-order sliding mode load frequency control for interconnected power systems, accounting for input delay. First, a predictor-based method is developed to deal with the input delay. By designing a predictor to accurately predict the future state, the delayed control input can be replaced by the delay-free control input. Then, a novel fractional-order sliding mode controller is designed, where the predictor is used rather than the system state, reducing the dependence on directly measurable system states and enhancing the dynamic response of the system. Furthermore, a disturbance observer is designed to estimate the disturbance, allowing the controller to correspondingly compensate for it, and the robustness of the system is improved. Finally, three cases are conducted to show the validity of the presented method.

Most output-constrained methods necessitate reference command within a predefined safe region, without considering cases where the command itself may conflict with safety boundaries. To handle this problem, this article proposes a predefined-time safe cooperative control scheme for multiagent systems under output constraints, privacy preservation and unknown disturbances. At the communication layer, an encryption-decryption mechanism is developed to safeguard information exchange among agents, preventing internal states from being identified by eavesdroppers. At the control layer, to ensure strict adherence to output constraints regardless of whether the original command complies with safety limits, an improved boundary protection method is explored to generate a safety reference trajectory, which is subsequently used in the controller design. Adaptive laws are then formulated to counteract the effects of unknown nonlinearities and disturbances. Finally, by leveraging predefined-time stability theory, a predefined-time safe cooperative controller is designed to ensure error convergence within a user-defined settling time. Theoretical analysis rigorously confirms the closed-loop stability, and simulations verify the effectiveness of the proposed method.

This article explores an adaptive security formation control issue for nonlinear multiagent systems (MASs) against unknown deception attacks. The stable operation of multiagent formation is highly dependent on effective communication transmission between agents. To realize the formation control task, first, a state observer is developed to estimate the states under FDI attacks. Then, the nonlinear state-dependent function is introduced to cope with the asymmetric constraints to ensure a safe and stable operation environment of the agent formation. Furthermore, with the help of coordinate transformation and fuzzy logic systems (FLSs), a fuzzy adaptive formation control scheme with an attack compensation mechanism is developed so that various desired formation patterns are achieved with free collision. Under the proposed scheme, the resulting formation tracking error is uniformly ultimately bounded, and the state constraint of the multiagent system is always maintained, even if the agents are subjected to malicious unknown deception attacks. The effectiveness of the control method is validated through simulation examples.

This article investigates the optimal containment control (OCC) problem of a class of nonlinear stochastic multiagent systems (MASs) under secure communication. A novel OCC strategy is designed, ensuring that all followers converge to the convex hull spanned by the leaders while maintaining secure information exchange and minimizing cost by the predefined performance function. To achieve this, an encryption and decryption mechanism is employed in the information exchange among agents, ensuring secure communication and preserving data integrity. Meanwhile, to solve the stochastic version of Hamilton-Jacobi-Bellman (HJB) equation arising in stochastic factor, a simplified adaptive dynamic programming (ADP) framework is introduced under the conditional expectation. Specifically, a single critic network weights tuning rule is developed based on the experience replay technique (ERT). The use of ERT relaxes the traditional persistence of excitation requirement. Theoretical analysis guarantees the uniform ultimate boundedness of the closed-loop system. The simulation results confirm the effectiveness of the designed OCC strategy.

The study investigates the problem of attack-compensated control for two-dimensional (2-D) fuzzy systems modeled by the Roesser framework, subject to the constraints of limited communication channels. A novel event-triggered stochastic protocol (ETSP) with nonhomogeneous sojourn probabilities is introduced to save communication resources. By randomly selecting a controller node at each time step to transmit signals, the protocol schedules communication between controllers and actuators, thereby reducing communication load. To address the injection of false data into control signals caused by network attacks, a compensation mechanism based on a sojourn-probability-based predictor is designed. Sufficient conditions are subsequently established, based on Lyapunov theory, to ensure the mean-square asymptotic stability of the closed-loop system while preserving the desired performance level. Finally, the particle swarm optimization (PSO) algorithm is employed to enhance the controller design, and a simulation example is provided to verify the effectiveness and applicability of the proposed method.

This paper addresses the tracking control problem for nonlinear multi-agent systems by developing a quantitative prescribed performance control framework. Under the backstepping design architecture, all virtual control signals and actual control inputs are constructed using quantized signals at each recursive step. A novel continuous and differentiable quantization function is employed to eliminate discontinuities commonly associated with traditional discrete quantizers, thereby ensuring smooth control transitions and reducing communication load. To strictly guarantee adherence to the prescribed tracking performance, a dynamic performance function incorporating quantized errors is further introduced. Moreover, the proposed scheme exhibits robustness against actuator faults and maintains satisfactory control accuracy in faulty scenarios. The effectiveness and superiority of the proposed approach are validated through comprehensive simulation.

This paper proposes an innovative Energy-Efficiency-Aware Fixed-Time (EEAFT) control strategy for distributed permanent-magnet synchronous motor systems. The primary contribution is achieving rapid, precise speed consensus while effectively balancing energy consumption against consensus error, a challenge often overlooked in fixed-time (FxT) control designs. A key novelty lies in the introduction of a real-time reward mechanism, uniquely derived from low-pass filtering both the system's consensus error and the control input signals. This adaptive reward function dynamically adjusts the upper bound on convergence time. When the convergence time becomes excessively stringent, a substantial increase in control effort is required. This can lead to energy spikes and hardware overload. To mitigate these risks, the bound on convergence time is then relaxed. Conversely, when large consensus errors necessitate an accelerated response, the bound on convergence time is tightened to expedite error correction. To enhance robustness against unmodeled friction, winding losses, and external disturbances, a fuzzy logic system is employed to approximate unknown nonlinear dynamics, while a disturbance observer compensates for residual errors. Rigorous theoretical analysis confirms the boundedness of all system signals and guarantees FxT convergence under the proposed EEAFT framework. Extensive experimental results demonstrate the effectiveness and superiority of the EEAFT-based controller, showcasing high-precision speed synchronization across multiple motors and achieving an improved trade-off between rapid transient response and overall energy efficiency.

This paper proposes a hierarchical fuzzy learning (HFL) framework that restructures fuzzy control synthesis around Virtual Tracking Targets (VTTs). First, we introduce a VTT-driven hierarchical architecture in which each non-output state is guided by an autonomous virtual agent that simultaneously (i) directs state evolution toward task-consistent trajectories and (ii) self-organizes its auxiliary control role via satisfaction of Hamilton-Jacobi-Bellman (HJB) conditions. This dual functionality departs from static virtual controls in fuzzy backstepping and endows the hierarchy with intrinsic adaptivity. Second, we establish a parallel computational paradigm for controller synthesis: instead of the canonical cascaded design that enforces strict layer-by-layer dependency, all VTTs are optimized concurrently. This parallelization not only removes the sequential bottleneck of adaptive/optimal backstepping but also yields scalability benefits for high-dimensional systems without sacrificing stability guarantees. Third, to handle unknown VTT derivatives that commonly hinder inverse/fuzzy designs, we embed a first-order low-pass filter to deliver real-time derivative surrogates, thereby avoiding explicit differentiation. A unified Lyapunov analysis proves uniformly ultimately bounded (UUB) of both the filtered tracking errors and the fuzzy weight estimation errors, providing a twofold convergence guarantee in fuzzy control. Comprehensive analyses characterize stability, and approximation properties; demonstrating improved tracking accuracy, reduced control energy due to parallel synthesis, and enhanced resilience under derivative uncertainty.

In this paper, a predictor-based fuzzy safety control framework is proposed for uncertain nonlinear systems with reference-independent output constraints. A safety-compliant reference regulation mechanism is developed to reshape potentially infeasible references into safety reference signals, thereby eliminating the conventional admissible-reference assumption. A novel dual-constraint framework is developed to simultaneously enforce output constraints and prescribed performance in a unified manner, allowing explicit boundary construction without undesirable coupling. To handle uncertainty, a predictor-based simplified fuzzy approximator is constructed, in which prediction errors rather than tracking errors are employed to update the norm of weight vector, substantially reducing computation complexity and alleviating transient chattering. Meanwhile, the use of prediction errors also decouples control and estimation loops, thereby simplifying the stability analysis. Furthermore, a new transient analysis theorem is established to quantify chattering degree and explicitly reveal the influence of design parameters. Rigorous Lyapunov-based analysis proves that all closed-loop signals remain bounded, and both safety and performance constraints can be strictly satisfied. Simulation results validate the usefulness and superiority the proposed method.

This paper investigates the prescribed-time (PST) bipartite time-varying output formation consensus problem for heterogeneous nonlinear multi-agent systems (HNMASs) subject to unknown leader input and actuator faults. To cope with the unknown leader's input, a distributed PST dynamic compensator is designed to estimate the leader-related signal and compensate for its effect on formation tracking. Meanwhile, by combining fuzzy logic systems with adaptive fault compensation, a fault bound-free fault-tolerant formation control strategy is developed for handling nonlinear function and actuator faults without requiring prior knowledge of the fault factors or their bounds. Under the proposed compensator-controller framework, the considered HNMASs are shown to achieve bipartite time-varying output formation consensus at a user-specified time, where the convergence time is independent of the control parameters and initial conditions. Finally, the theoretical results are corroborated through a detailed numerical simulation.

This work focuses on a constrained distributed optimization problem for nonlinear multiagent systems over directed communication networks. Under the information constraint that each agent observes only its own objective function and resource signals, a distributed observer is devised to locally reconstruct the constraint-related signals and Hessian-related information needed for optimization. Using these estimates, a preset-time (PST) distributed optimization scheme is formulated for the problem subject to a time-varying equality constraint and guarantees convergence to the optimal solution within a user-assigned time, irrespective of algorithm parameters. In contrast to existing prescribed-time optimization methods, the proposed approach applies to directed networks without requiring weight balance. Moreover, feasible initialization is not required, and the equality constraint is satisfied exactly at the PST and thereafter. The effectiveness and the practical applicability of the proposed algorithm are validated via an economic dispatch problem in power systems.

This article investigates a predefined-time optimized consensus secure control problem for nonlinear multiagent systems, where the leader to follower and follower to neighbor agent network communication are subjected to deferred denial-of-service (DoS) attacks. To observe the leader's state and mitigate the adverse effects of DoS attacks on the system, a switching consensus leader observer is designed. Through the synergistic use of backstepping control, predefined-time control theory, and adaptive dynamic programming, a Hamilton–Jacobi–Bellman equation is constructed for each subsystem to ensure the optimal control performance of the overall system. The identifier neural network is incorporated to approximate the unknown uncertainties existing in the system. Through the construction of the critic network, the proposed controller satisfies the Bellman optimality principle, from which the optimal controller of the system is derived. By employing the Lyapunov stability theorem, it is proven that all system signals remain bounded within the predefined time interval, and the followers' outputs ultimately synchronize with the leader's state. Finally, simulation studies are conducted to validate the feasibility and effectiveness of the proposed control scheme.

This article presents a constraint-based finite-time tracking control scheme for multivehicle systems (MVSs) with communication and operation restrictions, including network attacks and actuator faults. The stable operation of MVSs significantly depends on the effective communication transmission of state information (position and velocity) between vehicles and the ideal operation of the vehicle’s own actuators. However, the actual transmission signals may be tampered with due to malicious attacks, and different faults may occur during the operation of transportation. Therefore, first, a novel coordinate transformation is developed to cope with the difficulty of distorted position and velocity information, which will lead to follower vehicles making incorrect decisions. Then, an adaptive controller is designed with the help of the fuzzy approximation method to solve the problems of actuator fault compensation and unknown nonlinearity of vehicle’s dynamic. Besides, the nonlinear velocity constraint function is designed to avoid collisions and congestion between vehicles so as to ensure a safe and comfortable transportation environment. Not only a safe distance between vehicles is achieved in a finite time, but also the velocity constraint of vehicles is maintained based on the proposed finite-time control scheme. Finally, the simulation example elaborates the effectiveness of the presented method in this article.

Localization of multipoint weak leakage in energy transportation systems remains challenging due to signal-source mismatch and weak responses of negative pressure waves (NPWs). To address this issue, this article proposes a novel physics-informed localization framework that combines an attenuation-based NPW matching algorithm with a prior-embedded maximum entropy adaptive dynamic programming (MEADP) scheme. First, a matrix transformation-based matching algorithm establishes physically coherent correspondences between inlet and outlet NPWs. Building on this foundation, a prior-embedded MEADP localization strategy incorporates Gaussian priors derived from the attenuation physics of NPWs to guide policy optimization. Furthermore, a Wasserstein-distance regularization term is introduced to stabilize policy learning and ensure reliable convergence. Experimental results demonstrate that the proposed method effectively resolves multileak signal mismatches and achieves high-precision localization, with the minimum localization error remaining below 0.5%.

The integrated design of perception and enclosing control against an unknown target is a promising enabler for autonomous vehicles performing surveillance, entrapment, and escort missions. To address this challenge, this article develops an estimator–controller framework for target localization and enclosing under communication and measurement constraints. First, to improve the convergence performance, a novel fixed-time estimator is designed using bearing-only information, ensuring that target position is reconstructed within a fixed time. Then, to overcome the limitations of prior works that mainly rely on circular or elliptical orbits, a geometric path generator is exploited to synthesize arbitrary-shaped smooth orbits, based on which a fixed-time controller is constructed to guide the vehicle to surround the target along the prescribed path. For multivehicle scenarios, an arc length-guided decentralized formation strategy is proposed to maintain prespecified arc length separation between adjacent vehicles without relying on communication, which is particularly suitable for communication-denied environments. Finally, the fixed-time stability in the closed-loop system is rigorously proved by Lyapunov-based analysis, and both simulation and experimental results validate the effectiveness of the proposed framework.

This article proposes a dynamic self-triggered (DST) control strategy and a switching signal for a cascaded switched continuous stirred tank reactor (CSTR) system subject to asymmetric input constraints. First, the switched subsystems are established, and an improved nonquadratic performance function (PF) is designed, enabling the decentralized constrained switched control problem (CP) to be decomposed into a set of unconstrained optimal switched CPs for the individual subsystems. To reduce communication and computational burdens while eliminating the need for dedicated trigger-monitoring hardware, a dynamic self-triggering mechanism is incorporated into the controller. Subsequently, the DST Hamilton–Jacobi–Bellman equation associated with the transformed optimal switched CP is derived. To circumvent repeated trial-and-error and ease the selection of activation functions, a dual heuristic programming method utilizing echo state networks (ESNs) is adopted to approximate the PF. According to the Lyapunov theorem, the output weights of the ESN and the states of the closed-loop switched subsystems are proven to be uniformly ultimately bounded. Finally, the effectiveness of the proposed DST control strategy and switching signals is validated on a cascaded switched CSTR system comprising three reactors.

To address the issues of insufficient stability, low power allocation accuracy, and resource constraints in dc microgrids, this article proposes a distributed optimal control method based on edge computing. Adaptive dynamic programming method is introduced to address the nonlinear control problem, improving the stability and power allocation accuracy. A three-level cloud-edge-device control framework is constructed to improve system efficiency and flexibility combining distributed control and edge computing. The improved event-triggered control mechanism is designed to reduce unnecessary computation and improve the real-time responsiveness of the system. The practicality of the method is verified using a hardware-in-the-loop simulation platform based on the edge intelligent terminal.

Magnetic flux leakage (MFL) detection has been widely used in the pipeline integrity evaluation. However, the MFL signals will be distorted due to the speed effect, which reduces the accuracy of the defect size inversion severely. To solve above problems, a multiple information-guided collaborative network (MIG-CN) is proposed to invert defect sizes accurately for the distorted pipeline MFL signals caused by the speed effect. First, a multiview feature fusion method is proposed, which enables MIG-CN to predict the defect sizes of specific types accurately by fusing the features of specific views, where the mechanism features are injected into the network to fully exploit the physical information of the MFL signals, improving the inversion accuracy. Second, a physics-informed constrained network is proposed, in which a speed magnetic dipole model is developed to impose constraints from real physical laws with the speed effect, thus enhancing the physical plausibility of the inversion results. Third, a dynamic-static collaborative decision strategy is proposed, in which a two-point constrained quantile probability modelling is proposed to model the inversion results of the undistorted MFL signals and obtain the prior probability distribution. The accuracy and robustness of the inversion results at different speeds are improved by minimizing the difference between the probability distribution of the inversion results of the distorted MFL signals and those of the undistorted. The above three components are jointly trained and optimized to improve the inversion accuracy under the influence of speed effect. Finally, the experiments on real pipeline networks verifies the effectiveness of MIG-CN.

This article proposes a hierarchical neural learning (HNL) algorithm for optimal tracking control (OTC) of nonlinear strict-feedback systems (SFSs) with unmatched disturbances (uMDs) and unknown dynamics. Leveraging the recursive structure of SFSs, we introduce the virtual target (VT) construction scheme in which each VT is a nonlinear mapping of the current state and desired output, thereby eliminating the noncausal that typically plagues discrete-time SFS control. The VTs serve as auxiliary inputs for low-order subsystems, while a time-varying affine Hamilton-Jacobi-Isaacs (HJI) formulation establishes an explicit relationship between the auxiliary control and the disturbance. The controller is synthesized directly from input-output data, removing the need for an accurate plant model. Within an adaptive dynamic programming (ADP) framework, we further enhance the neural architecture by replacing the conventional action network with a tracking network (T-network) whose energy function merges gradient information with future tracking errors, ensuring that each policy update simultaneously reduces control effort and improves tracking accuracy. Simulations confirm that the proposed HNL scheme achieves outstanding performance in both (optimal) tracking modes, exhibiting strong robustness to uMDs and significant model uncertainties.

This paper investigates the robust consensus scheme for a specific type of leader-follower multi-agent systems (MASs) in the context of a human-cyber-physical interactive system. The system encounters frequent communication challenges characterized by packet loss and data leakage. To address these challenges, the paper proposes a secure decentralized scheme that leverages game theory to determine optimal policies in a zero-sum game. The scheme utilizes the adaptive dynamic programming (ADP) method, which involves a decentralized iterative process. The paper demonstrates that the generated sequence exhibits exponential convergence and provides the maximum iteration number based on the convergence error. To mitigate data leakage among players, the scheme incorporates a secure operation that involves encrypted data. This integration seamlessly combines data conversion and encryption-decryption into decentralized computation. Finally, the paper presents a simulation example to validate the efficacy of the consensus scheme.

This paper proposes a secure load frequency control (LFC) method for cyber-physical power systems integrated with photovoltaic energy and electric vehicles, addressing the challenges of asynchronous inputs and attack signals. A T-S fuzzy model is employed to effectively manage the uncertainties and nonlinearities of these integrated systems. A co-prediction approach is developed for simultaneous prediction of system states and False Data Injection Attacks (FDIAs), enabling compensation for input delays and robust mitigation of FDIAs. Theoretical conditions are derived to ensure the stability of the closed-loop system with H∞ performance. Extensive simulations validate the effectiveness of the proposed strategy in maintaining system stability under the combined challenges of input delays and FDIAs.

Intelligent Automation Systems, such as automated storage and retrieval systems, self-driving vehicles, various types of autonomous robots, and autonomous workshop plants, act independently of direct human supervision. Their impact on society and human life will be significant. These automation systems are safety-critical, complex, and powerful with a higher-level functionality. Safety and reliability to perform their tasks safely and minimize failures is one of the key challenges and becomes costly and difficult to achieve. The development of novel safety and reliability technologies dealing with theoretical aspects and for intelligent automation systems has become a hot spot in recent years. With the novel safety and reliability technologies, the intelligent automation systems can detect system failures, identify operation risks, predict unknown safety hazards and vulnerabilities, and avoid situations that pose risks to humans, property, or the automation systems themselves. Recent developments confirm that there are still areas of research to be explored within the safety-critical approaches. This Special Issue on Emerging Trends in Safety-critical Issues for Intelligent Automation Systems of IEEE Transactions on Automation Science and Engineering (TASE) aims to present recent advances in theories, methods, and applications that address safety-critical challenges in autonomous intelligent systems. The objective is to compile state-of-the-art research that contributes to the development of resilient and trustworthy automation solutions in safety-sensitive scenarios. After a thorough and rigorous peer-review process, 27 high-quality articles were selected from numerous submissions worldwide. These articles closely align with the Special Issue’s scope and can be categorized into seven key topics. - Risk assessment and model-based safety and cybersecurity analysis [A1], [A2], [A3], [A4], [A5]. - Intelligent fault detection and fault-tolerant control [A6], [A7], [A8], [A9], [A10]. - Reliability and traceability of decision-making for intelligent automation systems [A11], [A12], [A13]. - Conflict detection and resolution in intelligent automation systems [A14], [A15]. - Safety- and security-related issues including functional safety and system security [A16], [A17], [A18]. - Design, development, validation, and applications of intelligent automation systems such as UAVs, UGVs, and UUVs [A19], [A20], [A21], [A22], [A23]. - Human-robot collaboration, risk assessment of intelligent automation [A24], [A25], [A26], [A27]. We believe this collection will provide a valuable reference for academia and industry alike, and inspire further research in this rapidly evolving field.

Although the current sharing in islanded DC microgrids has been extensively studied, the low convergence rate and network attacks have hindered the consumption of renewable energy. Thus, this paper proposes a new fixed-time quasi-consensus control approach which can achieve accurate current sharing with a fast convergence rate under unknown denial of service (DoS) attacks. Firstly, the model of islanded DC microgrids with disturbances is built, where disturbances are caused by the renewable energy and three common loads (constant voltage, constant current and constant power loads). It is the basis for the subsequent design of the control scheme. Secondly, a dynamic compensator is developed in this paper to address the impact of unknown DoS attacks on islanded DC microgrids. Furthermore, a new fixed-time quasi-consensus approach with a new event-triggered mechanism is developed to realize accurate current sharing control with a fast convergence rate. Moreover, it can also realize low-bandwidth communication link and avoid continuous information exchange. Eventually, simulation and experimental results are given to verify the effectiveness of the proposed control approach for islanded DC microgrids under unknown DoS attacks.

Magnetic flux leakage (MFL) testing is an effective non-destructive testing (NDT) method for pipeline safety operation, and defect detection is one of the core issues in MFL signal processing. Currently, MFL defect detection is tough due to insufficient defect samples. To obtain more serviceable information, heterogeneous signals are collected in MFL process, while taking full advantage of heterogeneous signals is still a hard problem. In this paper, an end-to-end MFL defect detection architecture with insufficient training samples called heterogeneous signal fusion method (IHSF) is proposed. Firstly, heterogeneous signals from the same pipeline are collected by axial and radial sensors. Secondly, the features from fine-tuned and non-fine-tuned pre-trained models are fused, which increases the generality and adaptability of the features. Moreover, fine-tuning of a few parameters reduces the number of parameters during model training, which is more suitable for the insufficient sample training. Thirdly, a mutual supervision training strategy based on the features fusion of general and adaptive features is proposed to update the parameters of the proposed network, which establishes the latent relationship of heterogeneous signals. Finally, experiments on MFL defect detection are conducted, and IHSF is compared to the state-of-the-art methods. The results validate that the proposed method is effective.Note to Practitioners—The motivation of this paper is a hot signal processing issue on pipeline safety operation called defect detection with insufficient samples. Traditionally, the defect is usually detected by training a large number of defect samples, while defect samples are usually insufficient in practical MFL measurement. Moreover, conventional deep networks only use a single signal, which ignores information from multiple signals. Regarding the problems above, a heterogeneous signal mutual supervision method is proposed to replace the traditional fixed label mechanism. The mutual supervision method fully extracts the information in multiple signals by establishing hidden connections between defective heterogeneous signals. Furthermore, the fusion of the general features and adaptive features, which are extracted by the pre-trained model with non-fine-tuning and fine-tuning respectively, not only reduces the parameters in the network to be suitable for the insufficient sample problem, but also enriches the extracted features. The case on MFL signals demonstrates that the proposed method is more effective than the comparison methods.

This paper addresses the issue of distributed predefined-time output optimization for heterogeneous multi-agent systems with equality constraints. A novel distributed algorithm is proposed to enable state tracking of compensator variables under regulator equations assumption. By utilizing the properties of compensator variables and strongly convex functions, a predefined-time output optimization algorithm is proposed to achieve optimal output while satisfying given constraints. A key advantage of this algorithm is its flexibility, allowing convergence time is irrespective of control parameter constraints. Furthermore, a simplified segmented predefined-time optimization algorithm is presented to achieve output optimality within the specified time, reducing the computational and transmission information among agents. Finally, the practicality of the theoretical results is validated through a numerical example.

Defect recognition optimizes industrial manufacturing processes by monitoring the health of equipment and structures. The success of current deep learning (DL)-based defect recognition methods relies on the assumption that the samples are label-noise free, yet the low-quality of industrial data inevitably introduces label noise, resulting in a substantial drop in recognition performance. To tackle this problem, this paper develops a parallel hybrid penalty network (PHL-Net). It solves two essential issues in defect recognition under label noise: low sample utilization and difficulty in recognizing the ambiguous sample in the decision boundary region, so that the industrial defect recognition performance can be greatly enhanced under label noise. PHL-Net consists of three stages: data selection (DS) for explicitly selecting clean and noisy data; parallel feature mining (PFM) for improving the sample utilization by label-driven mining and distance-driven mining; hybrid penalty learning (HPL) to improve decision reliability to label noise, thereby the ambiguous samples in the decision boundary region can be accurately recognized. In PHL-Net, PFM and HPL are co-optimized to facilitate data selection. In turn, reliable data selection prevents the model from overfitting label noise. Three groups of experimental results on real-world industrial datasets MPV-W and NEU-CLS have demonstrated the effectiveness of our PHL-Net over state-of-the-art alternatives. Note to Practitioners—Defect recognition plays a vital role in industrial production, ensuring product quality and efficiency. When training deep learning-based defect recognition models, having datasets with precise annotations is essential. Unfortunately, label errors, known as label noise, are common when annotating industrial datasets due to the need for domain expertise, subjective judgments, and confusing samples. This makes it challenging to create a large-scale and accurate industrial dataset. To address this issue, our goal is to develop a robust model that can achieve optimal recognition performance even on datasets with noisy labels. On this basis, this paper proposes a parallel hybrid penalty network (PHL-Net). It improves recognition performance under label noise by improving sample utilization and recognizing ambiguous samples near the decision boundary. In experiments, we utilize the noise transition matrix to introduce label noise for MPV-W and NEU-CLS. The results of the experiments demonstrate that our PHL-Net outperforms the comparison methods on industrial datasets, highlighting the practical significance of our approach. This provides inspiration and reference for industrial defect recognition under label noise.

This paper proposes an all agents connectivity-preserving method and a data-filter memory-based event-triggered (ET) strategy to design cooperative learning control algorithm. Firstly, a type of error functions are proposed to achieve that all agents are within the communication boundary, which do not limit the initial values of agents. The designed method can dynamically adjust the boundary function based on the initial position of agents and gradually converge to the preset communication boundary, without abandoning any agents. Secondly, a data-filter memory-based ET strategy is proposed, which includes the designed error-based data filtering rules. The filtering rules avoid the problem that the ET mechanism stores abnormal historical data when the system has faults. Moreover, the presented error-based cooperative learning adaptive protocol does not need to presuppose that neighbor weights are bounded, reducing the conservatism. Finally, based on the above works, the constructed ET control method can achieve control objectives, and the effectiveness is demonstrated through theoretical analysis and simulation results. Note to Practitioners—In practice, multiagent systems (MASs) communicate through wireless communication mostly, which inevitably leads to an upper limit on the communication distance between agents. Exceeding the limitation of communication module will cause the problem that MASs cannot achieve signal transmission. Therefore, it is crucial to design appropriate constraint methods for different initial positions of agents to ensure that all agents can enter the predetermined communication boundary. In addition, the memory ET strategy can calculate the threshold of conditions based on the historical data. But in practice, it cannot guarantee the continuous normal operation of the system. If there are abnormal values in the stored signal data, this will lead to unreasonable calculation of the threshold. In response to this issue, this paper considers additional data filtering rules to avoid storing data when systems exist faults. Meanwhile, the cooperative learning algorithm proposed in this paper can adjust the learning information weights based on the control performance of neighbor agents, and remove the assumption of bounded neighbor learning laws.

This paper investigate the input constraint and signal encryption for distributed control. Firstly, for the algorithm operation session, an additional information protection mechanism is designed, and the designed encryption algorithm can ensure that both the encrypted signal and the key signal have chaotic randomness. The mask signal is composed of multiple chaotic states, which has higher randomness. The key signal is also encrypted, effectively improving the security of the encryption algorithm. Even when the channel of key is not secure, information security can still be guaranteed. Moreover, information decryption only requires an integral algorithm and does not require an embedded decryption module that satisfies chaotic synchronization conditions, effectively reducing the complexity of decryption. Secondly, the proposed output constraint function can dynamically adjust the constraint boundaries based on control performance, which avoids the problem that the boundary is too strict to affect the control performance. At the same time, the boundary initial value is related to the output initial value, which effectively improves the convenience of algorithm migration and avoids the need to consider designing different parameters in different application environments. Based on the above two main works, the designed control algorithm can meet the considered control objectives using Lyapunov theory, and the simulation results also verify the effectiveness of proposed methods.

This research investigates the problem of exponential tracking consensus for nonlinear switched multi-agent systems (NSMASs) under limited communication resources. To alleviate the communication load, edge-event-triggered strategies are applied to switched multi-agent systems for the first time. Due to the nonlinear switched dynamics in NSMASs, addressing the edge-event-triggered tracking consensus problem is more challenging compared to systems with linear or nonlinear fixed dynamics explored in previous studies. To tackle this challenge, the novel dynamic event-triggered rule for the leader and novel dynamic edge-event-triggered rules for edges are developed, both relying on model-dependent designs. Different triggering conditions are established for diverse system models to achieve enhanced triggering performance. Furthermore, the introduction of model-dependent dynamic auxiliary and edge-auxiliary variables contributes to a significant reduction in signal transmission frequency. Then, novel model-dependent edge-node-combined distributed dynamic event-triggered controllers are formed to ensure the global exponential consensus of NSMASs, with Zeno phenomenon explicitly excluded. Finally, the effectiveness, practicability and superiority of proposed methods are validated through two numerical examples and a practical physical simulation.

This paper researches the predefined-time fuzzy adaptive dynamics surface consensus control problem for nonlinear multi-agent systems with input saturation. With regard to nonlinear functions existing in the control systems, fuzzy logic system is employed to estimated them. A novel improved predefined-time dynamics surface filter is designed that can avoid the explosion of complexity problem, and the proposed filter satisfies predefined-time stable simultaneously. With the support of piecewise function, the singularity problem that may exist in virtual controller can be dodged greatly. Furthermore, a ameliorative predefined-time auxiliary dynamic system is presented to cope with input saturation. Combining adaptive backstepping control and predefined-time theory, a predefined-time adaptive fuzzy dynamics surface controller is presented that can assure the systems are predefined-time bounded, though the followers exists in the control saturation.Note to Practitioners—Numerous actual physical systems and devices can be modeled as uncertain nonlinear MAS. Furthermore, MAS also can be widely applied to disaster relief, spacecraft and multitudinous fields. On the one hand, the practical systems may exist in input saturation phenomenon due to human factors and in most of the relevant literatures, which can affect the system performance or bring about instability. On the other hand, the initial values of actual systems usually cannot be chosen freely because of the influence of environment and other factors. In addition, it is currently ordinarily supposed that realize the stabilization of controlled systems when time approaches infinity. Consequently, a predefined-time adaptive fuzzy controller is presented for MAS with control saturation, which can achieve the predefined-time stabilization of MAS.

This research considers the problem of event-based adaptive fault-tolerant control for nonlinear cyber-physical systems with deception attacks and actuator faults via intermittent available signals. Through the application of fuzzy logic systems, the unknown nonlinear functions of the systems are approximated. Then, a novel state observer is designed that can be driven by actuator faults and intermittent available signals arising from triggered attack-state signals, which can realize directly triggering the states after deception attacks and avoid the problem of virtual controller non-differentiability under the backstepping framework. In order to avoid the complexity explosion problem, the dynamics surface control method is introduced. Meanwhile, the dynamics surface filter signals also realize event-triggered control, and it results in substantial savings in communication resources. To verify the validity of the proposed method, two illustrative examples are presented. Note to Practitioners—Cyber-physical systems are frequently applied in modern industrial processes such as smart grids, industrial Internet of Things and so on. Nevertheless, the open network environment makes system components more vulnerable to attacks, posing a significant security threat to the operation of cyber-physical systems. When attackers deliver deceptive information to the sensors, the system state becomes inaccessible, which is a challenging issue based on the signals available after the attacks. Moreover, when actuators are affected by faults, system performance deteriorates, potentially leading to instability. Consequently, the event-triggered control problem of cyber-physical systems, considering both actuator faults and deceptive attacks, presents challenges for controller design. In order to resolve the issues outlined above, a fault-tolerant state observer based on intermittent available signals is developed. This design employs backstepping recursion and adaptive techniques to achieve stability for system under deception attacks and greatly alleviate communication burden, thereby enhancing the practicality of the proposed control strategy.

This article presents a robust fault reconstruction scheme for a class of discrete-time nonlinear singular systems with disturbances. The Lipschitz condition is utilized to describe the nonlinearity under investigation. To effectively estimate multiple types of faults such as time-varying faults and constant ones, a brand-new proportional-difference (PD) observer design method is established. What is worth mentioning is that all the gains of PD observer can be calculated at the same time by our method instead of manually selecting some gains and then solving other gains as has been done in the pertinent literature, which serves as a pioneering attempt for the similar design. Besides, by using the Lyapunov theory, less conservative linear matrix inequalities (LMIs) criteria with slack scalars and matrices are obtained to ensure that error systems are robust stability with the prescribed H∞performance. Finally, two examples on RC circuits and tanks level control models are presented to prove the superiority and practicability of the proposed fault estimation procedure.

In industrial processes, the defect data is continuously accumulated over time, and new classes of defects can arise at any time. When a well-trained model is adapted to new classes, its performance in old classes will sharply decline. To solve the above issue, a novel incremental defect detection method named elastic heterogeneous distillation network (EHD-Net) is proposed. First, an elastic knowledge transfer method is proposed to selectively transfer the core features of the old classes, so that the old knowledge is retained and new knowledge is effectively learned at the feature level. Second, warping heterogeneous distillation learning is proposed for the first time. In warping heterogeneous distillation learning, a gradient warping layer is proposed to balance the optimization direction of gradients for both old and new classes, and the proposed heterogeneous distillation learning strategy can clarify the association and difference between new and old classes, so that the rapid learning of new knowledge and comprehensive transfer of old knowledge are guaranteed at the decision level. Finally, a well-trained evolutionary model is employed to achieve the detection of both old and new classes. The proposed method can effectively overcome the catastrophic forgetting of old classes and guarantee the independent learning of new classes. Experimental results on two industrial datasets show that EHD-Net outperforms existing advanced methods. Note to Practitioners—The motivation for this paper is an important and highly practical issue called class-incremental defect detection. Specifically, new classes of defects always appear in industrial scenarios over time and when a well-trained detection model adapts to the new classes, its performance in the old classes decreases dramatically. To solve the above issues, an elastic heterogeneous distillation network (EHD-Net) via elastic knowledge transfer and warping heterogeneous distillation learning is proposed. The proposed method not only effectively suppresses the catastrophic forgetting of old classes while realizing the fast learning of new classes, so that the model can simultaneously achieve the high-accuracy detection of both the new and old classes without the participation of old class samples. The validity of the proposed method is verified under two industrial datasets, which fully guarantees its practical application value.

The power fluctuation and system inertia degradation in a short time put forward higher requirements on the rapidity and robustness of frequency control. This paper proposes an online multiplayer Stackelberg game control framework to achieve frequency control of multi-area power system with the uncertainties of load, renewable energy and line loss. Firstly, an optimal frequency control model incorporating efficient management of load aggregators (LA) is designed based on Stackelberg game, where the LA acts as the leader and all micro-turbines act as the followers. The two-level optimization problem of leader and followers is converted into solving the coupled Hamilton-Jacobi equations with the constraints of the follower’s costate equation. Then, an improved integral reinforcement learning is designed to enhance the wide adaptability of the control strategy online by introducing a general function with uncertainty upper bounds and line losses for all players, while satisfying the uniform ultimate bounded stability of the closed-loop system. A single critic neural network structure is utilized to obtain the optimal strategy. And the convergence of neural network weights is proven. Last, comparative simulation results verify the effectiveness of the proposed method.Note to Practitioners—High accuracy and robustness in frequency control of power systems is important for system safety. Demand-side participation in the interactive regulation of the system makes frequency control strategies more flexible and diversified. However, unreasonable control allocation schemes can incur large payment costs, while changes in control can cause dynamic changes in line losses to affect the supply-demand balance, leading to frequency deviations. This paper proposes an intelligent frequency control method based on the Stackelberg game. The designed IRL algorithm is able to solve the trade-off between system control performance and payment costs online without the priori knowledge of the system and with wide adaptability to uncertainty. Simulation results show that the proposed method outperforms non-game strategies and is robust.

This paper is concerned with the sliding mode predictive control problem for the load frequency control (LFC) of interconnected power systems with input delays and load disturbance. To deal with input delays, unknown disturbance, the interconnected influence existing simultaneously in power systems, the state-observer and disturbance-observer are respectively designed by employing the reduction transformation technique for state observer and introducing the sliding mode surface for the extended disturbance observer. Then the jointed observers based integral sliding mode controller with active disturbance compensation is designed. Furthermore, by constructing a novel Lyapunov function and matrix inequality technique, the sufficient conditions of exponential stability are provided for the closed-loop observer error systems in linear matrix inequality form, which can be solved by linear matrix inequality (LMI) toolbox in Matlab. Simulation is given to verify the effectiveness of the proposed approach.Note to Practitioners—This paper was motivated by the practical control problem of load frequency control systems with input delay. The input delay, unknown load disturbance and frequency fluctuation of tie-lines make it difficult to design a suitable control for load frequency control systems. The main practical contributions are summed as follows: A new jointed observer-based sliding mode predictive control approach is proposed to the interconnected power systems with input delays. By employing the reduction transformation technique, a jointed observer based integral sliding predictive mode controller with active disturbance compensation is developed to deal with input delay, unknown disturbance, the interconnected influence existing simultaneously in practical power systems.

This article is concerned with the memory-based event-triggered leader-following dissipative fault-tolerant consensus (LFDFTC) problem for the nonlinear multi-agent systems (NMASs) with semi-Markov switching topologies subject to the generally uncertain semi-Markov (GUSM) jumping process. Unlike the existing event-triggered (ET) consensus results, the dynamic memory event-triggered mechanism (DMETM) and memory-based distributed fault-tolerant (FT) controllers are designed to reduce the ET times. By constructing a general mode-dependent Lyapunov-Krasovskii functional (LKF) and strictly$(\bf {\mathcal {R,Q,T}})-\boldsymbol {\gamma }$dissipative analysis, the dissipative FT consensus conditions of NMASs are derived in this paper. Finally, three actual physical systems are utilized to verify the validity of the proposed method. Note to Practitioners—Owing to the complexity of engineering environment, the consensus control issue of NMASs has attracted widespread attention. Nowadays, the consensus control of NMASs is generally utilized in diverse fields, such as multi-vehicle coordination, smart grids, and unmanned aerial vehicle formation. However, for the electronic device in practical applications, the channel bandwidth is limited due to power and energy constraints, and it is difficult for the fixed communication topologies and traditional periodic sampled-data control method to cope with these unexpected situations. Therefore, the LFDFTC issue for the NMASs with GUSM switching topologies is investigated by adopting DMETM and memory-based distributed FT controllers in this paper. In addition, the proposed FT consensus control methods with prescribed dissipative performance are applied to multiple vehicles time-invariant formation, Chua’s circuits synchronization, and multiple manipulators consensus.

This article investigates the topic of adaptive tracking consensus for a family of nonlinear multi-agent systems modeled by random differential equations, which are different from well-known stochastic differential equations. This paper provides some primitive results on random nonlinear multi-agent systems. An improved backstepping method named command filtered control is adopted to derive the adaptive control law, where the convergence of the filtering error is guaranteed. To tackle the serious nonlinearities and uncertainties, a series of dynamic gains are introduced in the design process. The tracking errors for each follower concerning the output of the leader can be regulated arbitrarily to a small enough value by choosing appropriate tuning parameters. All signals of the closed-loop system are analyzed to have bounds almost surely. Moreover, the feasibility of the theory developed in this paper is validated by an example of numerical simulation. Note to Practitioners—Inspired by all kinds of biological clustering or grouping behaviors in our natural world, the research on multi-agent systems has long been popular in both theoretical and engineering scenarios. In addition, colored noises are ubiquitous and negligible when dealing with control problems of different engineering plants. Based on the above observation, this paper was motivated to realize the tracking consensus control of a class of nonlinear multi-agent systems disturbed by colored noise, which is also called random nonlinear systems, with the help of an enhanced adaptive backstepping approach called command filtered control. Although there exist nonlinearities and random noises in each follower agent, the objective of output consensus still could be achieved with the combination of the methods of dynamic gains and command filtering. The application potential of this research is considerable, such as drone formation performance and drone cruise. Our future research will further investigate control problems of random nonlinear multi-agent systems under some practical obstacles such as actuator failures.

Random differential equations (RDEs) involving colored noise hold more practical significance than stochastic differential equations (SDEs) driven by white noise. The research on random nonlinear systems is becoming more and more popular. This paper considers the problem of finite-time control for high-order random nonlinear systems with general growth conditions and unknown control coefficients. Based on the backstepping and domination idea, the technique of adding a power integrator is adopted to recursively design an adaptive controller that renders the closed-loop system finite-time stable in probability. This paper serves as a pioneering attempt at finite-time control design of random nonlinear systems. The theoretical results are corroborated by a numerical example.Note to Practitioners—This article was motivated to realize the finite-time stability of high-order random nonlinear systems, even though there are unknown control coefficients in the system. The practical significance of this paper is mainly twofold. Firstly, since random noise is ubiquitous in all kinds of engineering environments, the research on random nonlinear systems driven by colored noise is of great importance. Secondly, the asymptotic stability is infeasible in the practical implementation, so the achievement of finite-time control makes the method proposed in this paper applicable.

The event-triggered tracking consensus of multi-agent systems (MASs) with model switching, topological switching and non-zero leader inputs is concerned in this paper. In previous event-triggered studies on MASs, only a single switching behavior is considered and triggering parameters remain unchanged during switching, which shows more conservatism. In sharp contrast to existing research, this paper deals with two switching behaviors simultaneously, i.e. model switching and topological switching. Accordingly, dual-switch-based dynamic event-triggered rules are proposed to ensure better triggering performance. Further, dual-switch-based event-triggered control protocols are devised for agents. By constructing dual-switch-based multiple Lyapunov functions and utilizing the average dwell time (ADT) method, sufficient conditions are given to ensure the tracking exponential consensus. Finally, two examples of practical mass-spring systems with variable masses are carried out to certify the effectiveness and superiority of our approaches. Note to Practitioners—In real-world MASs, model switching and topological switching often coexist. Considering the limited computational and communication resources, it is of practical significance to conduct event-triggered research on MASs with model switching and topological switching. However, in existing event-triggered research on MASs, triggering control protocols are designed only for one type of switching behavior. To fill the research gap, this paper delves into the event-triggered control of MASs under both model switching and topological switching, offering greater practicality for engineering applications. Meanwhile, the devised triggering protocol is less conservative, ensuring better triggering performance and saving more computing and communication resources. In the simulation, the proposed approach is applied to practical mass-spring control problems under variable masses and switching topologies, and its practical application value is illustrated.

This paper delves into the output feedback sliding mode control (SMC) problems in a class of Interval Type-2 (IT2) fuzzy systems affected by sensor faults. Firstly, a Markovian chain is employed to depict transmission channels vulnerable to stochastic deception attacks, providing a more accurate representation of the real-world scenario. Secondly, by leveraging historical estimates of fault signals, a novel learning-based observer integrated with a Saturation Function is proposed to reconstruct sensor faults. This observer incorporates a dynamic saturation level aimed at enhancing filtering performance. Subsequently, based on the estimated fault information, an output feedback SMC law is designed, and its finite-time boundedness is analyzed. Finally, the proposed approach and its associated theoretical findings are extended to an inverted pendulum model.

Magnetic flux leakage (MFL) detector can analyze the state of pipelines and is widely used in pipeline inspection. Object detection of class extremely imbalanced MFL signals is a challenging and demanding task. It is difficult for existing deep learning-based detection methods to effectively detect a small number of component classes. To address the above issues, a novel masked multi-view bidirectional network (MMB-Net) is proposed in this paper. First, based on the problem of insufficient component samples, a mask-based data augmentation module is designed, where we develop a novel wavelet convolution blocking module and Gauss-like distribution mask strategy so as to expand component sample sets. Second, based on the problem that poor feature extraction of partial MFL signals leads to low accuracy of component detection, a learnable multi-view attention (LMA) module is designed to expand the perceptual field of the network and fully mine the features of MFL signals. Finally, a bidirectional attention module based on multiple classification layers (BAMC) is proposed to learn different decision boundaries, which maintains the accuracy of the defects while improving the accuracy of the components. Experimental results illustrate that the proposed network can effectively build a detection model under MFL signals and outperforms the state-of-the-art methods on mean precision (9.5%).Note to Practitioners—This article is motivated by the problem of extreme class imbalance in the MFL signals collected from pipelines, which seriously reduces the detection accuracy of defects and components in the MFL signals. In terms of the MFL signal intrinsic characteristics, a masked multi-view bidirectional network (MMB-Net) based on data dynamic adjustment of model parameters and bidirectional learning decision boundaries are constructed. Our method can extend the component samples and learn different decision boundaries to improve the detection accuracy of the component classes. The experimental results show that our proposed method is more advantageous than the state-of-the-art methods, and therefore, our proposed method has strong practical value.

This paper proposes some novel algorithms for photovoltaic (PV) energy DC circuit conversion systems to extract maximum power under the constraint of information security. First, a discrete-degree-judging-based composite chaotic mask function generation (DCCG) algorithm is constructed. This algorithm utilizes all the states in the chaotic system to construct a more volatile chaotic signal, effectively increasing the complexity of the mask signal and enhancing the encryption of the photovoltaic energy system. Secondly, for the superposition process of the encrypted signal and the mask signal, a Gaussian-high-dimensional mapping-based adaptive weight calculation (GMAWC) method is designed to adaptively adjust the superposition weights. This adaptive adjustment helps avoid issues of insufficient encryption and excessive noise interference caused by the large value domain variation between the mask signal and the system states. Moreover, an upper bound on the decryption bias tolerated by the PV energy conversion system is explored, ensuring that the algorithmic framework has greater decryption bias tolerance while achieving maximum power extraction. Finally, both theoretical analysis and data results show that the proposed encryption-decryption-control framework has better security, applicability and decryption bias tolerance.

In this paper, the problem of adaptive fault-tolerant optimal control is investigated for nonstrict-feedback nonlinear systems with deferred output and performance constraints (DOPCs). By skillfully constructing the shifting transformation and finite-time constraining function, a novel error-dependent barrier function is innovated to achieve the preassigned time tracking performance without the conservative initial limitations, helping the property of DOPCs be preserved. Neural networks (NNs)-based reinforcement learning (RL) is exploited, which takes advantage of approximation means to solve the Hamiltonian equation under the backstepping framework, optimally reconciling the tracking performance and control behavior. By utilizing NNs to online estimate the unaware faults and uncertainty, an adaptive fault-tolerant optimal controller is designed. Finally, the efficiency of the proposed method is confirmed by simulations.

Developing model-free $H_{\infty }$ optimal control schemes in systems with unknown model parameters and unmeasurable states is challenging. In this article, an output-feedback (OPFB) suboptimal control scheme based on adaptive dynamic programming (ADP) is proposed to realize model-free $H_{\infty }$ control under uncertain disturbances. First, a free matrix is introduced to compute the suboptimal gain in the absence of an optimal OPFB gain, and a policy iterative algorithm is developed to solve for the suboptimal gain and shown to converge to a solution of the algebraic Riccati equation. In addition, a model-free ADP algorithm is proposed to realize online learning of control parameters without relying on system dynamics parameters. The Lanczos method is introduced to solve the ill-condition problem in the model-free algorithm solution. After that, the algorithm is further extended to the case where the system state is not measurable and parameterized reconstruction is performed using online input-output data. The results show that the proposed algorithm can realize model-free control with unknown parameters and unmeasurable states. The effectiveness of the proposed control scheme is simulated by an F-16 aircraft.

In this article, a novel online adaptive control scheme is developed for the optimal control issues of integrated electric-gas systems with partially unknown dynamics, by combining the decentralized event-triggered mechanism and adaptive dynamic programming techniques. Initially, the complex electric-gas coupling network is modeled in the state-space form. By virtue of neural networks (NNs), the NN-based identifier and the critic NN are designed to approximate the unknown drift dynamic and the optimal value function in an online fashion, respectively. Subsequently, the decentralized event-triggered control strategies are devised under the identifier-critic framework. Moreover, a novel decentralized event-triggered scheme with the dead-zone operation is proposed, which updates the controller and actuator signals only when the triggering condition is violated. As such, the computation complexity and the waste of communication resources can be significantly reduced. On the foundation of the Lyapunov theory, the uniform ultimate boundedness stability of the closed-loop control system and the exclusion of the Zeno behavior are proven. Finally, the effectiveness of the developed algorithm is verified through two numerical examples.

Domain-adaptive object detection (DAOD) aims to generalize detectors trained in labeled source domains to unlabeled target domains by mitigating domain bias. Recent studies have confirmed that pretrained vision-language models (VLMs) are promising tools to enhance the generalizability of detectors. However, there exist paradigm discrepancies between single-domain detection in most existing works and DAOD tasks, which may hinder the fine-grained alignment of cross-domain visual-text features. In addition, some preliminary solutions to these discrepancies may potentially neglect relational reasoning in prompts and cross-modal information interactions, which are crucial for fine-grained alignment. To this end, this article explores fine-grained visual-text feature alignment in DAOD with prompt tuning and organizes a novel framework termed FGPro that contains three elaborated levels. First, at the prompt level, a learnable domain-adaptive prompt is organized and a prompt relation encoder is constructed to infer intertoken semantic relations in the prompt. At the model level, a bidirectional cross-modal attention is structured to fully interact visual and textual fine-grained information. In addition, we customize a prompt-guided cross-domain regularization strategy to inject domain-invariant and domain-specific information into prompts in a disentangled manner. The three designs effectively align the fine-grained visual-text features of the source-target domain to facilitate the capture of domain-aware information. Experiments on four cross-domain scenarios show that FGPro exhibits notable performance improvements over existing work (Cross-weather: +1.0% AP50; Simulation-to-real: +1.2% AP50; Cross-camera: +1.3% AP50; Industry: +2.8% AP50), validating the effectiveness of its fine-grained alignment.

This article proposes the row-stochastic event-based quantized (RSEQ) algorithm to address the distributed optimization problem with multiple communication constraints, including limited communication costs and bandwidth. In RSEQ, a novel event-based dynamic quantizer is designed to resist the negative effects of communication constraints on the algorithm. The quantizer encompasses the event generator and the dynamic encoder/decoder, which collectively adapt the frequency and size of information sharing based on real-time state. The RSEQ only requires the construction of a row-stochastic weight matrix, which leads to lower conservatism compared to algorithms based on column-stochastic matrices. Additionally, the introduction of an acceleration term enables RSEQ to linearly converge to the globally optimal solution without the deployment of the average gradient estimator. Instead, a Perron vector estimator needs to be employed to counteract the unbalancedness of the directed network. With the effect of the event generator, the Perron vector estimator can also be left inactive after a certain number of iterations, which means that the transmission of only state information between agents can linearly converge to the global optimal solution under directed networks. Finally, the effectiveness of the algorithm is demonstrated through an economic dispatch problem in smart grids.

In this article, the tracking control problem for nonlinear systems is investigated. For the first time, a fixed-time dynamic signal is constructed to handle unmodeled dynamics. A novel error-based function is first designed and applied to prescribed performance control, significantly enhancing the transient performance of the system. To mitigate the issue of extensive differential calculations, a modified fixed-time dynamic surface technique is incorporated into the backstepping design process. Combining the backstepping technique with fixed-time stability theory, a fast practical fixed-time controller is proposed to effectively address the control problem. Finally, the proposed scheme is validated for its effectiveness through the practical simulation example.

This article investigates the discrete-time fuzzy complex networks (FCNs) event-triggered (ET) synchronization control problem by considering the coupling effect and cyber attacks. To prevent data conflicts caused by communication resource limitations, a round-robin (RR) protocol is introduced to regulate the communication transmission order among coupled nodes. To be more general and flexible against Denial-of-Service (DoS) attacks, the concept of average dwelling time ratio (ADTR) is proposed to evaluate DoS attack models. In addition, a DoS-attack-aware ET mechanism is developed. Under this mechanism, a threshold parameter is dynamically adjusted according to the active and dormant time of the DoS attacks. The correlated co-design of anti-attack security control and ET mechanism is realized. Compared with the existing results, the scheme improves the control performance while reducing the controller update frequency. Then, by establishing appropriate Lyapunov function, sufficient criteria for synchronization of discrete-time FCNs are obtained, and a controllable range of the ADTR is calculated. Finally, the effectiveness of the proposed DoS-attack-aware ET control scheme based on RR protocol is verified through two simulations.

This article addresses the cooperative output regulation problem for discrete-time multiagent systems with unknown parameters, a challenge that arises in many practical applications where system models are unavailable. Unlike existing techniques, a model-free Q-learning algorithm is devised to iteratively obtain the optimal policy. This algorithm operates independently of system parameters, and its immediate cost formulation excludes the necessity of solving regulator equations. Consequently, it achieves a streamlined structure, facilitating direct determination of the optimal policy. Subsequently, the stability of each iteration of the algorithm is formally established, along with the derivation of a unique condition for the Q-function matrix. Additionally, to address the challenge of obtaining a stable policy when the initial policy is unstable, an innovative data-driven algorithm is introduced that effectively computes the initial stable gains, ensuring convergence to stability throughout the learning process. Meanwhile, we focus on demonstrating that the distributed observer and the excitation noise do not introduce bias. Finally, the efficacy of the proposed algorithm is validated through two simulation examples.

In this study, the synchronization problem of discrete-time fuzzy complex networks (FCNs) is investigated based on a state-dependent intermittent memory-event-triggered (MET) scheme. To alleviate the burden of network communication, a state-dependent-region-partitioning-based intermittent mechanism is proposed to set the working/rest interval of the controller. Compared with the existing results, it realizes on-demand control, reduces resource consumption, and expands applicability. A MET mechanism that depends on historical triggering states is developed during the working interval, which adds “smarter” to the triggering decision. Besides, a working-state-dependent threshold function is designed in the triggering condition, which realizes the correlation between intermittent and event-triggered mechanisms. Compared with the existing results, the mechanism provides intelligent regulation of the triggering instant, thereby optimizing the controller update instant and control effect, achieving the goal of improving the control effect while reducing the triggering frequency. Sufficient criteria for achieving synchronization of discrete-time FCNs are derived using Lyapunov stability theory under the state-dependent intermittent MET scheme. Simulation results are presented to demonstrate the effectiveness of the proposed scheme.

This research considers the predefined-time adaptive fuzzy formation control issue for high-order nonlinear multi-agent systems. By applying fuzzy logic systems, the systems unknown nonlinear functions can be approximated. To refrain from “explosion of complexity problem”, a novel dynamics surface for high-order nonlinear multi-agent systems is presented. Further, to minimize the communication burden, an event-triggered mechanism suitable for high-order nonlinear multi-agent systems is applied in the control methods. With the help of the backstepping design scheme and the adding power integral method, an adaptive fuzzy predefined-time formation control approach is proposed so that all signals in the considered systems are bounded and realize the desired formation control within predefined time. The illustrative examples are presented to verify the validity of the suggested method.

In this article, a finite-time learning-based optimal tracking problem for nonlinear systems with preassigned performance constraint is investigated. By designing a state predictor, a fuzzy approximator driven by prediction errors rather than tracking errors is formulated to precisely compensate the effect of the unknown uncertainties. The design realizes a decoupling of control and estimation loops, effectively ensuring transient approximation performance and avoiding chattering induced by nonzero initial tracking errors. Then, based on the estimated components, a robust steady-state control scheme embedded with a prescribed performance mechanism is tailored to guarantee that the output state can converge to a predefined range within a preassigned time. This endows the designed controller with a specified time tracking capability independence on control parameters. To make a tradeoff between tracking precision and energy cost, a finite-time learning-based optimal control policy is exploited by utilizing adaptive dynamic programming technique to serve as an adaptive supplementary controller, where single critic neural network is trained for acquiring the solution of the Hamilton–Jacobi–Bellman equation. Compared with the traditional gradient descent method, the established learning law is updated by introducing an auxiliary variable, which enhances learning performance and guarantees finite-time convergence of adaptive weights. Simulation examples examine the effectiveness and superiority of the suggested scheme.

This paper presents a distributed saturation-tolerant fuzzy control scheme for stochastic multiagent systems (MASs) with unknown measurement sensitivity, where the states, consensus errors, and control inputs all are constrained. A concise nonlinear mapping is tactfully devised to impose appropriate constraints on full states without reliance on feasibility conditions. Besides, a novel resilient quantitative prescribed performance control (RQPPC) is developed, which incorporates finite-time performance boundaries with input-relevant dynamic boundaries generated by an auxiliary system, being expected to flexibly adapt to input saturation without violating performance constraints. Building upon the RQPPC, the transient and steady-state behaviors of consensus errors can be quantitatively predesigned free from repeated parameter tuning processes. Uncertain nonlinear terms in the converted system are successfully addressed by fuzzy approximation. The superiority and effectiveness of theproposed algorithms are confirmed by simulations with comparisons.

This article investigates adaptive dynamic programming (ADP)-based optimal control issue of nonlinear stochastic systems with asymmetric input constraints. The solution starts with developing generalized fuzzy hyperbolic model (GFHM) in the stochastic system, which aims to approximate unknown nonlinear terms. By establishing a nonquadratic cost function, the constrained$H_{\infty }$control problem is converted into zero-sum game and Hamilton–Jacobi–Isaacs equation (HJIE) is derived. To solve the HJIE, the ADP algorithm is developed by constructing a single-network adaptive critic framework. Assisted by GFHM, the updating process obviates the necessity for the dynamics of unknown nonlinear terms. Under the designed controller, the stability of the stochastic system is guaranteed by the Lyapunov method. Two illustrative examples validate the presented method.

In this paper, a novel synchronization learning scheme is proposed for secure communication, where the signal transmission architecture with a chaotic encryption process is considered. Firstly, to realize the information security in communication, the original signals are encrypted by fractional order dynamics from the sender, and decrypted by receiver to achieve synchronization. For the process, a hybrid order dynamic optimization is constructed, where the fractional order and the integer order systems are modeled as constraints. Secondly, a transformation formula is developed to convert these constraints into new integer order dynamics, and the equivalence between two dynamic optimizations is obtained. Thirdly, to obtain the synchronization solution, a new iterative learning algorithm is designed, and the adaptive dynamic programming is successfully embedded into the solving process. Finally, we apply the proposed synchronization scheme into the secure image transmission, and the simulation results demonstrate the effectiveness and practicality successfully.

As a burgeoning approach, droop control of interfacing inverters in microgrids has been widely adopted. However, droop control cannot achieve proportional reactive power sharing among distributed generators because of the difference of feeder impedances in actual microgrids. Hence, a novel distributed adaptive virtual impedance method based on average consensus algorithm is proposed to relieve feeder impedance mismatch. There into, virtual impedance is composed of static and adaptive inductance, which is designed to reshape equivalent impedance by creating additional control loops. Adaptive inductance unit is designed based on reactive power mismatch by applying multiagent consensus algorithm. By fully distributed adjustment to equivalent impedance, reactive power sharing accuracy can be improved. Then, proposed control method is applied to modified droop control, which is embedded designed dynamic voltage average consensus estimator, providing superior voltage restoration. At last, the effectiveness of proposed control method is shown by using simulation and further demonstrated through experiment study.

Pipeline system, as a vital industrial infrastructure, inevitably suffers from corrosion due to long-term service, which poses great threat to safe and stable energy transport. Many intelligent quantitative assessment technologies have emerged to evaluate the pipeline corrosion condition. However, they heavily rely on the quality and quantity of corrosion data. For industrial scenarios, the acquisition of pipeline corrosion data is costly with precision equipment for excavation works, and the annotation task is also labor-intensive, which severely limits their applicability. To address these issues, a mechanism-aided bilevel knowledge transfer framework is proposed to achieve effective quantitative assessment of pipeline corrosion condition. First, a high-fidelity mechanism model is designed to simulate the pipeline corrosion process and generates sufficient simulation data to alleviate the dependence on real-field resources. Then, a bilevel distribution-aware idea is proposed to transfer knowledge from simulated to real corrosion data, which is our original effort to focus on both feature complexity and label finesse, so as to accommodate the real-world corrosion for sensible domain adaptation. Next, we propose the idea of divide-and-conquer based on variational integration embedding (VIE) to maximize model performance improvement with minimal expert workload, where unreliable corrosion data are asked to expert annotation, while others are utilized via pseudolabels obtained from VIE. In the experiments, the performance of our method based on both hardware test platform and practical application case is systematically validated, and the competitive results indicate that our method has great potential in intelligent pipeline systems.

Leakage localization in energy interconnection systems remains a significant challenge since the efficacy of traditional time-based methods is compromised by the requirements for high sampling frequency. To overcome this issue, a novel localization framework that eliminates the reliance on timestamp data is proposed in this article. This framework utilizes neural networks to model the attenuation patterns of negative pressure waves (NPWs) along the pipeline, transforming the localization problem into a consistency issue between the NPWs at both ends. First, the Bayesian neural network is employed to model the attenuation patterns, not only reducing overfitting and quantifying uncertainty, but also enhancing the optimization process by incorporating probabilistic reasoning. Furthermore, an innovative maximum entropy adaptive dynamic programming algorithm is developed. This algorithm integrates the variance of theQ-function into policy improvement and incorporates policy entropy to enhance exploration and mitigate convergence to suboptimal solutions. Finally, experimental results on a long-distance pipeline demonstrate that the proposed approach achieves high localization accuracy.

This article studies the modeling method of false data injection attacks (FDIAs) considering topological changes and relevant countermeasures. A novel robust FDIA model is built, which incorporates network and measurement uncertainties into the attack subnet and can be applied to attack scenarios during topological changes. To tackle such FDIAs, a flexible dynamic time warping strategy-based distributed unsupervised detection mechanism is developed. Furthermore, an enhanced recognition model via hierarchical agglomerative clustering and local outlier factor techniques is proposed to facilitate operators to distinguish FDIAs. Compared with related works, the proposed model is more applicable to topological change scenarios and the detection framework can effectively discern such FDIAs in a distributed fashion. Simulation results demonstrate the stealthiness of the proposed FDIA model during and related to topological changes and the effectiveness of the distributed unsupervised detection method in tackling such attacks.

In this article, a novel adaptive critic learning (ACL) framework is constructed for a class of nonzero-sum (NZS) differential games problem of unknown continuous-time (CT) nonlinear systems with state constraints. First, generalized fuzzy hyperbolic model (GFHM)-based identifiers are established to reconstruct the unknown system dynamics. Then, under the ACL framework, a critic network with secure finite-time experience replay turning law is developed for each player to acquire the Nash equilibrium point solution in finite time while the finite-time stability is guaranteed via Lyapunov analysis. Meanwhile, the persistence of excitation (PE) condition is no longer needed in this work, by introducing an easy-to-check rank condition. Furthermore, by incorporating the immediate cost function associated with each player and the control barrier function (CBF), the algorithm ensures that the system states evolve in a secure environment. Finally, two numerical examples are presented to demonstrate the validity of the developed scheme.

Fault transfer diagnosis is a key technology to ensure the reliability and safety of industrial systems, the core of which is to identify the health status of the equipment among different working conditions with multiclassification methods. However, most of them are based on a closed-set assumption that the label space among different working conditions is consistent, which is hard to satisfy in a practical industrial environment as unknown faults would inevitably occur during operation, i.e., the open-set fault transfer diagnosis (OSFTD) problem. Moreover, during the transfer process, unnecessary source-specific knowledge tends to be adapted, which brings about biased diagnostics on both domain and category. Aiming at this issue, an OSFTD framework, coined as knowledge transfer and reinforcement based on biunbiased neural network (KTR-BUNN), is proposed. First, a domain-unbiased knowledge transfer subnet is proposed, including an uncertainty-aware fault transferability evaluator (FTE) that estimates the transferability of target-domain samples unbiasedly to guide distribution alignment of known faults and a triple-tier unknown fault separator (UFS) that takes transferability as the criterion to extrapolate unknown faults. Second, a class-unbiased knowledge reinforcement subnet is designed to promote the recognition of fault semantic features at the embedding space, where fault knowledge graphs (FKGs) are constructed to describe the relationships between fault types, and they are optimized by a contrastive fault correlation loss, so that fine-grained class-level fault features can be further aligned. The knowledge transfer and knowledge reinforcement mechanisms work jointly to facilitate the performance of OSFTD. Finally, extensive experimental results conducted on diverse diagnostic tasks illustrate the superiority of the proposed KTR-BUNN.

This article introduces a hierarchical learning (HL) framework for discrete-time (DT) systems with strict feedback structures to enable tracking control. Unlike the backstepping approach, our method creates dynamically adjustable virtual targets (VTs) for state variables at each layer, forming a cascading optimization structure. This innovative framework enables each layer to learn by approximating the solution to the DT Hamilton-Jacobi-Bellman (HJB) equation, thereby facilitating inter-layer self-optimization and directing the modification of adjacent VTs. To tackle the noncausal problem, we implement an iterative predictive learning framework that maps the current measurable state and known reference trajectories to VTs. This process allows the VTs to gradually align with the optimal trajectory during policy evaluation and update, achieving indirect tracking of state variables toward the desired targets. Additionally, the action network is transformed into a tracking network, incorporating future tracking errors to optimize its weights. This approach reduces tracking costs in the subsequent policy update while improving tracking performance. Rigorous convergence analysis and numerical simulations confirm the effectiveness of our method, highlighting its considerable potential in adaptive control.

This article is centered on prescribed-time (PST) bipartite output consensus issue for the heterogeneous multiagent system (HMAS). First, a novel PST bipartite dynamic compensator is introduced to track the leader’s state within PST. Then, through dynamic compensator, a novel PST bipartite controller is developed. The significant advantage of this control scheme is that it can achieve precise tracking within finite-time, which is not constrained on initial values and control parameters. Furthermore, an adaptive PST compensator and a PST controller with time-varying scaling function are proposed to achieve the output consensus of the HMAS within PST. In contrast to existing compensators, the highlight of the adaptive compensator is not contingent upon the global information of the system topology, and also obviates the necessity for full access to the system matrix of the leader. Finally, a numerical example is performed to reveal the practicality of theoretical results.

In this article, a novel adaptive dynamic programming algorithm is devised to handle the multiplayer nonzero-sum (NZS) games of completely unknown nonlinear systems, subject to state and input constraints under the dynamic self-triggered mechanism. Initially, in order to eliminate the demand for system dynamics, a generalized fuzzy hyperbolic model based identifier is established, which only relies on input–output data. Then, the equivalent transformation of the reconstructed system is implemented by virtue of barrier functions. With the aid of the nonquadratic utility function, the Hamilton–Jacobi equation of the NZS game is derived. After that, an adaptive critic scheme with experience replay is employed to acquire the Nash equilibrium solution. Furthermore, a novel dynamic self-triggered rule is proposed with the dead-zone operation, which not only significantly reduces source consumption but also overcomes the implementation difficulty of monitoring hardware in the event-triggered mechanism. Moreover, the stability of the system and the uniform ultimate boundedness of the critic weights are guaranteed. Ultimately, two simulation examples are given to validate the feasibility of the developed method.

This article investigates the$H_{\infty } $optimal output-feedback control problem of heterogeneous multiagent systems. First, a hierarchical control scheme is designed to reduce the algorithm’s complexity and the expected global performance constraints can be ensured by designing compensation input indicator. Second, a relaxation parameter is introduced to derive the optimal solution under output feedback. Additionally, a policy iteration algorithm and vectorization method are presented to determine local and collaborative control gains. This relaxation parameter serves to ease the design conditions for performance indicator. In addition, adaptive dynamic programming (ADP) is introduced and reversible datasets are designed to obtain optimal parameters with unknown drift dynamics. This design achieves model-free control of optimal output feedback for heterogeneous multiagent systems. Finally, the effectiveness of the control schemes is validated using F-16 aircraft and 4-wheel autonomous vehicles as examples.

This article explores the challenge of triggered optimal control for random differential equations (RDEs) with Markovian switching. We initially address the inherent contradiction between whether to comply with or bypass the event-triggered mechanism. By navigating this challenge, we ensure noise-to-state stability (NSS) for RDEs through event-triggered control (ETC). Furthermore, we establish that random nonlinear systems utilizing self-triggered control (STC) can achieve NSS, by setting a minimum triggering time to prevent Zeno behavior. Lastly, by adopting the adaptive dynamic programming (ADP) strategy, we develop self-triggered optimal control for random systems with Markovian switching, ensuring the uniform ultimate boundedness (UUB) of the signals in all closed-loop systems. This article addresses three key gaps in the field of RDE optimal control, contributing substantially to both theoretical and practical advancements. To demonstrate the method’s feasibility, we include a representative example with simulation results.

This article investigates the robust fault estimation (FE) scheme for a class of discrete-time nonlinear dynamics subject to simultaneous bounded disturbances, sensor and actuator/process faults through the T–S fuzzy method. By constructing an augmented system that contains sensor faults as part of its state, a dissipativity-based FE observer is proposed to achieve sensor and actuator/process faults reconstruction. Combine with the fuzzy Lyapunov function method and dissipativity theory, some brand-new conditions with slack scalars and matrices are attained to ensure that observation error systems are strictly$(\mathcal {R},\mathcal {W},\mathcal {S}) -\delta -$dissipative. Besides, to improve the transient FE performance, regional pole placement problem is also considered in FE observer design. It is worth mentioning that different from some existing results which assume that actuator faults occur in a constant form, the method given in this article is suitable for more types of faults such as time-varying ones. By two simulation experiments, the validity and practicability of the proposed FE observer are fully illustrated.

This article addresses the event-based adaptive optimal fault-tolerant consensus control problem for a class of nonlinear multiagent systems (MASs) under a directed graph. It can be solved that both control gains and actuator failure parameters are unknown in considered MASs, which enhances the practicability of optimal consensus control. First, a reinforcement learning-based distributed optimal control law is designed by constructing the identifier-critic-actor learning networks. Furthermore, by utilizing the distributed optimal control law as an auxiliary variable, an adaptive fault-tolerant controller is proposed to effectively compensate for actuator failures. Meanwhile, a co-design scheme is proposed for the construction of an event-triggered control input with the fault-tolerant property. It is proven that the designed control input ensures the boundedness of closed-loop systems through rigorous stability analysis. Finally, the effectiveness of the developed approach can be illustrated via simulations of numerical nonlinear MASs and a group of autonomous underwater vehicles.

This research article considers the subject of finite-time control for a set of uncertain high-order stochastic nonlinear systems (HOSNSs) with unknown time-varying control coefficients. The growth conditions for the uncertain nonlinearities are more inclusive compared to those found in most previous studies. The exponents of high-order systems can take arbitrary rational numbers, which can be represented by two positive odd integers, and they are not all exactly larger than 1. Based on the conventional backstepping idea, the method ofadding a power integratoris adopted to design an adaptive controller that renders the closed-loop system stochastically finite-time stable (SFTS). Due to the general nonlinearities, high-order exponents, and stochastic characteristics, the design process requires more effort, so domination instead of cancellation techniques is used. A simulation example demonstrates the correctness of the theoretical results.

In this article, an adaptive dynamic programming (ADP)-based optimal control strategy for a series of fractional-order nonlinear systems (FONS) with unknown control directions is investigated. To eliminate the challenges posed by unknown control directions, fractional-order Nussbaum-type functions are introduced for FONS, expanding the range of possible applications. Additionally, since system performance is compromised by disturbances, a fractional-order disturbance observer is designed to counteract the effects of external disturbances and enhance system robustness. Furthermore, differential geometric methods are employed to investigate FONS, constructing appropriate diffeomorphism that provide equivalent systems for decoupled linearization. Then, an optimal control method is studied for a class of strictly feedback FONS, in which Nussbaum-type functions are combined with ADP theory during the backstepping design process. Finally, based on fractional Lyapunov stability theory and backstepping method, it is guaranteed that all signals of the closed-loop FONS are uniformly ultimately bounded (UUB). Numerical simulation and a PMSM model are utilized to verify the effectiveness of the presented method.

The issue of predictor-based neural fixed-time dynamic surface control for the nonlinear systems with unknown backlash-like hysteresis is the research focus of this article. By applying the predictor-based neural control scheme, the system nonlinear functions can be smoothly estimated. In addition, an improved dynamics surface is proposed to decrease the difficulty of the controller design procedure while ensuring that the dynamic surface compensating signals can satisfy the fixed-time stability. Further, on the basis of fixed-time theorem and backstepping control technology, the designed controller can ensure all signals of the considered closed-loop systems are fixed-time bounded in the presence of unknown backlash-like hysteresis. Eventually, the simulation cases are given to imply the effectiveness of the designed method.

This article studies a prescribed finite-time consensus problem for uncertain nonlinear multiagent systems (MASs) with event-triggered updates. First, the novel finite-time performance boundaries are proposed to ensure that consensus deviations converge to the predefined steady-state zones within a preassigned time, and by using asymmetrically parallel boundaries to constrain consensus errors to narrow feasible regions, small overshoots of consensus errors are assured. Second, by utilizing the inherent approximation property of fuzzy logic systems (FLSs), a fuzzy state observer is devised to recover the unmeasurable states. Based on the observation outcomes, an improved event-triggered output-feedback controller is synthesized so that the number of control input updates is reduced without incurring an evidently deteriorated control performance. The salient merits of the proposed approach are that all consensus errors are free from great overshoots, while settling time can be explicitly assigned in advance. Finally, two examples are given to verify the validity of theoretical results.

To address the finite-horizon coupled two-player mixedH2/H∞control challenge within a continuous-time affine nonlinear system, this research introduces a distinctiveQ-function and presents an innovative adaptive dynamic programming (ADP) method that operates autonomously of system-specific information. Initially, we formulate the time-varying Hamilton–Jacobi–Isaacs (HJI) equations, which pose a significant challenge for resolution due to their time-dependent and nonlinear nature. Subsequently, a novel offline policy iteration (PI) algorithm is introduced, highlighting its convergence and reinforcing the substantive proof of the existence of Nash equilibrium points. Moreover, a novel action-dependentQ-function is established to facilitate entirely model-free learning, representing the initial foray into the mixedH2/H∞control problem involving coupled players. The Lyapunov direct approach is employed to ensure the stability of the closed-loop uncertain affine nonlinear system under the ADP-based control scheme, guaranteeing uniform ultimate boundedness (UUB). Finally, a numerical simulation is conducted to validate the effectiveness of the aforementioned ADP-based control approach.

Magnetic flux leakage (MFL) testing and analysis is an effective non-destructive testing (NDT) method of pipeline health. The health status of pipelines is evaluated by analyzing MFL signals, especially the defect analysis. However, complex operating conditions and equipment deviations may cause deformation on the defects. Availability and integrity of the MFL defects are the keys to accurate signal processing. Due to the limited interpretability of abstract networks and insufficient feature extraction, it is hard to accurately recover the deformed defects. Considering the problems above, an MFL deformed defect recovery method with the guidance of physical features is proposed. The proposed method integrates a deep feature extraction model into a sparse autoencoder, whose parameters are guided by the combination of MSE loss and physics loss. First, the deep feature extraction model fully extracts the abstract features, which contains more information on deformed defects. Second, the sparse penalty factor in the sparse autoencoder reduces the feature redundancy of deformed defects. Third, the physical features are extracted to guide the MFL deformed defect recovery relying on the MFL mechanism for the first time, which enhances the robustness and accuracy of the network. Finally, several comparison experiments have been conducted on measured and simulated MFL defects. The results show that the proposed method is effective in deformed defect recovery.Note to Practitioners—The motivation of this paper is a practical problem of MFL deformed defect recovery. The deformation on MFL defects affects defect inversion and evaluation seriously, which causes great potential harm to pipeline safety transmission. In this paper, compared with traditional neural network-based methods, physical features based on MFL mechanism are extracted to guide the parameter update. In addition, a pre-trained model replaces regular convolutional layers to extract features sufficiently, while a sparse factor is applied to reduce redundant features. The proposed method not only utilizes deep features from deep networks, but also increases the guidance of physical features, so that the algorithm is more robust and accurate. The experiment results show that our proposed method is more effective than the comparative methods on MFL simulated and measured defects. In a word, the proposed method has strong theoretical research and practical value.

This paper investigates one type of distributed constrained optimization problem, e.g., the economic dispatch problem, in the presence of DoS attacks. Therein, multiple DoS attackers are collaborative to impede the communication transmission and change the communication topology at will. Consequently, the convergence and/or optimality of distributed algorithm may be compromised. To reduce the effect of this kind of DoS attacks, a distributed resilient initialization-free Jacobi descent algorithm is proposed. It is designed with three switched control protocols which enable the proposed algorithm reasonably employing the estimations to replace the missing information when attacks occur. Meanwhile, the proposed method is embedded with second order information, resulting in faster convergence speed. Moreover, theoretical analysis results are provided to show that the proposed algorithm can exponentially converge to the global optimal solution of the studied problem. Finally, simulation results tested in IEEE 30-bus system validate its effectiveness and flexibility.Note to Practitioners—The economic dispatch is a key issue in smart grid, which can be formulated as a kind of distributed constrained optimization problem. Since the distributed algorithms work under distributed sensor networks, they are easier to undergo DoS attacks. To address this issue, this paper presents a distributed resilient initialization-free Jacobi descent algorithm, which features strong robustness to resist DoS attacks and faster convergence. Meanwhile, the proposed method is shaped for common constrained optimization problem with better expansibility. We conduct the global convergence and optimality proofs, which benefits the practitioners to estimate the convergence performance, e.g., the convergence rate. Simulations further show the correctness and effectiveness of the proposed method. In future, we will pay more attention on the non-convex constrained optimization problem.

This paper studies the mixed$H_{2}/H_{\infty} $control problem for nonlinear closed-loop Stackelberg games via adaptive dynamic programming (ADP) technology. Firstly, by constructing a cost function with the Lagrange multiplier, the hierarchical Stackelberg game problem is transformed into a coupled Hamilton-Jacobi-Isaacs (HJI) equations problem. In the second place, the critic-actor neural networks (NNs) framework is established to approximate the cost functions and control strategies of Stackelberg game. The corresponding algorithm flow is given. This is a novel idea that NNs are used to approximate the control strategies in Stackelberg game that cannot be given a specific form. Finally, the algorithm is applied to the load frequency control (LFC) problem of single-area power system. The optimal neural network weights are obtained by the designed ADP algorithm. And the comparison results of the three control schemes show that the mixed$H_{2}/H_{\infty} $based on Stackelberg game can not only achieve better frequency response, but also have better performance indicators for leader. Note to Practitioners—$H_{\infty} $control studies the anti-interference ability of the system, and the designed controller should attenuate the suppression coefficient of adjusting error and external disturbance to a given minimum level.$H_{2}$control is optimal control, and the controller is designed to minimize the cost function of system state and system input. In general, in$H_{\infty} $or$H_{2}$control, only a single control performance can be achieved. In the control system design, if the control system satisfies both$H_{\infty} $and$H_{2}$performance, the overall performance of the system will be greatly improved. Therefore, in engineering practice, in order to achieve satisfactory system performance, the mixed$H_{2}/H_{\infty} $control is more attractive, because it integrates the advantages of$H_{\infty} $control and$H_{2}$control. On the other hand, in a real game, Stackelberg game is the best choice if two players are not on the same level. This paper takes Stackelberg game as the main research object. Stackelberg game is a kind of game problem in which the participants have different decision-making status, and there is one or more decision makers higher than other decision makers. As a traditional control strategy, PID control and SMC control are not uncommon in the research of LFC. With the development of control theory and application, state space and optimal control strategy are also applied to the design of load frequency control system. Intelligent control is the current development trend of LFC research.

This article investigate the cooperative differential game (CDG) for continuous-time linear Itô stochastic systems with markovian jumps (SSMJ) to obtain the Pareto solutions. Different from most existing works studying nonzero-sum games, this article studies the CDG on the quadratic infinite horizon for the Itô-type SSMJ with unknown system matrix and transition probability. A novel$Q$-learning online algorithm is developed, which consists of that (i) the optimal control problem is equivalent to solving a stochastic algebraic Riccatic equation (ARE); (ii) the joint cost function is approximated by a critic neural network (NN) and Pareto efficient is approximated by two actor NNs. The rigorous stability analysis shows that the system state for SSMJ and the NN weight errors are uniformly ultimately bounded (UUB). Finally, the theory analysis is validated by a numerical example with detailed discussions.Note to Practitioners—In practical applications, many systems are often affected by the change of external environment or the failure of internal components, which leads to the random jump of system parameters. Markovian jump system can effectively describe the above problems. And when the system model is disturbed by the internal parameters, the system control input and external environment, the random errors of state measurement, and other random factors, the deterministic model can no longer accurately describe the controlled system. Therefore, the SSMJ can describe practical problems more accurately. By cooperation, in general, the cost one specific player incurs is not uniquely determined anymore. If all players decide, for example, to use their control variables to reduce the cost of player 1 as much as possible, a different minimum is attained for player 1 compared with that in the case where all players agree collectively to help a different player in minimizing his cost. So, depending on how the players choose to ‘divide’ their control efforts, a player incurs different ‘minima’. Therefore, we will design an online learning algorithm to obtain pareto solutions with different weights. On the other hand, in practice, it is difficult to obtain an accurate system model. In order to solve this problem, a novel scheme is designed by using$Q$-learning technology, which does not need system matrix.

In this paper, to tackle the optimal control problem, we propose a$\mathcal{Q}$-Learning approach for continuous-time nonlinear systems without any dynamic information. Primarily, the Hamiltonian and optimum cost functions are utilized to articulate the$\mathcal{Q}$-function of continuous-time affine systems. To reduce the dependence of algorithms on system information, a novel$\mathcal{Q}$-Learning approach is derived to obtain optimal solutions of nonlinear continuous-time systems without requiring knowledge of either the drift information$p(x)$or input gain$q(x)$. To implement this approach, critic and actor neural networks can be iterated alternately using an integral reinforcement learning method to estimate the$\mathcal{Q}$-function. Furthermore, all signals in closed-loop system are demonstrated to be ultimate uniform bounded (UUB). It is worth noting that there exist rare literatures focused on the optimal control problem of continuous-time nonlinear uncertain systems via the$\mathcal{Q}$-Learning for actor/critic networks iteration. Finally, two simulations are used to confirm the effectiveness of the proposed algorithm.Note to Practitioners—Nonlinear continuous-time systems, being ubiquitous in engineering practice, are widely employed due to their versatility and effectiveness. Aiming at such systems, a$\mathcal{Q}$-learning approach with optimal feature is proposed to strengthen control efficiency while reduce costs. However, it is well known that accurately capturing all the dynamic information of the system is a formidable task in practical operation. This defect inevitably weakens the feasibility of model-based control algorithms. Since the$\mathcal{Q}$-learning algorithm presented in this paper does not require any dynamic knowledge of systems, it is promising enabler in enhancing the effectiveness and flexibility of engineering activities.

The optimal tracking problem for continuous-time boiler turbine systems (BTSs) with asymmetric input constraints is considered in this paper. Considering that continuous updating of control input will reduce the service life of engineering oriented system, a novel dynamic event-based triggering mechanism is proposed to reduce the number of controller updates, where a positive internal dynamic variable is introduced to expand the threshold. The objective is to find the optimal event-triggered control strategy for a given performance index function so that the system can track the ideal signal while minimizing the cost function. Firstly, by introducing tracking error variable, the optimal tracking problem is transformed into the optimal stability problem with symmetric input constraints. Then, three neural networks are designed to approximate the system model, cost function and control strategy respectively, and the feasibility of the proposed optimization algorithm is strictly proved by Lyapunov method, and the system will not exhibit Zeno behavior. Finally, simulation results effectively indicate the feasibility of the developed method in the industrial oriented system.Note to Practitioners—With the development of the national economy, China’s demand for electricity is also increasing. The BTSs in thermal power generation unit should not only realize load tracking, but also consider minimizing fuel consumption, minimizing pollutant emissions, maximizing the service life of BTSs. In this paper, adaptive dynamic programming (ADP) method and dynamic event-based mechanism are used to solve the optimal tracking problem of BTSs. The dynamic event-based mechanism not only affects the real performance of the BTSs, but also reduces the number of controller updates and saves resources of the BTSs.

Distributed control of DC microgrid is becoming more and more important in modern power system. An important control goal is to ensure voltage stability and current sharing of DC bus. In the presence of constant power loads and uncertainties, a novel distributed quadratic optimum control technique based on reinforcement learning (RL) is developed in order to ensure correct current sharing and adequate performance. Firstly, the system model with power coupling is established and transformed into a linear heterogeneous multi-agent system (MAS) with unknown disturbances. Subsequently, a neural network (NN)-based adaptive model-free observer is developed. Since not all followers have direct access to the leader’s information, a distributed cooperation performance index with discount component is created by fusing the dynamics of the observer and the follower. The$Q$-learning technique is applied to obtain the optimal control strategy to achieve voltage stabilization and current sharing without using system dynamics. Finally, simulation and experimental results show the effectiveness of this strategy.

This article investigates the cooperative control problem for stochastic multiagent systems (MASs) with dynamic constraints. A new universal barrier function is proposed, which is applicable to many systems with different types of constraint functions, even unconstrained systems. Several mapping functions are constructed to constrain the state variables directly without feasibility conditions, and the tracking control is achieved for stochastic MASs with deferred full-state constraints under the backstepping framework. In order to regulate the tracking error more precisely, the funnel error transformation is improved and the deferred funnel controller is developed by introducing a preassigned finite-time function. Based on the deferred funnel controller, the tracking error can be maintained within the predetermined funnel in the preassigned time. The convergence time can be defined according to the actual requirements, and it is independent of the design controller parameters and initial conditions. Finally, some simulation results are given to demonstrate the effectiveness of the proposed control algorithm.

In this article, a new leader-following tracking control approach is investigated for stochastic multiagent systems with multibridge-hole output constraints. The multibridge-hole output constraints mean that the output of the system is constrained in some intervals and unconstrained in other intervals. The constrained and unconstrained intervals can be set arbitrarily. By designing a new shift function to construct the barrier Lyapunov function, the optimal controller is constructed by combining the backstepping technique with the adaptive dynamic programming technique. The model network is used to estimate the unknown disturbances and uncertainty terms in the system. The critic network and the actor network are constructed such that the designed controller adheres to the Bellman optimality principle and gives the optimal solution of the system. The proposed control method is versatile and compatible with various types of output constrained control problems, such as unconstrained control problems, constrained control problems, and delay constrained problems without changing the structure of the controller. Finally, some simulation results are given to verify the effectiveness of the method.

The problem of the large-scale interconnected system (LSIS) control is prevalent in practical engineering and is becoming increasingly complex. In this article, we propose a novel decentralized event-triggered tracking control (ETTC) strategy for a class of continuous-time nonlinear LSIS with unmatched interconnected terms and asymmetric input constraints. First, auxiliary subsystems are established to address the unmatched cross-linking terms. Next, the dynamics states of the tracking error and the exosystem are combined to construct a nominal augmented subsystem. By employing a nonquadratic performance function, the input-constrained decentralized tracking control problem is transformed into an optimal control problem for the nominal augmented subsystem. A group of independent parameters and event-triggered conditions are designed to save communication bandwidth and computational resources. Subsequently, the critic-only adaptive dynamic programming (ADP) method is used to solve the Hamilton-Jacobi-Bellman equation (HJBE) associated with the optimal control problem. To improve training success rate, the weights of the critic neural network (NN) are updated by introducing a particle swarm optimization algorithm (PSOA). The tracking error and the NN weights are proved to be uniformly ultimately bounded (UUB) under the proposed ETTC by using the Lyapunov extension theorem. Finally, the simulation example of an unmatched interconnected system is provided to verify the validity of the proposed decentralized method.

In this article, the optimal control strategy for organism is investigated by using the adaptive dynamic programming (ADP) method under the architecture of nonzero-sum games (NZSGs). First, a tumor model is established to formulate the interaction relationships among normal cells, tumor cells, endothelial cells, and the concentrations of drugs. Then, the ADP-based method of single-critic network architecture is proposed to approximate the coupled Hamilton-Jacobi equations (HJEs) under the medicine dosage regulation mechanism (MDRM). According to the game theory, the approximate MDRM-based optimal strategy can be derived, which is of great practical significance. Owing to the proposed mechanism, the dosages of the chemotherapy and anti-angiogenic drugs can be regulated timely and necessarily. Furthermore, the stability of the closed-loop system with the obtained strategy is analyzed via the Lyapunov theory. Finally, a simulation experiment is conducted to verify the effectiveness of the proposed method.

This article investigates the tracking control problem for nonlinear systems. An adaptive model is proposed to represent the dead-zone phenomenon and solve its control challenge with a Nussbaum function in conjunction. Drawing inspiration from the existing prescribed performance control schemes, a novel dynamic threshold scheme is developed that fuses a proposed continuous function with a finite-time performance function. A dynamic event-triggered strategy is applied to reduce the redundant transmission. The proposed time-varying threshold control strategy has fewer updates than the traditional fixed threshold and improves the efficiency of resource utilization. A command filter backstepping approach is employed to prevent the complexity explosion faced by the computation. The suggested control strategy ensures that all system signals are bounded. The validity of the simulation results has been verified.

This article investigates the finite-time containment control problem for fractional-order nonlinear multiagent systems (FOMASs) with event-triggered inputs. First, a new Lyapunov stability lemma is developed, which provides a basic approach for the completely unknown nonlinear fractional-order system to realize finite-time convergence. Considering the containment control for FOMASs, the restricted assumption that the derivatives of leaders' trajectories are known to the followers is removed, and only the boundedness of derivatives is required in this article, whose upper bound need not be known. To reduce the burden of communication, an event-triggered condition consisting of the control input and a decreasing function related to the containment errors is devised, which can provide more design freedom to balance the system performance and communication resources. According to the proposed fractional-order finite-time convergence lemma, a distributed adaptive containment control scheme is developed, such that each follower can be steered to the convex hull spanned by the leaders in finite time. Simulation examples further demonstrate the effectiveness of our proposed method.

This article addresses the solution of continuous-time linear Itô stochastic systems with Markovian jumps using an online policy iteration (PI) approach grounded in -learning. Initially, a model-dependent offline algorithm, structured according to traditional optimal control strategies, is designed to solve the algebraic Riccati equation (ARE). Employing Lyapunov theory, we rigorously derive the convergence of the offline PI algorithm and the admissibility of the iterative control law through mathematical analysis. This article represents the first attempt to tackle these technical challenges. Subsequently, to address the limitations inherent in the offline algorithm, we introduce a novel online -learning algorithm tailored for Itô stochastic systems with Markovian jumps. The proposed -learning algorithm obviates the need for transition probabilities and system matrices. We provide a thorough stability analysis of the closed-loop system. Finally, the effectiveness and applicability of the proposed algorithms are demonstrated through a simulation example, underpinned by the theorems established herein.

This article studies the containment control problem of nonlinear multiagent systems (MASs) subjected to communication link faults and dead-zone inputs. In case of an unknown fault in the communication link, there is no constant Laplacian matrix anymore and each follower agent cannot be informed of the global information simultaneously. To deal with this problem, an adaptive compensating estimator is constructed to estimate the signal spanned by the leaders. Instead of using the linear filter, a nonlinear filter is employed, which both solves the classical complexity explosion in the traditional backstepping method and flushes out the usefulness of the boundary layer error. Considering the dead zone input, we propose two event-triggered schemes, that is, the update-triggered scheme and the transmit-triggered scheme. In the former, the threshold function involves the tracking errors and additional dynamic variable, which can provide the desirable tradeoff between the containment control performance of the considered MASs and saving communication resources. In the latter, the triggered condition is designed according to the characteristic of dead zone, which makes the communication burden be reduced further. Following the backstepping design framework, an adaptive containment control is constructed, it is shown that the containment error can converge to an adjustable residual set even if MASs are subjected to the unknown and bounded communication link faults and dead-zone inputs. Finally, an example is given to show the effectiveness of the proposed results.

In this article, the constrained adaptive control strategy based on virotherapy is investigated for organism using the medicine dosage regulation mechanism (MDRM). First, the tumor-virus-immune interaction dynamics is established to model the relations among the tumor cells (TCs), virus particles, and the immune response. The adaptive dynamic programming (ADP) method is extended to approximately obtain the optimal strategy for the interaction system to reduce the populations of TCs. Due to the consideration of asymmetric control constraints, the nonquadratic functions are proposed to formulate the value function such that the corresponding Hamilton-Jacobi-Bellman equation (HJBE) is derived which can be deemed as the cornerstone of ADP algorithms. Then, the ADP method of a single-critic network architecture which integrates MDRM is proposed to obtain the approximate solutions of HJBE and eventually derive the optimal strategy. The design of MDRM makes it possible for the dosage of the agentia containing oncolytic virus particles to be regulated timely and necessarily. Furthermore, the uniform ultimate boundedness of the system states and critic weight estimation errors is validated by Lyapunov stability analysis. Finally, simulation results are given to show the effectiveness of the derived therapeutic strategy.

In this article, the novel adaptive neural networks (NNs) tracking control scheme is presented for nonlinear partial differential equation (PDE)-ordinary differential equation (ODE) coupled systems subject to deception attacks. Because of the special infinite-dimensional characteristics of PDE subsystem and the strong coupling of PDE-ODE systems, it is more difficult to achieve the tracking control for coupled systems than single ODE system under the circumstance of deception attacks, which result in the states and outputs of both PDE and ODE subsystems unavailable by injecting false information into sensors and actuators. For efficient design of the controllers to realize the tracking performance, a new coordinate transformation is developed under the backstepping method, and the PDE subsystem is transformed into a new form. In addition, the effect of the unknown control gains and the uncertain nonlinearities caused by attacks are alleviated by introducing the Nussbaum technology and NNs. The proposed tracking control scheme can guarantee that all signals in the coupled systems are bounded and the good tracking performance can be achieved, despite both sensors and actuators of the studied systems suffering from attacks. Finally, a simulation example is given to verify the effectiveness of the proposed control method.

This article focuses on the issue of novel dynamic event-triggered consensus control of multiagent systems (MASs) with denial-of-service (DoS) attacks. Different from the conventional Markovian switching topologies, the generally uncertain semi-Markovian (GUSM) switching topologies with partially unknown elements and time-dependent uncertainties are constructed for the leader-following MASs by considering the equipment performance and external uncertain environment influence. To save communication resources, the novel dynamic memory event-triggered strategy (DMETS) is presented to decrease the frequency of communication between agents. Some secure consensus control criteria are established for the MASs with GUSM switching topologies and DoS attacks due to the potential system communication disruption caused by attackers. Finally, two physical system examples are designed to prove the effectiveness of the presented method.

In this article, the data-based output consensus of discrete-time multiagent systems under switching topology (ST) is studied via reinforcement learning. Due to the existence of ST, the kernel matrix of value function is switching-varying, which cannot be applied to existing algorithms. To overcome the inapplicability of varying kernel matrix, a two-layer reinforcement learning algorithm is proposed in this article. To further implement the proposed algorithm, a data-based distributed control policy is presented, which is applicable to both fixed topology and ST. Besides, the proposed method does not need assumptions on the eigenvalues of leader's dynamic matrix, it avoids the assumptions in the previous method. Subsequently, the convergence of algorithm is analyzed. Finally, three simulation examples are provided to verify the proposed algorithm.

This article is concerned with the convergence property and error bounds analysis of value iteration (VI) adaptive dynamic programming for continuous-time (CT) nonlinear systems. The size relationship between the total value function and the single integral step cost is described by assuming a contraction assumption. Then, the convergence property of VI is proved while the initial condition is an arbitrary positive semidefinite function. Moreover, the accumulated effects of approximation errors generated in each iteration are taken into consideration while using approximators to implement the algorithm. Based on the contraction assumption, the error bounds condition is proposed, which ensures the approximated iterative results converge to a neighborhood of the optimum, and the relation between the optimal solution and approximated iterative results is also derived. To make the contraction assumption more concrete, an estimation way is proposed to derive a conservative value of the assumption. Finally, three simulation cases are given to validate the theoretical results.

This article is considered on underactuated fractional-order stochastic systems (FOSSs) with actuator saturation and incrementally conic nonlinear terms, whose fractional-order α ∈ (0,1) . First, to bring FO dynamic signals, solving the unmodeled dynamics, in the meantime, the saturated nonlinear term of the control input is taken into account. At the time, to cope with the stability issue of FOSS under such situation, the fault tolerant resilient controller based on underactuated condition is designed. Then, according to the method of the Lyapunov and It∧ o differential formulation to design proper multiple Lyapunov-Krasovskii (L-K) functions, such that, a novel sufficient condition of the robustly asymptotically stability of fuzzy FOSS under underactuated conditions is rigorously proved in terms of linear matrix inequality (LMI). Furthermore, in order to research the mean square stability of the above-mentioned system, so the solution of FOSS is obtained to achieve this purpose. By applying the above method, which is proposed in this work that the controlled system can be obtained with faster response and higher control accuracy. At last, to display the superiority of the above-mentioned scheme is effective, tethered satellite system and numerical results are presented.

In this article, a novel fault reconstruction algorithms for fractional-order nonlinear switching systems (FONSSs) with actuator and sensor faults are investigated. First, fractional-order nonlinear system (FONS) with faults, is transformed into two fast and slow subsystems using global differential homogeneous transformation, one of which is unaffected by the fault and the state is partially observable; the other subsystem is affected by the fault but the state is fully observable. After that, it is introduced for the first time that persistent dwell-time (PDT) switching is taken into consideration in the design process of the observer for FONSS, which overcomes the transient problem of the switching moment and ensures the stability of the error dynamics equations of the two fast and slow subsystems. In addition, to eliminate the impact of faults, an optimal adaptive fault-tolerant control strategy based on actor-critic architecture NN are designed to effectively compensate. Finally, the effectiveness of the proposed control strategy is verified by simulation results.

This article investigates an optimized containment control problem for multiagent systems (MASs), where all followers are subject to deferred full-state constraints. A universal nonlinear transformation is proposed for simultaneously handling the cases with and without constraints. Particularly, for the constrained case, initial values of states are flexibly managed to the midpoint between upper and lower boundaries by utilizing a state-shifting function, thus eliminating the initial restriction conditions. By deferred constraints, the state is forced to fall back into the restrictive boundaries within a preassigned time. A neural network (NN)-based reinforcement learning (RL) algorithm is executed under the identifier-critic-actor architecture, where the Hamilton-Jacobi-Bellman (HJB) equation is built in every subsystem to optimize control performance. For actor and critic NNs, updating laws are simplified, since the gradient descent method is performed based on a simple positive function rather than square of Bellman residual error. In view of the Lyapunov stability theorem and graph theory, it is proved that all signals are bounded and the outputs of followers can eventually enter into the convex hull constituted by leaders. Finally, simulations confirm the validity of the proposed approach.

This article investigates the fully distributed resilient practical leader-follower bipartite output consensus (LFBOC) problem for heterogeneous linear multiagent systems (MASs) with denial-of-service (DoS) attacks and actuator faults. To estimate the leader matrix and state in the presence of DoS attacks, two novel adaptive event-triggered observers are proposed based on newly developed lemmas, and then the adaptive event-triggered fault-tolerant controller without chattering behavior is developed to solve the LFBOC problem. Different from most existing resilient practical LFBOC working with DoS attacks and actuator faults, our method does not rely on any global information, event-triggered communication between neighbors and discrete update controllers are implemented simultaneously. Finally, an example is presented to well illustrate the effectiveness of developed method.

This article analyzes and validates an approach of integration of adaptive dynamic programming (ADP) and adaptive fault-tolerant control (FTC) technique to address the consensus control problem for semi-Markovian jump multiagent systems having actuator bias faults. A semi-Markovian process, a more versatile stochastic process, is employed to characterize the parameter variations that arise from the intricacies of the environment. The reliance on accurate knowledge of system dynamics is overcome through the utilization of an actor-critic neural network structure within the ADP algorithm. A data-driven FTC scheme is introduced, which enables online adjustment and automatic compensation of actuator bias faults. It has been demonstrated that the signals generated by the controlled system exhibit uniform boundedness. Additionally, the followers' states can achieve and maintain consensus with that of the leader. Ultimately, the simulation results are given to demonstrate the efficacy of the designed theoretical findings.

This article addresses the problem of containment control for continuous-time multiagent systems. A containment error is first given to show the coordination between the outputs of leaders and followers. Then, an observer is designed based on the neighbor observable convex hull state. Under the assumption that the designed reduced-order observer is subject to external disturbances, a reduced-order protocol is designed to realize the containment coordination. In order to ensure the designed control protocol can achieve the effect of the main theories, a corresponding Sylvester equation is given with a novel approach which proves that the Sylvester equation is solvable. Finally, a numerical example is given to verify the validity of the main results.

This article proposes a practical and generalizable object detector, termed feature extraction-fusion-prediction network (FEFP-Net) for real-world application scenarios. The existing object detection methods have recently achieved excellent performance, however they still face three major challenges for real-world applications, i.e., feature similarity between classes, object size variability, and inconsistent localization and classification predictions. In order to effectively alleviate the current difficulties, the FEFP-Net with three key components is proposed, and the improved detection accuracy is proved in various applications: 1) Extraction Phase: an adaptive fine-grained feature extraction network is proposed to capture features of interest from coarse to fine details, which effectively avoids misclassification due to feature similarity; 2) Fusion Phase: a bidirectional neighbor connection network is designed to identify objects with different sizes by aggregating multilevel features and 3) Prediction Phase: in order to improve the accuracy of object localization and classification, a task specific prediction network is presented, which sufficiently exploits both the spatial and channel information of features. Compared with the State-of-the-Art methods, we achieved competitive results in the MS-COCO dataset. Further, we demonstrated the performance of FEFP-Net in different application fields, such as medical imaging, industry, agriculture, transportation, and remote sensing. These comprehensive experiments indicate that FEFP-Net has satisfactory accuracy and generalizability as a basic object detector.

This article investigates the problem of adaptive fuzzy consensus control for a class of interconnected nonlinear multiagent systems. To effectively address the dual requirement of full-state constraints and prescribed performance, we propose a novel unified barrier function that effectively constrains the states and errors separately. This approach eliminates the need for tedious computations, allowing for direct presetting of state and error bounds. The concept of “bridge hole” is introduced, referring to the states' transition from unconstrained to constrained and back to unconstrained. The control method demonstrates the capability to handle multiple bridges, with the number of bridges being able to be${\bm {n}}$or 0. Furthermore, event-triggered mechanisms are also considered among neighbors and the internal event-triggered mechanism in the actuator-to-controller channel, with the aim of minimizing the communication burden. Finally, some simulation results are provided to validate the effectiveness of the approach.

This article first defines finite-time semipassivity of switched nonlinear systems by using multiple storage functions and multiple supply rates. It means that each active subsystem is finite-time passive outside a ball, while the total of the energy changing at all switching times is bounded. Based on this passivity concept, finite-time practical stability is obtained. Then, a set of finite-time fuzzy adaptive semipassification controllers and a switching law relate to state are designed constructively to solve output tracking control problem of uncertain switched strict-feedback nonlinear systems. Finally, the effectiveness of the developed control method is validated by two simulation examples.

In this research, an event-based adaptive fixed-time fuzzy containment fault-tolerant control issue for multi-agent systems (MASs) with positive odd rational powers is discussed. As a result of the presence of unknown nonlinear functions, fuzzy logic systems (FLSs) can be employed to estimate them with the support of FLSs’ approximation capability. The adding a power integrator (API) can be introduced to handle the difficulties encountered in the backstepping design process for high-order nonlinear systems. Further, the event-triggered control ideology was carried out among neighbors of MASs, which is used to minimize the communication burden. In addition, the controlled MASs considered four types of sensor faults, adopting the adaptive control methods to achieve effective estimation of unknown fault parameters. According to the frame of backstepping control, an adaptive fuzzy containment event-triggered controller for MASs with positive odd rational powers was designed that can realize all signals are fixed-time bounded, even if MASs may exist sensor faults. In the last resort, the illustrative example can manifest the validity of the suggested approach.

In order to solve the two-player Stackelberg game for the continuous-time nonlinear stochastic system, using the Takagi–Sugeno (T-S) fuzzy stochastic model, this paper defines the novel$Q$-functions and suggests an adaptive dynamic programming (ADP)-based approach that is completely model-free. First, based on the T-S fuzzy model, the overall fuzzy control policies with corresponding cost functions are designed where coupled penalty functions are considered. Subsequently, we create a novel two-level algorithm based on integral reinforcement learning and provide the proof of convergence to overcome challenge of computing the optimal cost functions analytically. On this basis, in order to achieve entirely model-free learning, which is the first attempt in solving fuzzy stochastic nonlinear continuous-time systems with the Stackelberg game problem, the innovative action-dependent$Q$-functions are developed. Fuzzy linearization technique and$Q$-learning algorithm are ingeniously combined in this article to solve their respective difficulties. In addition, the Lyapunov approach under the ADP-based control scheme ensures the stability of the closed-loop nonlinear stochastic system based on fuzzy approximation and is characterized by asymptotic stability. Finally, a numerical simulation is offered to show the efficacy of the existing ADP-based control technique.

This article investigates a class of optimal joint therapeutic regimens based on adaptive dynamic programming (ADP) and generalized fuzzy hyperbolic model (GFHM) for multiple tumor populations subject to immune effects as well as multiple drugs. Obtaining a model of the abnormal proliferation process of single tumor cells with multiple interventions is then the first step to conducting the treatment. In this article, the trajectories of healthy and unhealthy systems are obtained by constructing a virtual affine proliferation model. A strong connected trace of multiple agents is further developed by constructing a Hamiltonian function of the drug delivery cost of the unhealthy system. ADP and GFHM are gathered as consensus–fuzzy policy iterative algorithms, in which convergence and stability are ensured and the optimal dosing scheme achieves tumor cell inhibition.

This article presents an adaptive fuzzy bipartite consensus tracking control scheme for nonlinear multiagent systems (MASs) with dead-zone inputs and actuator faults. The consensus problem for unbalanced communication topology of the MASs is difficult to deal with. First, the hierarchical algorithm is introduced to transform the consensus tracking problem for multiagents into the tracking problem of the single agent. Then, the novel error transformation and coordinate transformation are proposed based on the hierarchical design theory, leading to the condition of global Laplacian matrix information unnecessary so that the computational difficulty and communication burden are significantly reduced. Besides, the control performance would be seriously affected by the input constraints such as dead-zone and faults. There is a balance between the input constraints and the output constraints for MASs, but these two constraints are handled independently in the most existing works. Thus, a flexible performance function is developed to balance the system performance and the stability requirements by flexible signal switching. The proposed adaptive fuzzy control scheme ensures that not only the influence of the dead-zone and various types of actuator faults is eliminated, but also the preassigned finite-time tracking performance is achieved. Finally, the simulation experiment is conducted to verify the effectiveness of the established control scheme.

This article considers the predefined-time composite adaptive attitude control problem for nonlinear multiple six-rotor unmanned aerial vehicles (UAVs) with external disturbance. The multiple six-rotor UAVs considered are regarded as nonlinear multiagent systems, and each subsystem has multiple inputs. Via the backstepping recursive algorithm, a valid adaptive predefined-time control method is proposed by combining fuzzy logic systems (FLSs) and predefined-time theory. FLSs are utilized to handle unknown nonlinearity and unmodeled parts in attitude systems. By constructing a serial-parallel estimation model, the accuracy of approximating the unknown functions is ameliorated. Moreover, taking into account the prediction error of the serial-parallel estimation model, a novel composite adaptive fuzzy control scheme is developed. Unlike some existing control schemes for multi-UAVs, the developed control scheme allows one to shun the chattering phenomenon and ameliorate the approximation performance. The developed controller assures that all signals of the multiple six-rotor UAV attitude systems are bounded and the tracking errors converge to a small neighborhood of the origin within an expected settling time. Eventually, with the help of simulation results, the effectiveness of the proposed control scheme was illustrated.

This paper is concerned with the event-triggered practical fixed-time containment control for stochastic multi-agent systems, in which the input delay is considered. A novel event-triggered mechanism is proposed, where the containment error of stochastic MASs is considered as a part of the threshold function in the form of an exponential decreasing function, which can provide more freedom to balance system performance and resource utilization. Then, a practical fixed-time containment control scheme is presented, whose convergence time is not affected by the initial conditions. Furthermore, Pade approximation technique is utilized to convert the input delay to the case without delay. The design process of the controller is simplified, and the computational complexity is reduced. Finally, two examples are rendered to demonstrate the performance of the presented method.

This article investigates a finite-time fuzzy quantized control problem for a class of nonlinear systems considering deferred constraints. Instead of the tracking errors themselves, the auxiliary error variables constructed via the shifting function are employed into nonlogarithm barrier Lyapunov function to perform error constraints, not only making the restrictive conditions in initial phase be removed but also ensuring tracking errors to evolve within the preassigned regions after a given time. Then, to allow for a reduced computational cost concerning fuzzy/neural approximators, a single parameter updating based fuzzy wavelet neural network is devised to approximate the unknown nonlinearity acting on every subsystem. Furthermore, by using hysteresis quantizer to convert continuous control inputs into discrete scalars, a robust fuzzy quantized controller is synthesized with the aid of a novel quantization decomposition scheme, where the problem of constrained data bandwidth is successfully handled without involving chattering in control signals. Finally, simulations confirm the benefits and efficiency of the proposed method.

In this article, the event-triggered saturation-tolerant control problem of nonlinear multiagent systems (MASs) is investigated based on the finite-time fuzzy composite learning approach. Specifically, a novel concept, named as deferred saturation-tolerant prescribed performance control, is proposed, which guarantees the flexible prescribed performance in the face of input saturation, while there are no needs of initial restrictions on distributed errors. Moreover, by extracting weight errors from filtering operations and auxiliary variables, a finite-time fuzzy composite learning rule driven by weight and distributed errors is developed for improving the learning performance and ensuring that unknown nonlinearities are precisely estimated. Then, resorting to event-triggered communication mechanism, signal transmissions among connected agents only occur when triggering conditions are satisfied, contributing to a reduced communication burden. Finally, simulations with comparative studies are provided to confirm the effectiveness and superiority of the proposed method.

This article is concerned with the global dynamic stability problem of integrated energy system (IES) in a distributed fashion. An information energy coupling framework is established where the dynamic coupling of electricity, gas, heat, and information networks is fully considered for control design in multitime scales. Then, an innovative distributed containment control strategy is proposed to control the dynamic stability of voltage, frequency, flow, pressure, and temperature simultaneously. The convex hull of virtual leaders is adjusted dynamically to construct the stability region of states in IES. Based on the control framework, a distributed dynamic compensator is proposed to solve the distributed containment control problems of IES. The consensus and stable control of the parameters in the heterogeneous multienergy system is realized by the containment control strategy. Finally, the simulation results demonstrate the proposed strategy can synchronously achieve the control objectives with energy and topology variation.

Process fault diagnosis is of great importance to ensure the safe and stable operation of industrial systems. Many existing deep-learning-based process fault diagnosis methods assume that the samples are sufficient and obey the same distribution; however, it is almost impossible to achieve in practical industrial applications due to changing working conditions and the high cost of acquiring fault samples, which leads to a prominent performance degradation. In essence, those methods do not fully exploit the intrinsic and relevant knowledge under different working conditions. To address the above issue, a knowledge-mining-based semantic-aware network (KMSA-Net) is proposed in this article. First, a self-correlation knowledge mining subnet is proposed, where unshared attention mechanism is designed to extract knowledge inherent in each working condition so that the discriminative features can be captured. Second, a cross-correlation knowledge mining subnet is proposed, where we develop a fault relational knowledge graph so as to explicitly constrain the local consistency between the source domain, target domain, and cross-domain. Third, a semantic-aware knowledge transfer subnet is designed to impose a semantic constraint during knowledge transfer by encouraging the output of KMSA-Net to be consistent and distinguishable. These three subnets are jointly trained and then applied for cross-domain industrial process fault diagnosis. Finally, benchmark simulated experiments and real-world application experiments are conducted, and the experimental results validate the effectiveness and superiority of the proposed method.

Pipeline magnetic flux leakage (MFL) detection is an efficient and energy-saving nondestructive testing (NDT) method. However, under the high-speed detector, MFL signals become distorted with the influence of velocity effects, which adversely affects the accuracy of defect size inversion. The essential cause is the distorted signal multiplicity by velocity effects. In response to this issue, a high-precision defect size inversion method is proposed for the first time, which is called knowledge-guided contrastive fusion network (KCF-Net). First, MFL and eddy current (EC) mechanisms are analyzed, which are concluded that the sensitivity of EC signals to speed is much lower than that to defect sizes, so that EC and MFL abstract features are mined to improve the sensitivity of defect sizes. Moreover, MFL mechanism representations are mined to supervise neural networks to enhance the interpretability of the network. MFL and EC knowledge including abstract features and mechanism representations is fused to highlight the disparities between undistorted and distorted signals and enrich available information. Then, joint decision-making is proposed to eliminate the instability of fusion knowledge and enhance the universality and effectiveness of defect inversion. Finally, the experiments prove the effectiveness of KCF-Net. The length, width, and depth inversion MAEs of measured signals reach 2.2676, 1.6185, and 0.5664, respectively.

This article studies a preassigned time adaptive tracking control problem for stochastic multiagent systems (MASs) with deferred full state constraints and deferred prescribed performance. A modified nonlinear mapping is designed, which incorporates a class of shift functions, to eliminate the constraints on the initial value conditions. By virtue of this nonlinear mapping, the feasibility conditions of the full state constraints for stochastic MASs can also be circumvented. In addition, the Lyapunov function codesigned by the shift function and the fixed-time prescribed performance function is constructed. The unknown nonlinear terms of the converted systems are handled based on the approximation property of the neural networks. Furthermore, a preassigned time adaptive tracking controller is established, which can achieve deferred prescribed performance for stochastic MASs that provide only local information. Finally, a numerical example is given to demonstrate the effectiveness of the proposed scheme.

In this article, an integral reinforcement learning (IRL)-based event-triggered guarantee cost control (GCC) approach is proposed for stochastic systems which are modulated by randomly time-varying parameters. First, with the aid of the RL algorithm, the optimal GCC (OGCC) problem is converted into an optimal zero-sum game by solving a modified Hamilton-Jacobin-Isaac (HJI) equation of the auxiliary system. Moreover, in order to address the stochastic zero-sum game, we propose an on-policy IRL-based control approach involved by the multivariate probabilistic collocation method (MPCM), which can accurately predict the mean value of uncertain functions with randomly time-varying parameters. Furthermore, a novel GCC method, which combines the explorized IRL algorithm and MPCM, is designed to relax the restriction of knowing the system dynamics for the class of stochastic systems. On this foundation, for the purpose of reducing computation cost and avoiding the waste of resources, we propose an event-triggered GCC approach involved with explorized IRL and MPCM by utilizing critic-actor-disturbance neural networks (NNs). Meanwhile, the weight vectors of three NNs are updated simultaneously and aperiodically according to the designed triggering condition. The ultimate boundedness (UB) properties of the controlled systems have been proved by means of the Lyapunov theorem. Finally, the effectiveness of the developed GCC algorithms is illustrated via two simulation examples.

This article mainly proposes an evolutionary algorithm and its first application to develop therapeutic strategies for ecological evolutionary dynamics systems (EEDS), obtaining the balance between tumor cells and immune cells by rationally arranging chemotherapeutic drugs and immune drugs. First, an EEDS nonlinear kinetic model is constructed to describe the relationship between tumor cells, immune cells, dose, and drug concentration. Second, the N-level hierarchy optimization (NLHO) algorithm is designed and compared with five algorithms on 20 benchmark functions, which proves the feasibility and effectiveness of NLHO. Finally, we apply NLHO into EEDS to give a dynamic adaptive optimal control policy and develop therapeutic strategies to reduce tumor cells, while minimizing the harm of chemotherapy drugs and immune drugs to the human body. The experimental results prove the validity of the research method.

Automatic defect detection plays an important role in industrial production. Deep learning-based defect detection methods have achieved promising results. However, there are still two challenges in the current defect detection methods: 1) high-precision detection of weak defects is limited and 2) it is difficult for current defect detection methods to achieve satisfactory results dealing with strong background noise. This article proposes a dynamic weights-based wavelet attention neural network (DWWA-Net) to address these issues, which can enhance the feature representation of defects and simultaneously denoise the image, thereby improving the detection accuracy of weak defects and defects under strong background noise. First, wavelet neural networks and dynamic wavelet convolution networks (DWCNets) are presented, which can effectively filter background noise and improve model convergence. Second, a multiview attention module is designed, which can direct the network attention toward potential targets, thereby guaranteeing the accuracy for detecting weak defects. Finally, a feature feedback module is proposed, which can enhance the feature information of defects to further improve the weak defect detection accuracy. The DWWA-Net can be used for defect detection in multiple industrial fields. Experiment results illustrate that the proposed method outperforms the state-of-the-art methods (mean precision: GC10-DET: 6.0%; NEU: 4.3%). The code is made in https://github.com/781458112/DWWA.

This article researches the sliding mode control (SMC) for fuzzy fractional-order multiagent system (FOMAS) subject to time-varying delays over directed networks based on reinforcement learning (RL),$\alpha\in(0,1).$First, since there is information communication between an agent and another agent, a new distributed control policy$\xi_{i}(t)$is introduced so that the sharing of signals is implemented through RL, whose propose is to minimize the error variables with learning. Then, different from the existed papers studying normal fuzzy MASs, a new stability basis of fuzzy FOMASs with time-varying delay terms is presented to guarantee that the states of each agent eventually converge to the smallest possible domain of$0$using Lyapunov–Krasovskii functionals, free weight matrix, and linear matrix inequality (LMI). Furthermore, in order to provide appropriate parameters for SMC, the RL algorithm is combined with SMC strategy, and the constraints on the initial conditions of the control input$u_i(t)$are eliminated, so that the sliding motion satisfy the reachable condition within a finite time. Finally, to illustrate that the proposed protocol is valid, the results of the simulation and numerical examples are presented.

Leader-follower consensus problem for multiagent systems (MASs) is an important research hotspot. However, the existing methods take the leader system matrix as a priori knowledge for each agent to design the controller and use the leader's state information. In fact, only the output information may be available in some practical applications. On this basis, this article first designs a novel adaptive distributed dynamic event-triggered observer for each follower to learn the minimum polynomial coefficients of the leader system matrix instead of the leader system matrix. The proposed method is scalable and suitable for large-scale MASs and can reduce the information transmission dimension in observer design. Then, an adaptive dynamic event-triggered compensator based on the observer and leader output information is designed for each follower, thereby solving the leader-follower consensus problem. Finally, several simulation examples are given to verify the effectiveness of the proposed scheme.

This article investigates the position and attitude trajectory tracking control problem of quadrotor unmanned aerial vehicle (UAV) formation. A distributed optimal control protocol is proposed for UAVs formation to cooperatively complete the tracking tasks under unknown external disturbances. First, the concrete UAV dynamic model is established, and the UAV position and attitude loop controllers are designed based on detailed analysis of the UAV system. Second, in order to overcome the different types of external disturbances in the UAV flight process, the disturbance observer and disturbance suppression strategy are introduced. Then, the linear quadratic regulator (LQR) performance index is introduced to design the optimal control protocol of the attitude loop system such that the UAVs can successfully follow the target trajectory in a specific formation. Compared with the traditional consistent UAVs formation tracking strategy, the designed controllers involved in this article are characterized by low structural and computational complexity, which facilitates faster decision-making according to changes in the actual flight situation of UAVs. Finally, the effectiveness of the distributed optimal tracking control scheme based on the LQR algorithm is verified by a simulation example.

In order to suppress the swing of slung load during the flight of the quadrotor unmanned aerial vehicle (UAV), a new double closed-loop nonlinear control method Newton–Euler is proposed based on an energy function and the LaSalle’s invariance theorem. First, the forces of each part in the quadrotor-slung load system (QSLS) is analyzed separately, and the dynamics model of the QSLS is established by the Newton–Euler method. Then, an energy function is constructed based on the relative motion relationship between the UAV and the slung load, and a nonlinear position controller is designed based on the energy function to enable the UAV to suppress the swing angle of the slung load while moving to the desired position in three-dimensional space. The equilibrium point of the whole system is proved to be asymptotically stabilized by the Lyapunov technique and LaSalle’s invariance theorem. Finally, some comparisons are made with a nonlinear adaptive controller and the linear quadratic regulator (LQR) to verify the effectiveness and superiority of the controller proposed in this article. It is shown that for the QSLS, the proposed controller has faster convergence speed and better transient performance during the anti-swing control.

The problem of limited samples with missing information is an open challenge in data-driven fault diagnosis. Existing work has limited application in this field, since the reconstructed missing samples participating in sample generation may hurt the quality of the generated samples. To address this issue, the joint modeling of sample reconstruction and sample generation is proposed. First, the differentiated evaluation and reconstruction strategies are designed, which make reconstructed samples more reasonable and realistic, so that they can be employed to participate in sample generation. Second, the adaptive fusion mechanism is presented to introduce the knowledge of actual fault samples into the laboratory simulation samples, by which the quality and diversity of generated samples are guaranteed. By doing so, limited samples with missing information are enhanced to enable reliable fault diagnosis modeling. The proposed method is applied to the actual industrial process and benchmark simulated process. The experimental results highlight the superiority of the proposed method.

This article addresses the problem of prescribed-time optimal consensus control for a class of uncertain nonlinear multiagent systems (MASs) subject to denial-of-service (DoS) attacks over resilient event-triggered communication (RETC). First, a novel performance index function is devised to balance the convergence of consensus error and the cost of control energy, thereby achieving the optimal consensus with the preassigned steady-state precision and convergence time. Subsequently, a set of intermediate variables is established within the framework of reinforcement learning (RL)-based optimized backstepping to solve the problem of unknown control gains, which facilitates the implementation of optimal controllers. Furthermore, the designed actual optimal control law is transmitted through RETC, which provides two advanced advantages: 1) a resilient switching control protocol to avoid the impact of DoS attacks and 2) a flexible switching threshold event-triggered mechanism to conserve communication resources and balance system performance. Finally, the effectiveness of the presented approach is verified by a simulation example.

This article focuses on the event-triggered consensus control (ETCC) issue of the time-varying delayed leader-following nonlinear multiagent systems (TVDLFNMASs). In order to minimize the influence on uncertain factors of the information transmission and the data information loss, the switching topologies are constructed as the generally uncertain Markovian jumping forms whose transition rates include completely unknown elements and estimate values of uncertain elements. In addition, the event-triggered (ET) transmission strategy is given based on the threshold parameter and the ET matrix to relieve the communication burden of TVDLFNMASs. The new leader-following (LF) consensus conditions and control gains are obtained based on ET strategy. Finally, the effectiveness of the ET consensus criteria is demonstrated in the simulation section.

Random nonlinear systems (RNSs) are a class of nondeterministic systems that are distinguished from the intensively studied stochastic nonlinear systems (SNSs). The research on RNSs is becoming more and more popular thanks to its practical significance. To economize communication resources and alleviate network burden, a model-based event-triggered control (MBETC) scheme is applied to a family of RNSs in a strict-feedback fashion. With the help of the adaptive command filtering design technique, all signals of the closed-loop system are rendered ultimately bounded in the mean sense, and the notorious Zeno behavior is circumvented to appear. A simulation example is taken to manifest the validity of the main results.

This article investigates the distributed constrained optimization problem with event-triggered communication over time-varying weight-unbalanced directed graphs. A more generalized network model is considered where the communication topology may be variable and unbalanced over time, the information flows across agents are subject to time-varying communication delays, and agents are not required to know their out-degree information accurately. To address the above challenges, we propose a novel discrete-time distributed event-triggered delay subgradient algorithm. To facilitate convergence analysis, a consensus-only “virtual” agent technique is employed, dynamically adjusting its state (active or asleep) to ensure a delay-free information flow among agents. Additionally, an augmentation approach is proposed to ensure that the augmented time-varying weight matrix is row-stochastic. It is shown that the agents’ local decision variables converge to the same optimal solution, in the case of reasonable communication delays and event-triggering thresholds. Numerical examples show the efficiency of the proposed algorithm.

In order to solve the two-player Stackelberg differential game (SDG) for the continuous-time nonlinear Markov jump system (MJS), this article defines a unique$Q$-function and suggests a novel adaptive dynamic programming (ADP) method which is completely independent of system information. First, the optimal policies for the leader and follower are determined from down to the top, and it is further demonstrated that these policies are what make up the Stackelberg–Nash equilibrium point. Then, a novel action-dependent$Q$-function is established in order to attain completely model-free learning, which is the first attempt for SDG-based nonlinear MJS. Furthermore, the Lyapunov direct approach is employed to guarantee the stability of the closed-loop uncertain nonlinear MJS under the control scheme based on ADP, ensuring uniform ultimate boundedness (UUB). Ultimately, a numerical simulation is presented to validate the efficacy of the aforementioned ADP-based control approach.

In this article, the critical value problem of a class of model-free nonlinear surfaces with strong constraints is studied, and a$\boldsymbol {\gamma }$-iterative dual heuristic dynamic programming algorithm is proposed. Considering the high coupling of nonlinear surface and the algorithm structure of traditional dual heuristic dynamic programming, this article directly uses neural network to reconstruct the gradient relationship between adjacent strips of the Janbu segmentation method, so as to reduce the superdimensional calculation of partial derivatives still needed by first fitting the model. The critical value of the strongly constrained nonlinear surface is transformed into the optimal control law for solving the input-constrained nonquadratic HJB equation with discount factor$\boldsymbol {\gamma }$and the cofunction is defined to avoid the need to calculate the integral term in the repeated iterative process. Finally, the simulation results show that the$\boldsymbol {\gamma }$-iterative dual heuristic dynamic programming algorithm is effective in solving the critical sliding surface of a kind of rock and soil mass.

This article investigates the bipartite time-varying output formation tracking problem of heterogeneous linear multiagent systems with disturbances by adaptive dynamic event-triggered control. The goal is to make the outputs of all followers complete the preset formation configuration, and track the convex combination composed of the leaders’ outputs at the same time. Distributed dynamic event-triggered compensator is first designed to estimate the convex combination of the leaders’ states, where the internal variable is introduced into the dynamic event-triggered mechanism to dynamically adjust the threshold of each follower. Compared with the traditional static threshold, the time-varying threshold ensures larger adjacent triggering time intervals. Then, the output formation control law based on distributed compensator, compensation input and time-varying formation signal is proposed, where the compensation input provides a degree of freedom for the feasible formation set. Finally, two examples are introduced to verify the main results.

This article investigates the optimal control problem for the discrete-time (DT) nonlinear semi-Markovian jump systems (s-MJSs) that possess unknown dynamics. The study uses the semi-Markovian kernel approach to address the problem of mode-switching in these systems. This approach employs the transition probability and the sojourn-time distribution function to jointly determine the transitions between different modes. Then, with a neural network (NN) identifier, the demand for accurate information on the system dynamics is eliminated, and an optimal control method for the nonlinear s-MJSs is utilized to solve the Hamilton-Jacobi–Bellman equation (HJBE) built upon adaptive dynamic programming methodology. Additionally, a detailed analysis of the convergence of a value iteration-based algorithm, which solves the optimal control issue for the DT s-MJSs, is thoroughly discussed. Furthermore, an actor-critic NN is trained to attain an estimated solution to the relevant HJBE. Finally, to validate the designed approach, two simulations are performed to prove its effectiveness.

With renewable energy becoming more and more important, the heat and electricity integrated energy system (HE-IES) has been widely used because of its potential benefit in accommodating renewable energy. This work for the first time formulated a dynamic optimal energy flow (OEF) problem for the HE-IES with hybrid AC/DC sources. Firstly, to effectively and accurately assess the operation state of district heating network (DHN), the temperature dynamics are focused and solved with a set of difference equations where an adaptive discretization method is proposed. The method based on the delay characteristics of pipelines is proposed to select temporal and spatial steps for reasonable approximation of DHN, and to achieve satisfactory accuracy results with a lower computational burden. Secondly, a two-stage OEF model for HE-IES with new linear approximations is developed. In this model, a detailed converter station model coupled AC grid and DC grid is investigated, and a linearization constraint method is proposed to handle the highly nonlinear of converter station by adding extended branches to the AC grid. Finally, simulations demonstrate that the effectiveness of the proposed model, which can provide more intact information of state variables for optimal planning.Note to Practitioners—This work is motivated by the demand for optimal energy management in heat and electricity integrated energy system (HE-IES). On the one hand, the slow dynamic characteristic of the district heating network (DHN) is crucial for calculating the optimal results. On the other hand, the improvement of electronic technology has diversified energy supplies and enriched the way of using electricity, such as converter-based generators for electric power grid (EPG) and converter-based boilers for DHN. Therefore, the model of HE-IES exhibits highly nonlinearity, which brings huge challenges to optimal energy flow analysis, especially for the real-time OEF analysis with state information updated every 15–30 min. Existing studies focus on computing optimal energy flow without considering the slow dynamic characteristic of DHN or the hybrid AC/DC scenarios brought by the converter-based sources. This paper presents an optimal energy flow problem that integrates the slow dynamic characteristic and the converter-based sources to retain the complete state information of the system as much as possible in the optimization process. The work is used on an integrated IEEE-33 bus and Denmark-61 node system to show the effectiveness.

This technical note discusses the design process of dynamic event-triggered control (DETC) with the mixed$H_{2}/H_\infty $for partially unknown nonlinear stochastic systems. The purpose of this problem is to design a controller to make the closed-loop system achieve the expected$H_{2}$performance under the condition that the$H_\infty $continuous attenuation level is protected. Firstly, a two-player non-zero-sum game for stochastic system is given. Then we prove the optimal control strategy and the worst interference, which constitute the Nash equilibrium solution and can be derived from the corresponding Hamiltonian functions. Furthermore, two neural networks (NNs) are used to realize Nash equilibrium. Under the condition of dynamic event-triggered mechanism (DETM), the control strategy only is updated at the trigger moment. In addition, the stability and weights convergence of the system are proved mathematically. Finally, two numerical example are given to prove it.Note to Practitioners—In the practice of control engineering, mixed$H_{2}/H_\infty $control can not only evaluate the$H_{2}$performance index of the transient behavior of the system, but also measure the$H_\infty $performance index of the system’s robustness to external disturbances and parameter uncertainty. And many useful signals and interference vary randomly. Therefore, the optimal control of stochastic systems and dynamics play an important role in the modern industry. Saving control resources is very important for actual production. Therefore DETC is considered in this paper. On the other hand, in practice, accurate system models are difficult to obtain. In order to tackle this difficulty, by designing a novel scheme via integral reinforcement learning technique, the system relaxes the requirement of drift dynamic and Zeno behavior is avoided.

In this paper, the problem of secondary voltage restoration in an islanded microgrid (MG) is considered, in which the actuator of the distributed generators (DGs) may coexist with partial loss of effectiveness (PLOE) fault and bias fault. For each DG, an adaptive event-triggered fault-tolerant (ETFT) control protocol is designed to compensate for the effects of actuator faults in the DGs, thereby restoring the voltage to the reference value. The dependent event triggering mechanism saves the processor’s computational resources. A desirable feature of the protocol proposed in this paper is that the control protocol relies only on relative information between neighboring DG’s, independent of global information about the network graph, fault boundaries, and network scale. It means that the protocol is implemented in a fully distributed framework. The protocol fully applies to the common ETFT consensus control problem of linear multiagent systems (MASs) with actuator faults. Furthermore, comprehensive theoretical arguments for consensus stability and analysis of Zeno behavior ensure the approach’s feasibility. The simulation results verify the effectiveness of the algorithm.Note to Practitioners—This paper aims to propose a fully ETFT control protocol for secondary voltage restoration of an islanded MG. The control protocol consists of a linear term and a nonlinear time that compensates for the multiplicative and additive fault of the actuator. Moreover, DGs’ communication structure and global fault boundaries may be unknown in practice. Hence, adaptive coupling gains that depend only on the sampled relative information of DGs are introduced to estimate the controller gain, thus avoiding global information. A feasible strategy is provided for industrial applications.

This article focuses on the bipartite output tracking control for heterogeneous linear multiagent systems under the asynchronous edge-based event-triggered transmission mechanism. First, the distributed bipartite edge-based event-triggered compensator is established to estimate the state of the exosystem. The estimated state of the compensator is the same as the state of the exosystem in modulus and opposite in sign because of the existence of antagonistic communications. To be independent of the topology information, the adaptive compensator with an edge-based event-triggered mechanism is then established. And the observer is proposed to recover the unmeasurable system states. Then, the distributed control scheme based on the compensator and the observer is designed to address the bipartite output tracking problem. Moreover, the results in the signed fixed graph are extended to signed switching graphs. The Zeno behavior of each edge is ruled out. Finally, two numerical examples, one application example and one comparison example, are given to demonstrate the feasibility of the main theoretical findings.

The finite-time output consensus (FTOC) issue of heterogeneous fractional-order multiagent systems (HFO-MASs) is investigated in this article. First, a new principle of finite-time convergence for absolutely continuous functions is developed if a fractional derivative inequality is satisfied. Next, in order to remove the assumption that the leader's system matrix is known to all agents in previous studies, a distributed adaptive finite-time observer is designed, which can estimate not only the leader's state but also the leader's system matrix. Then, a novel finite-time event-triggered compensator with intermittent communication is constructed to estimate the leader's state by introducing a dynamic threshold for a novel triggering function. In this case, the high frequency triggering is restrained and the triggering number is significantly reduced. The Zeno behavior does not exist by choosing parameters appropriately. In addition, two finite-time control strategies are constructed based on the above distributed observer and event-triggered compensator, respectively, to achieve output consensus in finite time. The feasibility of the proposed method is ensured by the comprehensive theoretical demonstration of the finite-time consensus stability and the analysis of the Zeno behavior. Finally, the examples are given to demonstrate the conclusion.

This article focuses on the distributed robust fault estimation problem for a kind of discrete-time interconnected systems with input and output disturbances. For each subsystem, by letting the fault as a special state, an augmented system is constructed. Particularly, the dimensions of system matrices after augmentation are lower than some existing related results, which may help to reduce calculation amount, especially, for linear matrix inequality-based conditions. Then, a distributed fault estimation observer design scheme that utilizes the associated information among subsystems is presented to not only reconstruct faults, but also suppress disturbances in the sense of robust$H_{\infty }$optimization. Besides, to improve the fault estimation performance, a common Lyapunov matrix-based multiconstrained design method is first given to solve the observer gain, which is further extended to the different Lyapunov matrices-based multiconstrained calculation method. Thus, the conservatism is reduced. Finally, simulation experiments are shown to verify the validity of our distributed fault estimation scheme.

Small sample size leads to low accuracy and poor generalization of industrial fault diagnosis modeling. Domain adaptation (DA) attempts to enhance small samples by transferring samples in other similar domains, but it has limited application in industrial fault diagnosis, since the differences in working conditions lead to large variations of fault samples. To address the above issues, this article proposes a heterogeneous sample enhancement network with lifelong learning (HSELL-Net). First, a heterogeneous DA subnet (HDA-subnet) is presented, in which the designed heterogeneous supporting domain ensures dimension alignment and the designed distribution jointly matching improves the performance of distribution matching; thus, fault samples from other working conditions can be employed to reliably enhance small samples. Second, a lifelong learning subnet (LL-subnet) is designed, in which the proposed Admixup and shared knowledge repository enable incremental samples to further enhance small samples without retraining the network. The two subnets are mutually embedded and reinforced to enhance the number and types of small samples; thus, the accuracy and generalization of fault diagnosis under industrial small samples are improved. Finally, benchmark simulated experiments and real-world application experiments are conducted to evaluate the proposed method. Experimental results show the HSELL-Net outperforms the existing works under industrial small samples.

This article considers the time-varying formation (TVF) tracking issue of heterogeneous multiagent systems (HMASs) with the dynamic event-triggered control. The HMASs contain heterogeneous multiple leaders, all of which have the input signals to generate flexible reference, and only the output information can be measured. All leaders do not have access to the same followers, that is, the well-informed follower assumption is removed in this article. In this setting, the adaptive multileader state compensator is designed for each follower to estimate the integrated state information of all leaders, which can equip with two kinds of dynamic event-triggered mechanisms, that is, node-based event-triggered mechanism and edge-based event-triggered mechanism, to save communication bandwidth. Then, the TVF controllers are built by some estimation values to regulate the followers to achieve and maintain the geometric shape while tracking the reference which is the convex combination of outputs of leaders. The event-triggered compensator and TVF controller constitute the control protocol of HMASs, which are independent of global information with the fully distributed manner. The stability analysis and numerical simulations are given to verify the presented control protocol.

This article investigates optimal regulation scheme between tumor and immune cells based on the adaptive dynamic programming (ADP) approach. The therapeutic goal is to inhibit the growth of tumor cells to allowable injury degree and maximize the number of immune cells in the meantime. The reliable controller is derived through the ADP approach to make the number of cells achieve the specific ideal states. First, the main objective is to weaken the negative effect caused by chemotherapy and immunotherapy, which means that the minimal dose of chemotherapeutic and immunotherapeutic drugs can be operational in the treatment process. Second, according to the nonlinear dynamical mathematical model of tumor cells, chemotherapy and immunotherapeutic drugs can act as powerful regulatory measures, which is a closed-loop control behavior. Finally, states of the system and critic weight errors are proved to be ultimately uniformly bounded with the appropriate optimization control strategy and the simulation results are shown to demonstrate the effectiveness of the cybernetics methodology.

This article investigates the event-driven fault-tolerance (ETFT) consensus problem for general linear multiagent systems (MASs) with partial loss of effectiveness (PLOE) and bias faults of actuators in leader-follower networks. Each agent's controller is only updated relatively infrequently at its event moments. A desirable feature of this article is that the proposed event-driven algorithm is fully distributed also independent of global information and additive fault boundaries. Based on this, a consensus error prediction model is used to avoid the limitation that each agent needs to monitor its neighbors' state under event-driven conditions continuously. We further excluded the Zeno behavior by proving that any adjacent event interval for each agent is greater than zero. The simulations verify our results.

This article investigates the optimal control strategy problem for nonzero-sum games of the immune system based on adaptive dynamic programming (ADP). First, the main objective is approximating a Nash equilibrium between the tumor cells and the immune cell population, which is governed through chemotherapy drugs and immunoagents guided by the mathematical growth model of the tumor cells. Second, a novel intelligent nonzero-sum games-based ADP is put forward to solve the optimization control problem by reducing the growth rate of tumor cells and minimizing chemotherapy drugs and immunotherapy drugs. Meanwhile, the convergence analysis and iterative ADP algorithm are specified to prove feasibility. Finally, simulation examples are listed to account for availability and effectiveness of the research methodology.

The cooperative output regulation (COR) problem of heterogeneous linear multiagent systems under digraphs has been considered under the assumption that continuous communication between neighbors and continuous update of controllers. To get rid of this assumption, that is, to realize event-triggered communication between neighbors and discrete update of controllers, this article proposes the fully distributed event-triggered observers to estimate the matrix and the state for the exosystem, and the event-triggered controllers to solve the COR problem. Moreover, the Zeno behavior is excluded by proving that the interevent times of each agent are strictly greater than zero under the design triggering conditions. Finally, two examples are given to verify the effectiveness and advantages of the proposed methods.

This article focuses on the dissipativity-based consensus tracking control (DBCTC) problems of time-varying delayed leader-following nonlinear multiagent systems (LFNMASs) with the event-triggered transmission strategy. The switching topologies of the LFNMASs are subject to the uncertain and partially unknown generally Markovian jumping process. The control inputs of the following agents are updated according to the proposed event-triggered transmission strategy, which could reduce the communication burden. Based on the event-triggered transmission condition and distributed consensus protocol, some dissipativity-based criteria obtained by adopting the delay-product-term Lyapunov-Krasovskii functional (DPTLKF) and higher order polynomial-based relaxed inequality (HOPRII) are proposed to guarantee the LFNMAS consensus. The validity of the main results is verified by two simulation examples.

This article proposes an observer-based event-driven fault-tolerant (OBEDFT) secondary control strategy for AC microgrids (MGs) to achieve load voltage regulation. First, the input-output feedback linearization method transforms the voltage regulation issue into an output feedback tracking problem for linear multiagent systems (MASs) with nonlinear dynamics. This transformation provides the necessary preprocessing for load voltage regulation. Then, an OBEDFT secondary control protocol that considers full-state immeasurability is proposed. The actuators of distributed generators (DGs) may experience partial loss of effectiveness (PLOE) and bias faults, and these fault parameters may be heterogeneous and time-varying. The protocol introduces adaptive techniques to avoid information related to fault parameters while using event-driven mechanisms to achieve discrete measurements of neighboring DG. Additionally, the protocol uses boundary layers to construct smooth controllers that prevent the chattering effect caused by nonsmooth controllers. Finally, simulation results confirm the effectiveness of this load voltage regulation strategy.

The time-varying formation (TVF) tracking problem is studied for linear multiagent systems (MASs), where followers reach a preset TVF when tracking the leader's state. Followers are divided into the informed ones, which directly receive the leader's information, and uninformed ones. To alleviate communication requirements, trigger mechanisms are designed for the leader and all edges. Note that the designed trigger mechanisms enable the leader to send information intermittently and each follower to transmit information asynchronously when the corresponding trigger mechanism is satisfied. To address the TVF tracking problem, the node-event (for the leader) and (dynamic) edge-event triggered adaptive control strategy is proposed, which is fully distributed and has no relation to the system network's scale. Moreover, the MASs do not exhibit the Zeno behavior. Finally, a practice example is introduced to effectively illustrate the theoretical results.

This article investigates the distributed fuzzy adaptive containment control problem of stochastic nonlinear multiagent systems under a directed communication topology suffering denial-of-service (DoS) attacks. First, considering unknown stochastic disturbance and nonlinear characteristics for the followers, the mathematical models are modeled as It$\hat{o}$stochastic nonlinearity terms approximated through fuzzy logic systems. Second, the proposed dynamically adjusted event-triggered condition can effectively avoid inefficient transmission behavior. Moreover, an adaptive compensation protocol for input saturation is constructed to eliminate the effects of nonlinearities caused by input saturation. Finally, instead of the previous uniformly ultimately bounded containment control results, stability and asymptotic performance are guaranteed through valid reasonable adaptive control laws acquired through the backstepping control approach despite suffering DoS cyberattacks. Moreover, a simulation is given to verify the feasibility of the proposed method.

This article presents a novel finite-time adaptive fuzzy event-triggered control strategy for stochastic nonlinear systems in nonstrict feedback structure, where time-varying output constraints and input delay are considered. To deal with the output constraints, a stochastic nonlinear mapping is developed to transform the original system into an equivalent one without output constraints. Furthermore, the Pade approximation method is applied to address the input delay by introducing a new intermediate variable. Then, a novel adaptive event-triggered mechanism is proposed to reduce the number of data transmissions and mitigate the effects of tracking error. The stability analysis shows that the closed-loop signals are semiglobal finite-time stable in probability, while the tracking performances are well and satisfy the output constraint condition. Finally, simulation results demonstrate the effectiveness of the developed approach.

In this article, the leaderless adaptive fuzzy consensus control problem is studied for a class of stochastic nonlinear multiagent systems with unknown measurement sensitivity under false data injection attacks. Unknown measurement sensitivity and false data injection attacks can prevent sensors from obtaining right state information and make it difficult to design controllers and adaptive laws. The existing works considered only one of these cases for deterministic systems. In this article, the coexistence of both cases is considered with the help of Nussbaum functions and fuzzy logic systems, and the corresponding controllers and auxiliary variables are not only codesigned to solve the problem, but also extended to stochastic multiagent systems. Then, an improved switching threshold event-triggered mechanism is proposed to reduce the communication burden of the control channel. Furthermore, the leaderless asymptotic consensus control scheme for stochastic multiagent systems is proposed. The boundedness of all signals and leaderless asymptotic consensus control performance are guaranteed via the Lyapunov stability theorem. Finally, two simulation examples are given to verify the effectiveness of the proposed control scheme.

This article investigates the bipartite time-varying output formation tracking issue for heterogeneous linear multiagent systems with multiple leaders under signed digraph. Different from previous related works, the leaders are considered to be heterogeneous, which greatly increases the difficulty of designing distributed control strategies. To address this issue, we develop a novel fully distributed dynamic event-triggered formation tracking control strategy and the Zeno behavior is ruled out. Compared with previous relevant results, in addition to addressing a more meaningful new issue, the developed control strategy also has the following advantages: 1) the control strategy gets rid of this assumption that each given follower must be well informed or uninformed; 2) the adaptive gains do not increase unboundedly in the presence of external disturbances; 3) the interevent time is larger. Moreover, a self-triggered realization based on sampled information is also formulated for the developed control strategy. Finally, the theoretical result is demonstrated by a simulation example.

Class-imbalanced weld defect recognition, which realizes defect recognition via learning features of class-imbalanced X-ray images, is an emerging but challenging task. Nevertheless, the existing studies on the class-imbalanced problem mainly focus on large-scale data, and it is difficult to extract high-quality features from the insufficient industrial data, resulting in weak recognition performance. To address the above issue, this article comprehensively learns features from the perspective of basic-class and cross-class, on this basis, a novel hybrid feature learning model for class-imbalanced weld defect recognition is proposed. First, an image acquisition method completed by photographing, scanning, and sampling is designed to collect the class-imbalanced X-ray images. Second, a hybrid feature learning model is proposed to learn the distinctive and effective features from acquired images, so that the class-imbalanced data are mapped to a balanced feature distribution. Third, with the distinguishable features learned by our hybrid feature learning model, an unbiased defect recognition model can be trained to recognize different types of defects. The practical weld data W-PPLN, W-MTL, and W-GDXray are adopted in the experiments, and the experimental results show that our method outperforms the state-of-the-art methods on the task of class-imbalanced weld defect recognition.

Deep learning methods have demonstrated promising performance in magnetic flux leakage (MFL) defect detection under adequate amounts of labeled samples. However, in industrial occasions, obtaining adequate amounts of labeled samples is time-consuming and expensive, and applying only limited labeled samples can lead to unsatisfactory defect detection accuracy. To address the above issues, a defect detection method named semisupervised circular teacher network (SSCT-Net) is proposed in this article. First, a parallel feature extraction network with hybrid attention is proposed in SSCT-Net so that the useful features of multiview MFL signals can be extracted simultaneously. Second, semisupervised circular learning is proposed for the first time. In semisupervised circular learning, a distinguishable feature embedding space is constructed, and two structurally identical deep networks cosupervise and collaborate through the proposed consistent circular strategy so that the decision bias of unlabeled samples can be reduced. Finally, the trained model is applied for defect detection in practice. The proposed method can establish a potential connection between multiview MFL signals and fully utilize labeled and unlabeled MFL signals. The experiments in simulations and real-world applications demonstrate that SSCT-Net can reach 92% detection accuracy with only 20% labeled samples, which is more effective than the state-of-the-art methods and leads to a promising practical utility of the proposed method.

This study investigates the adaptive bipartite event-triggered time-varying output formation tracking for heterogeneous linear multi-agent systems (MASs) under signed directed communication topology. Both cooperative communication and antagonistic communication among agents are considered. The fully distributed bipartite compensator based on the novel composite event-triggered transmission mechanism is first put forward to estimate the state of the leader. Compared with the existing methods, our compensator can save communication resources using event-triggered transmission mechanism; is independent of the global information of the network graph; and is applicable for the signed directed graph. With the developed compensator, the distributed control protocol is designed to achieve the time-varying output formation tracking. Moreover, the case that the networked systems subject to external disturbances is also considered. To estimate the state of leader with disturbance, the fully distributed bipartite compensator based on an innovative composite event-triggered mechanism is presented. And the novel distributed control protocol is proposed to address the output formation tracking issue for linear MASs with heterogeneous dynamics and external disturbances. It is shown that the Zeno-behavior can be excluded in both transmission mechanisms. Finally, the effectiveness of the developed control methods is illustrated through three simulation examples.

In traditional leak location methods, the position of the leak point is located through the time difference of pressure change points of both ends of the pipeline. The inaccurate estimation of pressure change points leads to the wrong leak location result. To address it, adaptive dynamic programming is proposed to solve the pipeline leak location problem in this article. First, a pipeline model is proposed to describe the pressure change along pipeline, which is utilized to reflect the iterative situation of the logarithmic form of pressure change. Then, under the Bellman optimality principle, a value iteration (VI) scheme is proposed to provide the optimal sequence of the nominal parameter and obtain the pipeline leak point. Furthermore, neural networks are built as the VI scheme structure to ensure the iterative performance of the proposed method. By transforming into the dynamic optimization problem, the proposed method adopts the estimation of the logarithmic form of pressure changes of both ends of the pipeline to locate the leak point, which avoids the wrong results caused by unclear pressure change points. Thus, it could be applied for real-time leak location of long-distance pipeline. Finally, the experiment cases are given to illustrate the effectiveness of the proposed method.

This article considers the bipartite time-varying output formation-containment tracking control issue for general linear heterogeneous multiagent systems with multiple nonautonomous leaders, where the full states of agents are not available. Both cooperative interaction and antagonistic interaction between neighboring agents are taken into account. First, an observer is constructed using the output information to observe the state information. Then, based on the information between neighboring agents, an independent asynchronous fully distributed event-triggered bipartite compensator is put forward to estimate the convex hull spanned by the states of multiple leaders. Note that the compensator does not require to use of any global information. Subsequently, a formation-containment tracking control strategy based on the observer and compensator and an algorithm to determine its control parameters are given. The Zeno behavior is further proved to be excluded in any finite time. In addition, a novel self-triggered control strategy based only on the sampled information at triggering instants is also formulated, which avoids continuous communication among agents. Finally, a numerical example is given to validate the effectiveness and performance of the proposed control strategies.

This article considers the consensus problem of unknown linear multiagent systems (MASs) through adaptive event-driven control in leader-follower and leaderless networks. The proposed event-driven algorithms do not involve any global information related to the network communication structure and rely only on local information exchange to achieve consensus on MASs and are therefore fully distributed. Furthermore, the constraint of continuous communication among the agents is eliminated in terms of control law updates and triggering state monitoring. Another desirable aspect of this article is that the design process of the control algorithms is independent of the parameters of each agent's dynamics and thus does not require precise information about the dynamics of MASs. We further exclude the Zeno behavior of each agent by proving the existence of a strict positive lower bound between any two adjacent events. Finally, the effectiveness of the proposed adaptive event-driven algorithms is verified by a simulation example.

This article focuses on the adaptive bipartite containment control problem for the nonaffine fractional-order multi-agent systems (FOMASs) with disturbances and completely unknown high-order dynamics. Different from the existing finite-time theory of fractional-order system, a lemma is developed that can be applied to actualize the aim of finite-time bipartite containment for the considered FOMASs, in which the settling time and convergence accuracy can be estimated. Via applying the mean-value theorem, the difficulty of the controller design generated by the nonaffine nonlinear term is overcome. A neural network (NN) is employed to approximate the ideal input signal instead of the unknown nonaffine function, then a distributed adaptive NN bipartite containment control for the FOMASs is developed under the backstepping structure. It can be proved that the bipartite containment error under the proposed control scheme can achieve finite-time convergence even though the follower agents are subjected to completely unknown dynamic and disturbances. Finally, the feasibility and validity of the obtained results are exhibited by the simulation examples.

In this article, a dynamic event-triggered stochastic adaptive dynamic programming (ADP)-based problem is investigated for nonlinear systems with a communication network. First, a novel condition of obtaining stochastic input-to-state stability (SISS) of discrete version is skillfully established. Then, the event-triggered control strategy is devised, and a near-optimal control policy is designed using an identifier-actor-critic neural networks (NNs) with an event-sampled state vector. Above all, an adaptive static event sampling condition is designed by using the Lyapunov technique to ensure ultimate boundedness (UB) for the closed-loop system. However, since the static event-triggered rule only depends on the current state, regardless of previous values, this article presents an explicit dynamic event-triggered rule. Furthermore, we prove that the lower bound of sampling interval for the proposed dynamic event-triggered control strategy is greater than one, which avoids the so-called triviality phenomenon. Finally, the effectiveness of the proposed near-optimal control pattern is verified by a simulation example.

This article is concerned with distributed resilient load frequency control (LFC) for multi-area power interconnection systems against jamming attacks. First, considering uncertainties and high dimension nonlinearity, the model-free adaptive control (MFAC) model is adopted for the power system, in which only input and output (I/O) data are used. Second, jamming attacks are modeled in a stochastic process, and a multistep predictive compensation algorithm is developed to mitigate the impact of jamming attacks. Then, the distributed MFAC protocol with predictive compensation algorithm is designed such that the frequency tracking errors under the predictive compensation algorithm of multi-area power interconnection systems converge consensually into a small neighborhood of origin in the mean square sense. Simulation results show the effectiveness of the approach.

In this article, the time-varying output formation issue of heterogeneous multiagent systems is investigated by the event-triggered control scheme. Only the outputs of all agents, including leader agent and follower agents, are measurable. The leader agent contains an unknown input signal to generate flexible reference trajectory. Also, only a subset of follower agents have the direct access to the leader agent. First, for each follower, the leader-state compensator is designed to estimate the state of leader. Two kinds of dynamic event-triggered (DET) mechanisms, i.e., node- and edge-based event-triggered schemes, can be equipped on the compensator to save the communication bandwidth of leader-follower and follower-follower interactions, respectively. Then, the distributed formation controller is built to drive each follower achieving formation tracking. The presented control protocol consisting of the DET state compensator and formation controller is fully distributed, which is independent of the global information of communication topology, such as the eigenvalues of Laplacian matrix of communication topology and amount of whole agents. Finally, the numerical experiments and comparison experiments are exhibited to verify the effectiveness of the presented control protocol.

The article investigates full information control problem for switched neural networks subject to fault and disturbance. First, the main objective is realizing interval stability and zero tracking error under condition that neither of the neuron states’ vectors including the plant and reference models is available. Second, the desired full information controller and neural networks’ observer are designed to ensure observer-based dynamic error system mean-square exponentially stable with sufficient condition of strict weight$\mathcal {H}_{\infty } /\mathcal {H}_{-}$performance levels. Finally, we concentrate on stability analyses and fault tolerance for switched neural networks with fault accompanied by disturbance through linear matrix inequalities (LMIs), Lyapunov function, and average dwell time, discussing it according to different values of fault. Finally, simulation examples are listed to account for the availability and effectiveness of the research methodology.

This article focuses on the synchronization problem of delayed inertial neural networks (INNs) with generally uncertain Markovian jumping and their applications in image encryption. The random connection weight strengths and generally uncertain Markovian are discussed in the INNs model. Compared with most existing INNs models that have constant connection weight strengths, our model is more practical because connection weight strengths of INNs may randomly vary due to the external and internal environment and human factor. The delay-range-dependent synchronization conditions (DRDSCs) could be obtained by adopting the delay-product-term Lyapunov-Krasovskii functional (DPTLKF) and higher order polynomial-based relaxed inequality (HOPRII). In addition, the desired controllers are obtained by solving a set of linear matrix inequalities. Finally, two examples are shown to demonstrate the effectiveness of the proposed results.

Considering that cooperative interactions and antagonistic interactions between neighboring agents may exist simultaneously in practice, this article studies the bipartite time-varying output formation tracking (BTVOFT) problems for homogeneous/heterogeneous multiagent systems with multiple nonautonomous leaders under switching communication networks. First, a full-dimensional observer-based nonsmooth distributed dynamic event-triggered (DDET) output feedback control scheme is proposed to ensure that BTVOFT is achieved, and the Zeno behavior is excluded. Note that the nonsmooth distributed control scheme requires global communication network information and may cause unexpected chattering effect, and the design cost of full-dimensional observer is relatively high. Thus, a reduced-dimensional observer-based continuous fully DDET scheme is proposed. Compared with the existing event-triggered schemes, the dynamic event-triggered scheme can ensure larger interevent times by introducing an additional internal dynamic variable. Finally, the effectiveness and performance of the theoretical results are validated by numerical simulations.

In this article, the neural network (NN)-based adaptive dynamic programming (ADP) event-triggered control method is presented to obtain the near-optimal control policy for the model-free finite-horizon H∞ optimal tracking control problem with constrained control input. First, using available input-output data, a data-driven model is established by a recurrent NN (RNN) to reconstruct the unknown system. Then, an augmented system with event-triggered mechanism is obtained by a tracking error system and a command generator. We present a novel event-triggering condition without Zeno behavior. On this basis, the relationship between event-triggered Hamilton-Jacobi-Isaacs (HJI) equation and time-triggered HJI equation is given in Theorem 3. Since the solution of the HJI equation is time-dependent for the augmented system, the time-dependent activation functions of NNs are considered. Moreover, an extra error is incorporated to satisfy the terminal constraints of cost function. This adaptive control pattern finds, in real time, approximations of the optimal value while also ensuring the uniform ultimate boundedness of the closed-loop system. Finally, the effectiveness of the proposed near-optimal control pattern is verified by two simulation examples.

This article focuses on the problem of adaptive bipartite output tracking for a class of heterogeneous linear multiagent systems (MASs) by asynchronous edge-event-triggered communications under jointly connected signed topologies. By designing the observers to estimate the states of followers and the dynamic compensators to estimate the states of zero input and nonzero input leader, respectively, the fully distributed edge-event-triggered control protocol is presented. Moreover, it is proven that the bipartite output tracking problem is implemented, and the systems do not exhibit Zeno behavior under a fully distributed control strategy with edge-event-triggered mechanisms. Compared with the existing works, one of the highlights of this article is the design of triggering mechanisms, under which the leader avoids continuous information transmission and any pair of followers that make up the edge asynchronously transmit information through the edge. The methods greatly avoid unnecessary information transmission in the systems. Finally, several simulation examples are introduced to demonstrate the theoretical results obtained in this article.

The time-varying output bipartite formation containment (TVOBFC) problem for linear multiagent systems (MASs) under directed graphs is an important problem. However, the methods in existing works rely on the global information of the MASs or do not use event-triggered communication. This article investigates two kinds of TVOBFC problems for heterogeneous linear MASs under signed digraphs by event-triggered communication. For the first case where leaders have the same dynamics, the innovative fully distributed event-triggered protocol for the follower is proposed. In this case, the followers form the preset formation shape. For the second case where leaders have different dynamics, the leaders are divided into two groups. One group can directly obtain the output information of the virtual leader, while the other group cannot. In order to make leaders achieve the formation shape and track the virtual leader, two kinds of innovative observers are designed for two kinds of leaders to estimate the state of the virtual leader, and the control protocol is designed for each leader based on the designed observers. Then, the control law for each follower is designed to solve the formation containment problem. Finally, two examples are introduced to illustrate the main results.

In this article, the fixed-time fuzzy adaptive tracking control problem is studied for a class of one-link manipulator systems with stochastic disturbances and multiple constraints. First, a modified dynamic surface control technique is proposed for the stochastic system, which provides a useful filtering solution to the fixed-time control of the stochastic nonlinear systems. Then, an extended stochastic fixed-time stability criterion is introduced to simplify the complex discussion process of stability analysis instead of existing fixed-time stability criteria. By using the unified barrier function, the constrained nonlinear system can be transformed into the nonconstrained nonlinear system. Moreover, a fuzzy adaptive controller is constructed with the funnel error transformation function, which improves the transient response of the system. The control objective of this article is that the steady-state error can be driven to the prespecified funnel in a fixed time based on the Lyapunov stability theorem and the extended stochastic fixed-time stability criterion. Finally, a simulation example is given to demonstrate the effectiveness of the proposed method.

This article has a research on the problem of output consensus for linear heterogeneous multiagent systems (LHMASs) by using dynamic event-based impulsive control. First, according to whether the transfer of information from different agents is continuous or not, two corresponding control approaches are provided, respectively. Second, for the case of continuous information transfer, the corresponding dynamic event-triggered condition is obtained. Third, for the case that the information communication from each agent only occurs at the event-triggered time instant, two different dynamic event-triggered conditions according to whether the event-triggered time instant for each agent is same or not are proposed, respectively. For all these event-triggered mechanisms, both the output consensus conditions and the proofs of avoiding Zeno-behavior are given. Moreover, the effect of the dynamic variables belong to these dynamic event-triggered conditions on the minimum event-triggered time interval is also discussed. Finally, the analysis for a numerical example with two control approaches and the corresponding event-triggered conditions provided in this article is obtained to support our opinions.

This economic dispatch problem has been tended to be solved by using distributed optimization algorithms which are easier to suffer from diversified cyberattacks, e.g., the denial-of-service (DoS) attacks. It leads to enormous secure risks for the economic operation of smart grid. To address this issue, this article aims to propose a distributed secure dispatch method to effectively defend the DoS attacks. First, considering the coexistence of the attack sequence and triggering sequence, the actual affected period and actual safe period are analyzed and defined. It provides an analysis model for the subsequent algorithm design. Then, by designing switched system dynamics along with hybrid-triggering concept, a novel distributed secure dispatch strategy is presented. The proposed method can enable each distributed generator (DG) to reasonably use estimation values and switched rate of system evolution to mitigate the effect of the DoS attacks. Meanwhile, contributed by the designed hybrid-triggering communication strategy, the proposed method takes advantages of reduced communication costs, flexible execution, and fast and reliable communication recuperation among DGs. With those efforts, the proposed method is capable of high robustness to resist the DoS attacks well. Moreover, theoretically analytic results are proposed to verify the correctness of the proposed method. Finally, simulation results are provided to show the feasibility and effectiveness of the proposed method.

For nonlinear systems, a novel adaptive dynamic programming (ADP) algorithm of self-triggered control (STC) strategy is proposed. This is a novel attempt to introduce self-triggering into the ADP algorithm. First, an identifier based on a generalized fuzzy hyperbolic model (GFHM) is established, which only uses input–output data to reconstruct the unknown system, thus reducing the requirements for system dynamics. Then, the critic neural network (NN) adjusts continuously, while actor NN updates the control strategy only at triggering instants. The event-triggered control (ETC) reduces the use of control resources and improves the anti-interference capability. However, it requires dedicated hardware to monitor whether triggering rules are violated, which is not feasible on most general-purpose devices. Hence, we propose a novel technique, which uses the current state of the device to determine the state measurement at the next moment, calculate the control law, and then abandon persistently monitoring of the plant. This technique is called STC. Finally, the closed-loop system is guaranteed to be ultimate uniform boundedness (UUBs). Furthermore, a simulation example is given.

In this article, the mixed$H_{2}/H_{\infty }$control problem with closed-loop Stackelberg games for nonlinear stochastic systems is studied. With the help of the value functions of leader and follower, the Stackelberg game problem is transformed into the numerical solution of Hamilton–Jacobi (HJ) equations with generalized Hamiltonian functions. On the premise of ensuring that the system state is uniformly ultimate bounded (UUB), an adaptive neural networks (NNs) algorithm is proposed to approach the closed-loop Stackelberg equilibrium. This is the first time that adaptive dynamic programming (ADP) is used to solve the closed-loop Stackelberg games control problem of nonlinear stochastic systems. Further, in order to reduce computational load and save communication resources, the event-triggering control strategy is obtained. Moreover, a novel stochastic comparison lemma is given, which is used to exclude Zeno behavior during the learning process. Finally, a simulation example is used to demonstrate the usefulness of the suggested near-optimal control scheme.

This article discusses the fault estimation (FE) problem of nonlinear fractional-order (FO) systems subject to faults and unknown inputs through the T–S fuzzy approach. A novel fuzzy FO unknown input observer is well synthesized to not only achieve the desired FE but also decouple the unknown input in contrast to the traditional integer-order observer design technique. It is worth pointing out that the provided FE scheme can reconstruct the fault signal appearing in both input and output equations simultaneously. Furthermore, by resorting to linear matrix inequalities, the stability criteria of observation error dynamics are first formulated based on general quadratic Lyapunov functions, which are further extended via fuzzy Lyapunov functions containing the information of membership functions. Finally, the performance of theoretical results is verified through two elaborate examples.

Though voltage-regulation and current-sharing of distributed generations (DGs) in dc-microgrids have been widely studied, additional communication links or independent modulation circuit should be added to achieve information transmission. To accomplish precise current-sharing/voltage-regulation without additional communication devices, this article proposes a communication-free intelligent edge control regarding voltage and current for dc-microgrids. First, the power-information dual modulation (PIDM) is designed to achieve information exchange among DGs and eliminate additional communication devices. Second, the cooperative control problem with two coupled targets, i.e., accurate voltage-regulation and current-sharing, is converted into a matter of optimal control. Therefore, the voltage-regulation and current-sharing could be solved concurrently. In addition, the control objective function of each DG is switched to provide the optimal controller and minimize the voltage/current control deviation, which is further switched to solve the Hamilton–Jacobi–Bellman (HJB) function. In order to solve this HJB function, which is difficult to obtain analytical solution, an intelligent edge control strategy with PIDM is proposed to solve the HJB function. Therefore, the precise voltage-regulation and current-sharing can be accomplished. Finally, the proposed control approach is verified through simulation results.

This article concentrates on the neural network (NN)-based adaptive sliding-mode control (SMC) for fuzzy fractional-order system (FOS),$\alpha \in (0,1)$. First of all, a novel method of optimal SMC approach is developed for fuzzy FOSs by using the adaptive dynamic program (ADP), integral sliding mode, and NN with unmatched disturbances and time-varying delays. Next, to weaken the influence of the nonlinearities, the SMC strategy is proposed for the specific system, which is established on the corresponding SMD to ensure that the FOS reach the SMS in a finite time. Moreover, it shows that the matrix of SMS can be described by the linear matrix inequality (LMI). Furthermore, the Hamilton–Jacobi–Bell man (HJB) equation can be approximated by a single NN method, and the Lyapunov stability principle proves that the weight errors are convergent, further guaranteeing the asymptotically stability of the fuzzy FOS. Finally, to display that the above-presented policy is effective, simulation results are presented.

A distributed control law synthesized several effective tools which called adaptive compensators solve a containment control problem. Distinguish from many studies on tracking, the situation where external system information is not available to network nodes is considered in a multileader system under a structurally balanced topology, the designed compensator is used to estimate the adaptive state of the convex hull to each follower. Considering the special case of external system information, a novel adaptive internal model compensator is proposed to dispose of the system robustness problem. Moreover, a novel Sylvester equation is given without considering all closed-loop system information and present an algorithm for calculating adaptive solutions of Sylvester equations based on containment control.

In the real-time status monitoring of pipeline network, incomplete pressure data are unavoidable due to some device or communication errors. To solve this problem, a hierarchical data recovery method based on generative adversarial networks (GANs) is proposed in this article. First, a hierarchical data recovery framework is proposed to handle different numbers of incomplete data due to the structure of the semicentral pipeline network. Second, a joint attention module is presented to capture both interior nature and correlation relationships of multivariate pressure series and further guarantee the consistency of pressure data. Third, the macromicrodual discriminators are proposed to evaluate the recovery result through the combination of the local and global variation in temporal and spatial dependencies. Based on the novel structures, the proposed model is able to recover incomplete data with abnormal fluctuation values, unreasonable fixed values, or missing values. Finally, under a series of data recovery experiments, the efficiency of the proposed method is evaluated. Experimental results demonstrate that the proposed method is a practical way to ensure data recovery performance in the pipeline network.Note to Practitioners—Status monitoring based on pressure data is of great importance for safe and efficient operation in a pipeline network. However, due to unexpected situations, the appearance of incomplete pressure data affects the subsequent data processing and status analysis, resulting in an incorrect decision. In this article, a deep learning-based method is proposed to recover the incomplete data. With the help of the spatiotemporal dependencies of multivariate pressure series, the proposed method can recover different numbers of incomplete data through the no-missing part of pressure data. The experiment results show that the proposed method is better than the similar data recovery methods through three different evaluation metrics. In the future, we will address the data recovery problem without the complete data pairs in the training process.

The security and economy of multienergy systems (MESs) are directly threatened by the potential cyberattacks. It is of great importance to investigate the effects of cyberattacks, e.g., denial of service (DoS) attacks, on distributed energy management algorithms. To this end, this article focuses on exploring how the frequency and the time of duration of DoS attacks influence the behavior of Newton–Raphson-based distributed energy management (NRBDEM) algorithm for MES and in which condition the optimal operations can still be obtained. First, a switched NRBDEM algorithm is presented, which is composed of the normal operation mode and the attack mode. In the attack mode, the attackers are able to change the communication structure at will and make it unconnected to destroy the convergence of the switched NRBDEM algorithm. Then, by making use of automatons to generate the hybrid time domain, the switched NRBDEM algorithm is further modeled and formulated as a hybrid dynamical system, which provides a mathematical model for the subsequent convergence analysis. Therein, the generated hybrid time domain satisfies the average dwell-time constraint and time-ratio constraint to limit the persistent attacks. Furthermore, we analyze the restrained conditions for persistent attacks, under which the optimality and convergence of the switched NRBDEM algorithm can be guaranteed still. Finally, simulation results demonstrate the effectiveness of the proposed method. Note to Practitioners—The multienergy system (MES) is viewed as a typical cyber-physical system. Since it works in a networked environment, the convergence and optimality of the corresponding distributed energy management algorithms are easily destroyed by various cyberattacks, such as the DoS attack. Few noticeable studies have been documented to investigate the distributed energy management problem for MES under persistent DoS attacks. To address this issue, this article proposes a switched NRBDEM algorithm subject to persistent DoS attacks, which is made up of normal operation mode and attack mode. Moreover, sufficient conditions are derived for the tolerable frequency of attack and the time of duration. It is helpful for the practitioners to know in which conditions the global convergence and optimality can be maintained still. Several simulations are provided to verify the correctness of the proposed theoretical analysis results. Our future work will aim at analyzing the performance of different energy management algorithms under different cyberattacks.

The device faults commonly exist in microgrids (MGs) and often undermine the system stability. However, the existing distributed methods generally design control protocols based on the absence of faults or only considering actuator faults, which limits the applicability of controllers in MG systems. Therefore, this paper investigates an adaptive prescribed fixed-time performance (PFTP) consensus fault-tolerant control (FTC) problem for the voltage regulation of a nonlinear islanded MG inverter-based distributed generators (DGs) with output contactor and actuator fault via the state event-triggered mechanism. The PFTP method for the voltage restoration synchronization error is proposed by constructing a novel performance function to increase the speed of voltage recovery. Based on the estimation properties of fuzzy logic system, an unknown nonlinear function and the residual term of output contactor are estimated. Then, an adaptive state event-triggering mechanism is designed to reduce the communication burden, and its main advantage is that the control protocol is updated in the aperiodic way at the triggering instants. Further, according to backstepping technology, a novel adaptive event-triggered consensus FTC protocol is constructed to achieve the voltage regulation of islanded MG, and a parameter adaptive law is designed to compensate the adverse effects of the output contactor fault. Based on the Lyapunov stability theory, it is proved that the voltage recovery synchronization error satisfies the PFTP, and all signals in the closed-loop systems are cooperatively uniformly ultimately bounded. Finally, the proposed control strategy is also validated by using the MATLAB.

Although the cyber-physical system stability is widely studied, scholars focus more on system stability with communication time delay. Therein, grid-connected inverters with the digital control system are regarded as one simplest and typical cyber-physical system. Meanwhile, the switching/sampling frequency of the inverter is always selected as low as possible from an efficiency viewpoint, resulting in unavoidable delay time. This delay time is apt to cause the system instability, which is more prone to severity under weak grid. To this end, this paper provides a minimum switching/sampling frequency for grid-connected inverters. Firstly, the system impedance model with equivalent delay time is constructed, which is based on padé approximate approach. This equivalent delay time consists of three parts, i.e., sampling delay time in cyber/physical level, calculation delay time in cyber level and pulsewidth modulation delay time in physical level, which reflects the cyber-physical interaction impact. Furthermore, the stability forbidden criterion is applied to make the switching/sampling frequency solving process become Hurwitz matrix identification problem through space mappings. Based on these space mappings, an adaptive step search approach is adopted to obtain the minimum switching/sampling frequency. Finally, the proposed approach can well evaluate the system stability under different frequencies through simulation and experiment.

The heuristic dynamic programming (HDP) ( λ )-based optimal control strategy, which takes a long-term prediction parameter λ into account using an iterative manner, accelerates the learning rate obviously. The computation complexity caused by the state-associated extra variable in λ -return value computing of the traditional value-gradient learning method can be reduced. However, as the iteration number increases, calculation costs have grown dramatically that bring huge challenge for the optimal control process with limited bandwidth and computational units. In this article, we propose an event-triggered HDP (ETHDP) ( λ ) optimal control strategy for nonlinear discrete-time (NDT) systems with unknown dynamics. The iterative relation for λ -return of the final target value is derived first. The event-triggered condition ensuring system stability is designed to reduce the computation and communication requirements. Next, we build a model-actor-critic neural network (NN) structure, in which the model NN evaluates the system state for getting λ -return of the current time target value, which is used to obtain the critic NN real-time update errors. The event-triggered optimal control signal and one-step-return value are approximated by actor and critic NN, respectively. Then, the event trigger-based uniformly ultimately bounded (UUB) stability of the system state and NN weight errors are demonstrated by applying the Lyapunov technology. Finally, we illustrate the effectiveness of our proposed ETHDP ( λ ) strategy by two cases.

This article considers the distributed bipartite adaptive event-triggered fault-tolerant consensus tracking issue for linear multiagent systems in the presence of actuator faults based on the output feedback control protocol. Both time-varying additive and multiplicative actuator faults are taken into account in the meantime. And the upper/lower bounds of actuator faults are not required to be known. First, the state observer is designed to settle the occurrence of unmeasurable system states. Two kinds of event-triggered mechanisms are then developed to schedule the interagent communication and controller updates. Next, with the developed event-triggered mechanisms, a novel observer-based bipartite adaptive control strategy is proposed such that the fault-tolerant control problem can be addressed. Compared with some related works on this topic, our control scheme can achieve the intermittent communication and intermittent controller updates, and the more general actuator faults and network topology are considered. It is proved that the exclusion of Zeno behavior can be realized. Finally, three illustrative examples are given to demonstrate the feasibility of the main theoretical findings.

The consensusability and global optimality problems are solved for the discrete-time linear multiagent system (MAS) with marginally stable and strictly unstable dynamics. A unified framework is proposed by capturing the maximal disc-guaranteed gain margin (GGM) of the discrete-time linear quadratic regulator (LQR). Sufficient and necessary conditions on consensusability are established. Two bounds of the consensus region are derived only in terms of the unstable eigenvalues of the agent' dynamics. For the single-input MAS, by proving that the radius of the consensus region exactly equals the reciprocal of the Mahler measure of the agent' dynamics, we incidentally reveal the relation between the maximal GGM and the intrinsic entropy rate of the system dynamics for single-input discrete-time linear systems. By employing the inverse optimal control approach, it is proved that the globally optimal consensus is achieved, if and only if the associated Laplacian matrix is a simple matrix and all its nonzero eigenvalues can be radially projected into a specific subset of the consensus region. Moreover, the limitation on the eigenvalues vanishes for the marginally stable MAS. As an application of the global optimality, the minimum-energy-distributed consensus control problem is solved for the marginally stable MAS. Finally, a numerical example is given to demonstrate the effectiveness of the obtained results.

This article presents an improved online adaptive dynamic programming (ADP) algorithm to solve the optimal control problem of continuous-time nonlinear systems with infinite horizon cost. The Hamilton-Jacobi-Bellman (HJB) equation is iteratively approximated by a novel critic-only structure which is constructed using the single echo state network (ESN). Inspired by the dual heuristic programming (DHP) technique, ESN is designed to approximate the costate function, then to derive the optimal controller. As the ESN is characterized by the echo state property (ESP), it is proved that the ESN can successfully approximate the solution to the HJB equation. Besides, to eliminate the requirement for the initial admissible control, a new weight tuning law is designed by adding an alternative condition. The stability of the closed-loop optimal control system and the convergence of the out weights of the ESN are guaranteed by using the Lyapunov theorem in the sense of uniformly ultimately bounded (UUB). Two simulation examples, including linear system and nonlinear system, are given to illustrate the availability and effectiveness of the proposed approach by comparing it with the polynomial neural-network scheme.

In daily pipeline inspection, it is significant to ensure good network communication and security. With the development of drone technology, it is possible to apply drones as air routers to collect information from pipeline networks and transmit it to pipeline inspectors. It is also crucial to achieve optimal drone deployment in pipeline networks. This article proposes a two-phase evolution optimal 3-D drone layout algorithm to deploy drones in pipeline networks. First, a 3-D pipeline graph model is designed to represent the possible projection position of drones, and the objective function is proposed for optimal drone deployment. Then, in the first phase, based on the features of the 3-D pipeline graph, the drone flight rules and constraint conditions are presented to calculate the number of drones and the initial layout sequence. In the second phase, according to the objective function and the above results, every drone is continuously moved in a small area to achieve a tradeoff between signal coverage and interference. Moreover, the key parameters of the objective function can be discussed to further optimize drone deployment. Simulation results are presented to illustrate the effectiveness and advantages of the proposed algorithm.

The microgrid with the high proportion of renewable sources has become the trend of the future. However, the negative features, such as renewable energy perturbation, nonlinear counterpart, and so on, are prone to causing the low-power quality of the ac microgrid. To deal with these problems, this article proposes an event-triggered consensus control approach. First, the nonlinear state-space function regarding the ac microgrid is built, which is further transformed into the standard linear multiagent model by using the singular perturbation method. It provides indispensable preprocessing for the direct application of advanced linear control approaches. Then, based on this standard linear multiagent model, the secondary consensus approach with the leader is designed to compensate for the output voltage deviation and achieve accurate power sharing. In order to decrease the communication among various distributed generators, the event-triggered communication method is further proposed. Meanwhile, the Zeno behavior is avoided through the theoretical proof. Finally, simulation results are presented to demonstrate the effectiveness of the proposed approach.

This study focuses on dissipativity-based fault detection for multiple delayed uncertain switched Takagi–Sugeno fuzzy stochastic systems with intermittent faults and unmeasurable premise variables. Nonlinear dynamics, exogenous disturbances, and measurement noise are also considered. In contrast to the existing study works, there is a wider range of applications. An observer is explored to detect faults. A controller is studied to stabilize the considered system. A piecewise fuzzy Lyapunov function is collected to obtain delay-dependent sufficient conditions by means of linear matrix inequalities. The designed observer has less conservatism. In addition, the strict$(\mathfrak {Q},\mathfrak {S},\mathfrak {R})-{\epsilon }-$dissipativity performance is achieved in the residual dynamic. Besides, the elaborate$H_{\infty }$performance and the elaborate$H\_{}$performance are also acquired. Finally, the availability of the method in this study is verified through two simulation examples.

This article devotes to solve the fault-tolerant control problem based on interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy stochastic switched uncertain time-delayed systems with signal quantization. Stochastic switched systems can model a dynamic structure susceptible to abrupt faults, making it more practically significant in power systems or economic systems. The core design is an observer-based fault-tolerant control scheme that can estimate incomplete measurable variables and eliminate the influence of fault dynamically well and enhancing the robust stability of the systems subject to quantization effects. A novel method in seeking the upper bound solution of time-varying delay efficiently decreases conservativeness, especially for the proposed time-delayed system. The simulated analysis is specified to verify the availability and validity of the obtained design method.

This article investigates the fault-tolerant control problem for stochastic switched interval type-2 (IT2) fuzzy time-delayed uncertain systems based on unknown input observer synthesis, which can avoid uneasy measurement on the time derivative of output, and estimate unavailable or partially measurable states, including sensor and actuator faults accurately. First, a desired fuzzy observer is designed to ensure the observer-based dynamic error system mean-square exponentially stable with sufficient condition of a strict$(\ell,\hbar,\wp)$-$\mho $-dissipative performance, which is a unified framework of passivity, and$\mathcal {H}_{\infty }$provides results with less conservativeness. Then, we concentrate on stability analyses on dissipativity-based switched IT2 fuzzy systems with stochastic perturbation through linear matrix inequalities, Lyapunov function, free-weighting matrices, and average dwell time, discussing it according to different values of disturbance. Finally, simulation examples are listed to account for availability and effectiveness of the research methodology.

In this article, a new design method based on the echo state network with time-varying reservoir parameter (TVRP-ESN) is proposed to optimize the proportional-integral-derivative (PID) controller parameters for a class of discrete-time systems with time delay. The TVRP-ESN can quickly obtain the PID controller parameters to meet the control performance of the system. According to the network learning and approximation ability of TVRP-ESN, the output weights and the reservoir parameters of TVRP-ESN can be synchronously updated, and then the TVRP-ESN can improve the convergence speed of determining the PID controller parameters. In order to update the output weights and the reservoir parameters of TVRP-ESN, the partial derivative of the system output error is used. Three simulation examples are used to show the effectiveness of the proposed method.

In terms of pipeline leak detection, the unavoidable fact is that existing data could not provide enough effective leak data to train a high accuracy model. To address this issue, this article proposes mixed generative adversarial networks (mixed-GANs) as a practical way to provide additional data, ensuring data reliability. First, multitype generative networks with heterogeneous parameter-updating mechanisms are designed to explore a variety of different solutions and eliminate the potential risks of instable training and scenario collapse. Then, based on expert experience, two data constraints are proposed to describe leak characteristics and further evaluate the quality of generated leak data in the training process. Through integrating the particle swarm optimization algorithm into generative model training, mixed-GAN has better generation performance than the conventional gradient descent algorithm. Based on the above-mentioned contents, the proposed model is able to provide satisfactory leak data with different scenarios, contributing to data quantity expansion, data credibility enhancement, and data variety enrichment. Finally, extensive experiments are given to illustrate the effectiveness of the proposed generative model for pipeline network leak detection.

In this article, a novel data-based adaptive dynamic programming (ADP) method is presented to solve the optimal consensus tracking control problem for discrete-time (DT) multiagent systems (MASs) with multiple time delays. Necessary and sufficient conditions of the corresponding equivalent time-delay system are provided on the basis of the causal transformations. Benefitting from the construction of tracking error dynamics, the optimal tracking problem can be transformed into settling the Nash-equilibrium in the graphical game, which can be completed by solving the coupled Hamilton-Jacobi (HJ) equations. An error estimator is introduced to construct the tracking error of the MASs only using the input and output (I/O) data. Therefore, the designed data-based ADP algorithm can minimize the cost functions and ensure the consensus of MASs without the knowledge of system dynamics. Finally, a numerical example is given to demonstrate the effectiveness of the proposed method.

In the complex practical engineering systems, many interferences and attacking signals are inevitable in industrial applications. This article investigates the reinforcement learning (RL)-based resilient control algorithm for a class of Markovion jump systems with completely unknown transition probability information. Based on the Takagi-Sugeno logical structure, the resilient control problem of the nonlinear Markovion systems is converted into solving a set of local dynamic games, where the control policy and attacking signal are considered as two rival players. Combining the potential learning and forecasting abilities, the new integral RL (IRL) algorithm is designed via system data to compute the zero-sum games without using the information of stationary transition probability. Besides, the matrices of system dynamics can also be partially unknown, and the new architecture requires less transmission and computation during the learning process. The stochastic stability of the system dynamics under the developed overall resilient control is guaranteed based on the Lyapunov theory. Finally, the designed IRL-based resilient control is applied to a typical multimode robot arm system, and implementing results demonstrate the practicality and effectiveness.

Considering that there are many systems with limited network bandwidth in practice, this article studies the leader-following consensus problem for a class of nonlinear multiagent systems (MASs). The purpose of this article is to reduce unnecessary information transmission between any pair of adjacent agents including the leader in the MASs through intermittent communication. The novel event-triggered and asynchronous edge-event triggered mechanisms are designed for the leader and all edges, respectively. The static and dynamic consensus protocols under these mechanisms are proposed to address the leader-following consensus problem for MASs with Lipschitz dynamics, and the systems will not exhibit Zeno behavior under these two control schemes. Note that the dynamic consensus protocol does not rely on any global values of MASs, it is a fully distributed way. Finally, a practice simulation example is introduced to illustrate the theoretical results obtained.

The cooperative bipartite containment control problem of linear multiagent systems is investigated based on the adaptive distributed observer in this article. The graph among the agents is structurally balanced. A novel distributed error term is designed to guarantee that some outputs of the followers converge to the convex hull spanned by the leaders, and the other followers' outputs converge to the symmetric convex hull. The matrices of the exosystems are not available for each follower. A general method is presented to verify the validity of a novel distributed adaptive observer rather than the previous approach. In other words, the definition of the M -matrix is not necessary in our result. Based on the distributed adaptive observer, an output-feedback control protocol is designed to solve the bipartite containment control problem. Finally, a numerical simulation is given to illustrate the effectiveness of the theoretical results.

In this article, the finite-time containment control problem of the nonlinear multiagent systems (MAS) is investigated, in which the follower agents are subjected to full state constraints. The fuzzy logic system is employed to approximate the uncertain dynamic of the nonlinear MAS. A new nonlinear transformation function (NTF) is proposed, which serves as a unified tool for coping with the systems subjected to state constraints and no constraint. By combing the variable transformation with the proposed NTF, the original systems with state constraints are converted into the equivalent free-constrained systems. Moreover, as long as the boundedness of the states in transformed systems is guaranteed, the states of the original MAS do not transgress the preassigned boundaries. Based on the finite-time stability theory, the adaptive fuzzy control scheme is constructed for the transformed systems. It is proved that the outputs of all the followers are driven to converge to the convex hull spanned by the leaders in a finite time, and all the signals in the closed-loop systems are bounded in sense of mean square. The full state constraints for the followers are not violated all the time. Especially, the control structure does not need to change even there is no constraint imposes on the state. Finally, simulation examples are given to verify the effectiveness of the proposed finite-time control scheme.

This article investigates the prescribed finite-time control for the nonlinear system with uncertain nonlinearity and unknown control directions. Different from the existing finite/fixed-time control methods on the basis of the fractional-order state feedback, the proposed finite-time control in this article only employs regular state feedback. The key is that a novel prescribed finite-time performance function is introduced such that the controller design is under the standard Lyapunov stability theoretical framework. A lemma is developed as a tool for coping with the coexistence of multiple Nussbaum gains for the stochastic system with unknown control directions. Fuzzy logic systems are used to approximate the uncertain nonlinear functions. Applying the backstepping structure, an adaptive fuzzy control scheme is developed. The salient characteristic of the proposed control is that the finite convergence time and the accuracy of the tracking error are independent of the initial states and design parameters, and that can be assigned in a prior. Two examples are given to demonstrate the feasibility and effectiveness of the proposed control scheme.

This article investigates multiple delay-dependent robust$H_\infty$finite-time filtering for uncertain Itô stochastic Takagi–Sugeno (T–S) fuzzy semi-Markovian jump systems with state constraints. Few studies exist for Itô stochastic T–S fuzzy semi-Markovian jump systems with state constraints. First, a T–S fuzzy semi-Markovian jump filter is explored and a controller is designed. In terms of linear matrix inequalities, delay-dependent sufficient conditions as well as bounded real lemma are gathered by utilizing stochastic Lyapunov function. Robust finite-time boundedness and input–output finite-time mean square stabilization are achieved and gain matrices of the filter and controller are obtained at the same time. A numerical example is presented to validate the feasibility of the proposed results in this article. And the admissible maximal delay bounds are calculated.

In this article, we pay our attention on exploring observer-based actuator fault and sensor fault reconstruction associated with fault-tolerant control (FTC) for nonlinear systems, which are approximated by the Takagi–Sugeno (T-S) fuzzy method. By designing a brand-new unknown input observer (UIO), unknown state, sensor, and actuator faults can be reconstructed simultaneously, where some constraints imposed on the actuator fault such as the first derivative of fault being equal to zero required in the previous results are not needed in our work. With the support of this estimation information, a FTC scheme is well established, by which the system may recover its performance even in the occurrence of faults. Another contribution of the developed method is that all the stability criteria are deduced via fuzzy Lyapunov functions. It makes the obtained conditions more relaxed than the ones derived by quadratic Lyapunov functions. Finally, simulation results conducted on two practical dynamics are provided to show the validity of the achieved procedure.

This article is concerned with the fuzzy functional observer-based finite-time adaptive sliding-mode control for nonlinear systems with the partly unmeasurable states and some matched uncertainties. First, considering that the upper bound of the uncertain function exists but unknown, a fuzzy functional observer (FFO) with an adaptive compensator is constructed. Second, an FFO-based fuzzy integral sliding mode controller (ISMCr) is designed such that the closed-loop fuzzy systems are finite-time bounded with$H_{\infty }$performance over the reaching phase, the sliding phase, and the whole finite-time interval, respectively. To reduce the conservatism and increase the solution space of linear matrix inequality conditions, the fuzzy Lyapunov functional approach and equivalent fuzzy relaxed matrices technique are developed by introducing some relaxed matrices in the derivative of the fuzzy normalized membership function. Compared with the common Luenberger-type observer-based approach, the gain matrices of ISMCr depend on the FFO designed, which also enhances the flexibility of controller design. Finally, a simulation example with some comparison is given to show the effectiveness of the proposed method.

The main task of noninvasive load monitoring is to disaggregate the power consumption of a single household appliance from an electricity meter that detects the power consumption of all household appliances. The deep neural network method has achieved leading results in this field. In this article, a multichannel spatio-temporal feature fusion method is proposed, where the spatial features extracted by convolution neural network and the temporal features extracted by the recurrent neural network are fused. And the attention module is introduced to further improve the performance of the model. Finally, the effectiveness and superiority of the proposed method are verified on three public datasets.

Although privacy-preserving is often considered in energy management and energy scheduling of microgrids (MGs), to the best of authors’ knowledge, there are almost no literatures on distributed secondary control of MGs with privacy-preserving. To fill this gap, this article investigates the privacy-preserving problem for voltage restoration of an ac MG. First, the model of the MG is transformed into a linear multiagent system model by feedback linearization. Furthermore, output mask approach, which avoids divulging the initial condition by inserting a dynamic mask on the exchanged state information, is adopted to make the distributed generator agents exactly converge to the reference value rather than the value with a preset certain convergence error by differential private. Meanwhile, the sliding mode control scheme with simple structure is adopted to accelerate the convergence speed of the masked system. However, it is difficult to design the controller directly because of the strong nonlinearity brought by the mask function. To solve this problem, we first implement the controller for the original system, and then extend it to the masked system. After that, we carry out the consensus analysis of the controlled masked system. Finally, the effectiveness of the proposed control scheme is validated in the MATLAB/Simulink environment.

Distributed algorithms are increasingly being used to solve the economic dispatch problem of integrated energy systems (IESs) because of their high flexibility and strong robustness, but those algorithms also bring more risk of cyber-attacks in IESs. To solve this problem, this article investigates the distributed robust economic dispatch problem of IESs under cyber-attacks. First, as the first line of defense against attacks, a privacy-preserving protocol is designed for covering up some vital information used for economic dispatch of IESs. On this basis, a distributed robust economic dispatch strategy is presented to achieve the energy management of IESs in the presence of misbehaving units, which consists of a neighbor-observe-based detection process and a reputation-based isolation process. The proposed strategy is implemented in a fully distributed fashion and possesses strong robustness against various colluding and noncolluding attacks. In addition, the strategy can not only ensure the reliability of information transmission among energy units, but also solve the problem of incorrect measurement of distributed local load data caused by cyber-attacks. Finally, the effectiveness of the proposed strategy is illustrated by simulation cases on a 39-bus 32-node power–heat IES.

To improve the validity of magnetic flux leakage (MFL) multisensor signals, anomaly detection has become a significant part of MFL signal processing. The anomalies in MFL are uncertain and have no prior information or labels. Therefore, the detection and location of the anomalies become a difficult issue. Regarding the abovementioned problem, we propose an unsupervised method called multisensor cycle-supervised convolutional neural network (CsCNN). The CsCNN is built including multiple CNNs with the same structure and a cycle-supervised part. The proposed model realizes unsupervised anomaly detection through multiple cycle-supervised CNNs for the first time. Moreover, the latent relationship between multisensor signals is established by CsCNN to take full use of multisensor information. Besides, a dynamic threshold is applied to detect anomalies. In the end, experiments on simulated signals and measured signals are conducted, and CsCNN is compared to the state-of-the-art methods. The results show that the proposed method is effective.

The small sample problem is an open challenge in industrial data-driven fault diagnosis. Domain adaptation (DA) has been successfully used to enhance small samples by transferring fault samples in other working conditions, but it suffers from low accuracy and poor generalization for modeling. To address this issue, in this article, a task-based bias DA network (TBDA-net) is proposed. First, an adaptive dimension alignment subnet is proposed, which overcomes the information loss of the target domain caused by the dimension alignment of different domains, thus the accuracy of modeling is improved. Second, a task-based distribution matching subnet is proposed, in which the correlation difference of domains is considered and the designed auxiliary branch is embedded in the matching network to protect the sample diversity of multisource domains, thus the generalization of modeling is improved. Finally, benchmark simulated experiments and real-world application experiments are conducted to evaluate the proposed method. Experiment results show the TBDA-net outperforms the existing methods for modeling under industrial small samples

This study investigates the bipartite fixed-time time-varying output formation-containment tracking issue for heterogeneous linear multiagent systems with multiple leaders. Both cooperative communication and antagonistic communication between neighbor agents are taken into account. First, the bipartite fixed-time compensator is put forward to estimate the convex hull of leaders' states. Different from the existing techniques, the proposed compensator has the following three highlights: 1) it is continuous without involving the sign function, and thus, the chattering phenomenon can be avoided; 2) its estimation can be achieved within a fixed time; and 3) the communication between neighbors can not only be cooperative but also be antagonistic. Note that the proposed compensator is dependent on the global information of network topology. To deal with this issue, the fully distributed adaptive bipartite fixed-time compensator is further proposed. It can estimate not only the convex hull of leaders' states but also the leaders' system matrices. Based on the proposed compensators, the distributed controllers are then developed such that the bipartite time-varying output formation-containment tracking can be achieved within a fixed time. Finally, two examples are given to illustrate the feasibility of the main theoretical findings.

This article is concerned with the issue of dissipativity-based finite-time multiple delay-dependent filtering for uncertain semi-Markovian jump random nonlinear systems with state constraints. There are multiple time-varying delays, nonlinear functions, and intermittent faults (IFs) in the systems. This is one of the few attempts for the issue studied in this article. First, a filter is designed for the uncertain semi-Markovian jump random nonlinear systems. An augmented system with regard to the resulting filtering error is acquired. Then, sufficient conditions of the augmented system are generated by the stochastic Lyapunov function. Finite-time boundedness (FTB) and input-output finite-time mean square stabilization (IO-FTMSS) are both realized. The effectiveness and feasibility of the method are rendered via three examples.

This brief considers the security control problem for nonlinear cyber-physical systems (CPSs) against jamming attacks. First, a novel event-based model-free adaptive control (MFAC) framework is established. Second, a multistep predictive compensation algorithm (PCA) is developed to make compensation for the lost data caused by jamming attacks, even consecutive attacks. Then, an event-triggering mechanism with the dead-zone operator is introduced in the adaptive controller, which can effectively save communication resources and reduce the calculation burden of the controller without affecting the control performance of systems. Moreover, the boundedness of the tracking error is ensured in the mean-square sense, and only the input/output (I/O) data are used in the whole design process. Finally, simulation comparisons are provided to show the effectiveness of our method.

The consensus problem of general linear multiagent systems (MASs) is studied under switching topologies by using observer-based event-triggered control method in this article. On the basis of the output information of agents, two kinds of novel event-triggered adaptive control schemes are designed to achieve the leaderless and leader-follower consensus problems, which do not need to utilize the global information of the communication networks. Finally, two simulation examples are introduced to show that the consensus error converges to zero and Zeno behavior is eliminated in MASs. Compared with the existing output feedback control research, one of the significant advantages of our methods is that the controller protocols and triggering mechanisms do not rely on any global information, are independent of the network scale, and are fully distributed ways.

In the study, a MIMO model-free-adaptive-iterative-learning-control-based (MFAILC-based) consensus tracking scheme for multiagent systems (MASs) has been proposed. The dynamics of agents are heterogeneous and unknown. And the compact form dynamic linearization (CFDL) technique is utilized to describe the unknown nonlinear dynamics of agents along iteration axis. Then, the MIMO MFAILC-based consensus tracking algorithm is proposed for MASs under fixed topology. From the proof, we can obtain that the consensus tracking error can converge to zero along iteration axis asymptotically. Next, the MFAILC-based consensus tracking algorithm is extended to controlling the MASs under iteration-switching topologies and the MASs with external disturbances, respectively. Compared with prior work, the main features of this article are the MFAILC-based consensus strategy can be utilized for MIMO MASs, and the framework of robust MFAILC is built for MIMO MASs with external disturbances. Finally, three simulations are given to verify the effectiveness of the consensus strategy for MASs under fixed and switching topology and MASs with external disturbances, respectively.

This article studies the command-filter-based fixed-time bipartite containment control problem for a class of nonlinear stochastic multiagent systems (MASs). The considered stochastic MASs in nonstrict feedback form is subject to unknown nonlinear functions and stochastic disturbances, which can be solved by exploiting the universal approximation property of radial basis function neural networks. In addition, the event-triggered mechanism is used to improve the utilization of communication resources while avoiding Zeno behavior. The control protocol based on the command-filtered backstepping technique is proposed to ensure that the followers can converge to the convex hull formed by the leaders. Moreover, the closed-loop stability of stochastic MASs is proved to be semiglobal practical fixed-time stability. Finally, a numerical example simulation and an actual system simulation about a group of five single-link manipulator systems are presented to verify the effectiveness of the proposed method.

In this study, the state formation control issue of general linear networked multi-agent systems (MASs) is considered. Combining self-triggered strategy with general event-triggered strategy, a novel fully distributed asynchronous mixed self- and event-triggered control strategy is proposed, which can make the MASs achieve the prescribed state formation structure. The control strategy proposed in this study does not depend on any global network information. Therefore, no matter how large scale of the network is, the control strategy is feasible. Meanwhile, the control strategy uses the sampled state information at triggering instants instead of the real-time state information, which efficiently eliminates continuous communication among agents. Different from the existing studies, the event-triggered detector starts to work if and only if the next self-triggering instant comes in the control strategy proposed in this article. Thus, the control strategy can save more communication resources between sensor and event-triggered detector within the same inter-event interval compared with the previous event-triggered strategies. In addition, the control strategy designs a exponential decay term in the triggering function to rule out the unexpected Zeno behavior and reduce the triggering number. Finally, the numerical simulation result of multi-robot formation is given, which demonstrates the feasibility and performance of the proposed control strategy.

In this work, we study simultaneous reconstructions of sensor fault and actuator fault, as well as fault-tolerant control (FTC) for nonlinear systems approximated by the T–S fuzzy model. By making the sensor fault as part of the state, a rectangular singular system is firstly given, based on which, a proportional-integral observer (PIO) is synthesized to realize the reconstructions of sensor and actuator faults simultaneously. With the support of these estimation information, an FTC scheme is well provided to maintain the stability of the closed-loop system even in the occurrence of faults. Furthermore, by resorting to linear matrix inequalities (LMIs), the stability criteria are formulated via the fuzzy Lyapunov function method. Finally, the performance of the theoretical result is verified through two real dynamics.

The issue of fault detection (FD) and fault-tolerant control is developed for a switched random nonlinear system against multiple delays in this work. There also exist parameter uncertainties, exogenous disturbances, nonlinear functions as well as measurement noise in this model. This is one of the few tries to investigate the dissipativity issue and an design FD-based observer for the switched random system. Based on the dimensionally adjustable observer, the controller is designed to make this system noise-to-state exponentially mean-square stable. Then, these sufficient stability conditions can be earned by the piecewise Lyapunov function. Concurrently, this strict$\left({\mathsf {Q}, \mathsf {S}, \mathsf {R}}\right)-{\upsilon }-$dissipativity performance, this elaborate$H_{\infty }$performance as well as this elaborate$H\_{}$performance can be gained, separately. In addition, the merits and feasibility are presented via a numerical simulation and a practical example.

Distributed renewable energy source is an advisable solution for dc microgrids to reduce fuel consumption and CO2emission. In such microgrids, the installation of two or more battery energy storage (BES) units is utilized to compensate the power imbalance between the sources and loads. Nevertheless, energy management with numerous BES units does not simultaneously consider the impacts of distributed generators (DGs) and constant power loads (CPLs). Since the inaccurate current sharing will shorten the lifetime of the batteries and cause instability problem, this article proposes a distributed secondary$H_{\infty }$consensus approach based on the dynamic event-triggered communication method to realize accurate current sharing and efficient operation in the presence of numerous DGs and CPLs. First, the whole state-space function model of the dc microgrid consisting of DGs, batteries, resistive loads, and CPLs, is first built in detail. This model is further transformed into standard linear heterogeneous multiagent systems, which provides an indispensable preprocessing for advanced control strategy application. Then, the distributed secondary$H_{\infty }$consensus approach based on the foresaid systems is designed to achieve accurate current sharing. For reducing the communication among batteries and the controller updating frequency, the dynamic event-triggered communication method is proposed. Compared with existing event-triggered methods, the communication and controller updating frequency of the proposed dynamic event-triggered method have been reduced a lot. Additionally, the proposed method can not only avoid the Zeno behavior, but also obtain the lowest bound of the sampled time interval. Finally, the numerical simulation results and experimental results verify the effectiveness of the proposed control strategy.

Compared with stochastic differential equations (SDEs) driven by white noise, random differential equations (RDEs) generated by colored noise are claimed to be more practical. This article considers reduced-order high-gain observer (ROHGO)-based neural tracking control on random nonlinear systems having output delay. In order to foster the design and analysis, the estimated states and the estimation errors are scaled by the high gain of the observer. Based on neural network (NN) approximation and state observation, an adaptive controller is designed for the overall system using the backstepping method. It is proved that all the closed-loop signals are bounded almost surely, letting alone the tracking error. By tuning the related design parameters, the asymptotic tracking error could be regulated arbitrarily small. Within the best of our knowledge, this article serves as the first attempt for NN-based control on RDE systems. Finally, the validity of main results is confirmed by a simulation example.

In this article, we consider complex networks without connection information. The absence of global structure induces a great obstacle in the structural controllability analysis of these networks. Thus, a topology predicting method based on the connection probability matrix is proposed to provide a global structure for structural controllability analysis in this article. Furthermore, the modified principles of predicting global network topologies are established to acquire a more accurate global connection relationship. Eventually, the drive node set of these networks is determined by predicting global topologies. The accuracy of the proposed topology predicting method is verified by numerical simulations in the context of artificial networks and real networks. The results reveal that the global topology and structural controllability of complex networks with large scale and high edge density could be accurately predicted by utilizing the proposed method.

This article studies a decoupling cooperative control (DCC) with state feedback. It solves the output consensus problem on heterogeneous multiagent systems (MASs) over the input graph whose all nodes are reachable from the external reference signal. In light of the communication topology, the DCC forces each agent to reach a consensus on the given trajectory, which is from the reference signal of the common exosystem. Here, the output consensus problem is first equivalently converted into a stabilization one. Second the DCC involves a mapping${\mathcal{ L}}_{d}$of Laplacian matrix${\mathcal{ L}}$, i.e., every diagonal element of the Laplacian matrix is multiplied by a parameter$d_{i}$.${\mathcal{ L}}_{d}$moves Geršgorin circles of${\mathcal{ L}}$. By the merit of${\mathcal{ L}}_{d}$, the equivalent stabilization problem is solved by the strictly positive real (SPR). In fact, the DCC is an approximate distributed decoupling control, when$d_{i}$is sufficiently large. In addition, under the framework of the DCC, the assignment problem of the dominant poles for each agent is also solved by inverse optimal regulator technology in this article. Finally, several simulation examples verify the effectiveness of our method.

In this article, the leader-following exponential consensus problem of fractional-order descriptor multiagent systems (FOD-MASs) with event-triggered control (ETC) protocol is investigated, which includes integer-order descriptor multiagent systems as the special case. Two classes of control schemes and the corresponding event-triggered conditions are presented, respectively. First, a distributed state feedback ETC protocol is developed to reach the leader-following exponential consensus. The leader-following exponential consensus is achieved in the sense of the Mittag-Leffler stability of fractional-order systems. Second, when full-state measurements are not available, a novel observer-type output feedback ETC strategy with some desirable characteristics is provided. For two distributed ETC protocols, consensus conditions are derived and convergence rate of the system can be adjusted. Also, the integral inequality is applied to get the fact that Zeno behavior is excluded, which verifies the feasibility of ETC schemes. Finally, the effectiveness of conclusions is demonstrated by the examples.

Safe transmission of oil pipelines is one of the guarantees of national defense and environmental protection. Magnetic flux leakage (MFL) testing is critical to the safety inspection of in-service pipelines. In the detection process, the incompleteness of MFL data affects defect location and inversion severely. This article proposes an MFL data recovery method based on multifeature condition risk, which can effectively handle the block data gap problem. First, a preprocessing mechanism is proposed to determine defect boundaries and uniformly interpolate the raw data automatically. Second, a multifeature extraction method of MFL defect data is proposed, which takes full advantage of the complete data and reduces the impact of data gaps. Third, a novel data reconstruction method based on feature conditional risk is proposed, where prior information of MFL data is regressed, relying on the regression coefficient calculated by dynamic programming. Finally, comparison experiments on different sizes of data gaps, varying robustness, and average running time are conducted, respectively. The MFL data are derived from actual measurements. The results indicate that the proposed method is more robust, more efficient, and faster.Note to Practitioners—This article is motivated by the problem of data gaps in magnetic flux leakage (MFL) data in actual pipeline safety transmission measurements, which seriously affects defect inversion and assessment. In this article, in terms of the intrinsic characteristics for MFL data, we propose a new and effective approach by extracting the multifeature of defects and using conditional risk to recovery data gaps. Our method takes advantage of the no-missing part of MFL data and prior samples, which are obtained from practical measurements. The experiment results show that our proposed method is more advantageous than the comparison methods, and therefore, our proposed method has strong practical value. Our future work will aim at improving the versatility of the proposed method.

In this article, off-policy reinforcement learning (RL) algorithm is established to solve the discrete-time N -player nonzero-sum (NZS) games with completely unknown dynamics. The N -coupled generalized algebraic Riccati equations (GARE) are derived, and then policy iteration (PI) algorithm is used to obtain the N -tuple of iterative control and iterative value function. As the system dynamics is necessary in PI algorithm, off-policy RL method is developed for discrete-time N -player NZS games. The off-policy N -coupled Hamilton-Jacobi (HJ) equation is derived based on quadratic value functions. According to the Kronecker product, the N -coupled HJ equation is decomposed into unknown parameter part and the system operation data part, which makes the N -coupled HJ equation solved independent of system dynamics. The least square is used to calculate the iterative value function and N -tuple of iterative control. The existence of Nash equilibrium is proved. The result of the proposed method for discrete-time unknown dynamics NZS games is indicated by the simulation examples.

This article is concerned with observer-based fault estimation (FE) and tolerant controller design for a series of discrete-time Takagi-Sugeno (T-S) fuzzy stochastic systems. There exist multiple time-varying state delays, intermittent sensor and actuator faults, nonlinear dynamics, and exogenous disturbances in the systems. Compared with the results of the existence, this approach suggested in this article is more flexible and feasible. By means of the FE information, a novel fuzzy adaptive descriptor observer is developed to obtain the error dynamics. Then, an active observer-based fault-tolerant controller is designed to stabilize the closed-loop fuzzy system. Furthermore, a set of delay-dependent sufficient conditions are provided by the fuzzy Lyapunov function with the way of linear matrix inequalities (LMIs), which has less conservatism compared with the ones of the existing observers and fault-tolerant controllers. Finally, a simulation example is shown to illustrate the advantages and effectiveness of this approach depicted in this article.

In this article, by use of sliding-mode control, a descriptor stochastic fuzzy system with time-varying delay is considered. First, a new sliding-mode surface is proposed. By use of it, the fuzzy system with different input matrices could be studied. At the same time, the controlled system still has strong robustness to external disturbance. Second, since time delay is variable, there is not the time-delay term in the function of the new sliding-mode surface. It makes the controller construct hard. Third, the process of constructing the controller is different from the traditional method. The process of designing the controller does not use the border of external disturbance. Using a new self-adaptive method, the considered system reaches and is kept on the surface. Unlike the earlier sliding-mode controller, the virtue of this controller is that the fluctuation of the controller is small. Finally, two simulations are given to illustrate the effect of this method.

This article considers an adaptive fuzzy control problem for nonstrict-feedback nonlinear stochastic systems, which contain input delay, output constraints, and unknown control coefficients, simultaneously. First, an original stochastic nonlinear mapping and the Pade approximation transformation techniques are developed to solve the symmetric output constraints and input delay. Then, an adaptive fuzzy controller is designed for the unknown nonlinear systems, in which the Nussbaum function is employed to deal with the unknown time-varying control coefficients. Tracking errors are ensured to converge to a small neighborhood around the origin, and the system output does not violate the predefined constrained conditions. All the signals of the closed-loop systems have proven to remain bounded in probability. Moreover, the asymmetric output-constrained control is also studied. Two simulation examples are provided to show the effectiveness of the developed method.

In this article, the bipartite fixed-time output consensus problem of heterogeneous linear multiagent systems (MASs) is investigated. First, a distributed bipartite fixed-time observer is proposed, by which the follower can estimate the leader's state. The estimate value is the same as the leader's state in modulus but may not in sign due to the existence of antagonistic interactions between agents. Then, an adaptive bipartite fixed-time observer is further proposed. It is fully distributed without involving any global information. This adaptive bipartite fixed-time observer can estimate not only the leader's system matrix but also the leader's state. Next, distributed nonlinear control laws are developed based on two observers, respectively, such that the bipartite fixed-time output consensus of heterogeneous linear MASs can be achieved. Moreover, the upper bound of the settling time is independent of initial states of agents. Finally, the examples are given to demonstrate the results.

This article focuses on finite-time adaptive fuzzy output-feedback control for a class of nontriangular nonlinear systems with full-state constraints and unmeasurable states. Fuzzy-logic systems and the fuzzy state observer are employed to approximate uncertain nonlinear functions and estimate the unmeasured states, respectively. In order to solve the algebraic loop problem generated by the nontriangular structure, a variable separation approach based on the property of the fuzzy basis function is utilized. The barrier Lyapunov function is incorporated into each step of backstepping, and the condition of the state constraint is satisfied. The dynamic surface technique with an auxiliary first-order linear filter is applied to avoid the problem of an "explosion of complexity." Based on the finite-time stability theory, an adaptive fuzzy controller is constructed to guarantee that all signals in the closed-loop system are bounded, the tracking error converges to a small neighborhood of the origin in a finite time, and all states are ensured to remain in the predefined sets. Finally, the simulation results reveal the effectiveness of the proposed control design.

In this article, a novel parallel tracking control optimization algorithm is first proposed for partially unknown fuzzy interconnected systems. In the existing standard optimal tracking control, the bounded or nonasymptotic stable reference trajectory will lead the feedback control not converging to zero, which causes the performance index infinite and invalid. By using the precompensation technique, in this article, the working feedback control is considered as a reconstructed dynamic with the virtual control and a new augmented fuzzy interconnected tracking system is built, thus that the performance index is valid for optimal control. Then, combining the integral reinforcement learning (RL) method and decentralized control design, the novel integral RL parallel algorithm is first developed to solve the tracking controls for interconnected systems, which relax the requirements of exact matrices information Aikand Bikduring the solving process. Both the convergence and stability of the designed control optimization scheme are guaranteed by theorems. Finally, the new parallel tracking algorithm for interconnected systems is verified through the dual-manipulator coordination system and simulation results demonstrate the effectiveness.

This article addresses an adaptive fuzzy control for the nonstrict-feedback nonlinear systems with asymmetric time-varying full state constraints. To prevent the state constraints being violated, the nonlinear state-dependent function (NSDF) is introduced instead of the tradition barrier lyapunov function (BLF). The constrained systems are transformed into a novel free-constrained systems based on the NSDF. A direct approach is given to cope with the state constraints. Fuzzy logic system is utilized to approximate the uncertain nonlinear system functions. The new affine variables are constructed for the transformed systems, and the prior knowledge of the control gains is not required necessarily. Under the designed control scheme, the boundedness of the signals in the closed-loop systems is guaranteed certainly, and the asymmetric time-varying constraints are maintained. This proposed method removes the feasibility condition on the virtual controller derived from the method of BLF. The effectiveness of the proposed control strategy is verified by two simulation examples.

This article deals with delay-dependent H∞guaranteed cost control for uncertain switched Takagi-Sugeno (T-S) fuzzy systems with multiple interval time-varying delays. It is the first time that the fault tolerant control (FTC) of nonlinear systems subject to measurement noise, external disturbances, nonlinear functions, and intermittent faults (IFs) in sensors and actuators is investigated. A passive dynamic full-order output feedback controller is first addressed for the system to guarantee the exponential stability. Compared with the existing results, the restrictions on the multiple time-varying delays are relaxed in the derivation of this work. The suggested controller has a wider application. Moreover, as the piecewise fuzzy Lyapunov function is introduced with some slack matrices, the delay-dependent sufficient conditions are provided in the way of a cluster of linear matrix inequalities (LMIs) and then less conservatism is attained by our design contrast to the existing fault tolerant controllers. At last, two simulation examples and some comparisons are achieved to illustrate the novelty and feasibility of the method gathered in the article.

This article investigates delay-dependent stability analysis and stabilization for continuous Takagi–Sugeno fuzzy systems with a time-varying delay. By employing dynamic delay partition, the delay interval$[0, d(t)]$is partitioned into some variable subintervals so that much more information of time-varying delay can be utilized. Moreover, a novel relaxed integral inequality is applied to estimate the derivative of Lyapunov–Krasovskii functionals for achieving reduction of estimation gap. As a result, several further robust stability and stabilization criteria with less conservativeness are proposed. Finally, three examples are provided to demonstrate the effectiveness and advantages of the presented methods.

This article addresses an adaptive fuzzy finite-time control for a class of uncertain strict-feedback nonlinear systems with backlashlike hysteresis and stochastic disturbances. At first, a novel criterion of semiglobally finite-time stability in probability (SGFSP) is established based on Lyapunov function method. Under the proposed stability criterion, an adaptive fuzzy finite-time control scheme is designed. In the design process of the controller, command filter technique is introduced to overcome the problems of “explosion of complexity” and “singularity” inhered in the traditional adaptive finite-time control based on the backstepping method. Meanwhile, via constructing the corresponding error compensating systems, the effect of errors generated by the command filters is reduced, such that the original systems have more better tracking performance. To cope with the influence of backlashlike hysteresis input, an auxiliary system is constructed, in which the output signal is applied to compensate the effect of the hysteresis. It is shown that the tracking error can converge to a small neighborhood of original in finite time, and the closed-loop system is SGFSP under the constructed controller. Finally, the effectiveness of the proposed control strategy is further verified by two simulation examples.

This article investigates the problem of distributed cooperative energy management of multiple energy bodies with the consideration of both the optimal energy generation/consumption of each participant within single energy body and the optimal energy distribution on the interconnected lines between any pair of energy bodies. First, we define the physical and communication structure of the system formed by many energy bodies, each of which is viewed as a multienergy prosumer. Then, a distributed energy management model is proposed to achieve not only maximum profits of overall energy generation and consumption, but also minimum cost of energy delivery. To address this issue, a distributed double-Newton descent (DDND) algorithm is proposed, which possesses two advantages. On the one hand, by employing second-order information, the concept of Newton descent is embedded into the implementation of the proposed algorithm, resulting in faster convergence speed. On the other hand, the proposed algorithm performs in a fully distributed fashion. As a consequence, each participant can locally obtain its optimal operation as well as the global energy market clearing prices; meanwhile, each energy router can locally obtain the optimal exchanged energy with its neighbor energy routers. Moreover, we prove that the proposed DDND algorithm can asymptotically converge to the global optimal point. As a result, the correctness of the DDND algorithm can be guaranteed in theory. Finally, simulation results validate the effectiveness of the proposed algorithm.

This article addresses the adaptive event-triggered neural control problem for nonaffine pure-feedback nonlinear multiagent systems with dynamic disturbance, unmodeled dynamics, and dead-zone input. Radial basis function neural networks are applied to approximate the unknown nonlinear function. A dynamic signal is constructed to deal with the design difficulties in the unmodeled dynamics. Moreover, to reduce the communication burden, we propose an event-triggered strategy with a varying threshold. Based on the Lyapunov function method and adaptive neural control approach, a novel event-triggered control protocol is constructed, which realizes that the outputs of all followers converge to a neighborhood of the leader's output and ensures that all signals are bounded in the closed-loop system. An illustrative simulation example is applied to verify the usefulness of the proposed algorithms.

This article investigates the adaptive resilient event-triggered control for rear-wheel-drive autonomous (RWDA) vehicles based on an iterative single critic learning framework, which can effectively balance the frequency/changes in adjusting the vehicle's control during the running process. According to the kinematic equation of RWDA vehicles and the desired trajectory, the tracking error system during the autonomous driving process is first built, where the denial-of-service (DoS) attacking signals are injected into the networked communication and transmission. Combining the event-triggered sampling mechanism and iterative single critic learning framework, a new event-triggered condition is developed for the adaptive resilient control algorithm, and the novel utility function design is considered for driving the autonomous vehicle, where the control input can be guaranteed into an applicable saturated bound. Finally, we apply the new adaptive resilient control scheme to a case of driving the RWDA vehicles, and the simulation results illustrate the effectiveness and practicality successfully.

This article investigates the bipartite consensus problem for linear multiagent systems by the event-triggered output feedback control scheme. Both cooperative interaction and antagonistic interaction between neighbor agents are considered. Assuming that the system states are not available for measurement, the state observer is thus proposed to settle this scenario. Then, a novel observer-based bipartite control scheme is developed on the basis of two event-triggering mechanisms. One is designed for the communication between neighbors and another is for controller updates. Different from the existing methods, the proposed control strategy does not need continuous updates, avoids continuous communication between neighbors, and is applicable for the signed communication topology. Moreover, we extend the results from the bipartite leaderless consensus to the bipartite leader-following consensus and the bipartite containment consensus. It is proven that the proposed controllers fulfill the exclusion of Zeno behavior in three consensus problems. Finally, three examples are provided to illustrate the feasibility of the theoretical results.

This paper investigates the situation awareness issue of power system with massive measured data. To address this issue, first, a graph-theory-based network partitioning algorithm is proposed to realize decentralized detection in a faster response speed, while using power flow characteristics highlights the independency of different groups. Further, a hierarchical event detection method is proposed to judge voltage change and locate event position according to spectral distribution change of established multidimensional matrix. With the proposed method, the system situation can be assessed and the knowledge of the system model is not required. In addition, the accurate result of weak event happened in system could also be obtained. The simulation results are presented to illustrate the effectiveness of the proposed detection method.

The high penetration of renewable energy sources always leads to the fluctuation of the droop coefficients which are designed in inverse proportion to their rated capacity, and power electronics devices are prone to static instability. Although the impedance-based approaches have been widely studied to deal with this problem, the stability-oriented droop coefficients region identification due to the fluctuation of renewable energy sources is not provided. Thus, this article proposes an impedance-based approach to assess the droop coefficients stability region in the power system consisting of numerous distributed generators (DGs). First, the modified phase margin and opposing argument (MPMOA) forbidden criterion is constituted to acquire the complementary space of the droop coefficients stability region. The MPMOA forbidden criterion is first transformed into the condition that the generalized return-ratio matrix is Hurwitz through mirror, rotation, and translation mapping. In comparison with the previous simplified stability criteria, the conservatism of the proposed criterion is reduced a lot. Thereafter, the droop coefficients stability region is directly calculated by the generalized return-ratio matrix and guardian map theory. Eventually, the simulation and experimental results are provided to validate the conservatism and effectiveness of the impedance-based stability region identification approach. Therein, the simulation results illustrate that the proposed droop coefficients stability operation criterion has lower conservatism than these of the previous simplified stability criteria. Furthermore, the simulation and experimental results illustrate that the proposed stability-oriented droop coefficients region identification approach can provide an effective parameter stability region.

By use of the method of sliding-mode control, the stabilization of the T-S fuzzy stochastic system with time delay is considered in this article. The characteristic of the proposed method is that the input matrices of the fuzzy system are not common. And the proposed control method of this article can resist the disturbance. First, a novel sliding-mode surface is introduced. Using the new surface, the stability of the fuzzy system can be studied even if the input matrices are not common. Second, a new lemma is given to judge if the surface can be utilized in the considered system. Third, a new control law is constructed according to the new sliding-mode surface. The control law is designed to deal with the problems produced by time delay and stochastic systems. At last, two simulations are provided. In this part, the input matrices of fuzzy systems are different from each other and there is a disturbance in this considered system. From the simulations, the results illustrate the validity of the proposed method.

This article addresses the decentralized control problem of continuous-time nonlinear large-scale interconnected systems by the means of echo state network (ESN). The interconnected terms between the subsystems are treated as the disturbances added to the system dynamics, then the control problem is solved by the proposed robust controllers. By designing a cost function for the nominal subsystems, the robust controller is obtained by solving optimal controllers. The stability of the interconnected systems is proved by a composite Lyapunov function. The online adaptive dynamic program (ADP) method is employed to solve the transformed optimal problem, where a single ESN is utilized to approximate the critic cost and control policy. The optimal control policies of all the isolated subsystems are obtained simultaneously. The closed-loop stability of the feedback nominal systems is also proved, in a uniformly ultimately boundedness (UUB) manner. In the simulation, a large-scale interconnected system with four subsystems is provided to verify the effectiveness of the proposed decentralized controllers.

This article is concerned with the optimal tracking control problem of the coupled Markov jump system (CMJS) by using the reinforcement learning (RL) technique. Based on the conventional optimal tracking architecture, an offline tracking iteration algorithm is first designed to solve the coupled algebraic Riccati equation that can hardly be solved by mathematical methods directly. To overcome the crucial requirements and existing shortcomings in the offline tracking method, a novel integral RL (IRL) tracking algorithm is first proposed for CMJS, which develops a transition-probability-free optimal tracking control scheme with a reconstructed augmented system and discounted cost function. Both the requirements of transition probability πij and system matrix Ai are avoided via the designed IRL algorithm. The stability and convergence of the novel schemes are proved by the Lyapunov theory, and the tracking objective is achieved as desired. Finally, we apply the designed algorithms in a fourth-order Markov jump control problem and the stochastic mass, spring, and damper system to track continuous sinusoidal waveforms, and the simulation results are provided to show the effectiveness and applicability.

This paper investigates the problem of adaptive fault-tolerant tracking control for the multiagent systems (MASs) under the time-varying actuator faults and bounded unknown control input of the leader. On the basis of the local state information of neighboring agents, an adaptive fault-tolerant control protocol, which consists of the adaptive estimation of faults, is constructed to compensate for the loss of actuator effectiveness in the leader-follower consensus of MASs. Moreover, the modification term in the adaptive estimation can avoid high-frequency oscillations. It is shown that the tracking errors converge to a neighborhood around the origin in the presence of actuator faults, and the performance of the tracking problem is improved. Furthermore, the protocol is distributed in the sense that the coupling gains are independent. Finally, two examples are given to show the effectiveness of the proposed control protocol.

In this paper, a novel robust controller for continuous stochastic polynomial fuzzy systems is investigated. The aim of the proposed method is to eliminate the restrictive assumptions that the local input matrix Bi must be uniform and the sliding mode surface proposed did not consider the stochastic perturbations, which are required in most existing results. At the same time, the proposed method could handle the system with matched external disturbances. First, a novel vector integral sliding mode surface (VISMS) is constructed according to the basis matrix B(x). The sliding mode surface parameter matrix K(x) can be obtained through the provided sum of squares conditions. Second, by using an improved Lyapunov method and a new proposed lemma, a novel sliding mode control law is designed to keep the state of the closed-loop systems on the VISMS approximately since the initial time. Third, a practical example and a numerical one are provided to illustrate the validity of the proposed approach.

This article is concerned with the periodic tracking control problem for nonlinear systems. First, the Takagi-Sugeno (T-S) fuzzy model is employed to describe the nonlinear control systems. Second, considering the partly unmeasurable states of the system, a novel fuzzy observer-based repetitive controller, which is the mixed controller of the fuzzy observer-based controller and the fuzzy repetitive controller, is designed to deal with the periodic tracking control problem. To reduce the conservatism and increase the feasible solution space of the stabilization conditions, a new fuzzy relaxed matrix technique is developed by introducing some relaxed matrices in the derivative of the fuzzy normalized membership function. Then, the fuzzy Lyapunov functional with an additional separation parameter and the augmented fuzzy matrix technique (the interactions of fuzzy observer subsystems) are proposed such that the delay-dependent stability condition of the closed-loop system in the form of linear matrix inequality is obtained with less conservatism. It is worth noting that due to introducing an additional parameter in the fuzzy Lyapunov functional, the fuzzy controller and fuzzy observer can be separately designed, which largely enhances the flexibility of design with low computational complexity. Finally, three examples are provided to illustrate the effectiveness and less conservatism of the proposed method.

In this paper, fault estimation (FE) and fault-tolerant control (FTC) are investigated for Takagi-Sugeno (T-S) fuzzy systems with multiple time-varying delays as well as actuator and sensor faults. Local nonlinear models and external disturbances are also considered. Inspired by the information from the (k-1)th induction FE, a novel observer is addressed to establish the kth error dynamics. The k-step induction FE observer can weaken the effect from input disturbances caused by the derivatives of actuator faults and can perform better FE subject to sensor and actuator faults and multiple time delays simultaneously. Compared with the existing results, the proposed observer can realistically better show the sizes and shapes of the actuator and sensor faults. In addition, according to online information from the k-step FE, an active dynamic output feedback fault-tolerant controller is proposed to make the closed-loop fuzzy system asymptotically stable. Moreover, the fuzzy Lyapunov-Krasovskii functional is developed by introducing some free-weighting matrices such that the delay dependent sufficient conditions are given in the form of a set of linear matrix inequalities (LMIs) with less conservatism for the existence of observer and fault-tolerant controller. At last, two simulation examples are given to prove the advantages and effectiveness of the approach given in this paper.

This article studies an event-triggered communication and adaptive dynamic programming (ADP) co-design control method for the multiplayer nonzero-sum (NZS) games of a class of nonlinear discrete-time wireless sensor-actuator network (WSAN) systems subject to input constraints. By virtue of the ADP algorithm, the critic and actor networks are established to attain the approximate Nash equilibrium point solution in the context of the constrained control mechanism. Simultaneously, as the sensors and actuators are physically distributed, a decentralized event-triggered communication protocol is presented, accompanied by a dead-zone operation which avoids the unnecessary events. By predefining the triggering thresholds and compensation values, a novel adaptive triggering condition is derived to guarantee the stability of the event-based closed-loop control system. Then resorting to the Lyapunov theory, the system states and the critic/actor network weight estimation errors are proven to be ultimately bounded. Moreover, an explicit analysis on the nontriviality of the interevent times is also provided. Finally, two numerical examples are conducted to validate the effectiveness of the proposed method.

In this article, a novel online decentralized event-triggered control scheme is proposed for a class of nonlinear interconnected large-scale systems subject to unknown internal system dynamics and interconnected terms. First, by designing a neural network-based identifier, the unknown internal dynamics of the interconnected systems is reconstructed. Then, the adaptive critic design method is used to learn the approximate optimal control policies in the context of event-triggered mechanism. Specifically, the event-based control processes of different subsystems are independent, asynchronous, and decentralized. That is, the decentralized event-triggering conditions and the controllers only rely on the local state information of the corresponding subsystems, which avoids the transmissions of the state information between the subsystems over the wireless communication networks. Then, with the help of Lyapunov's theorem, the states of the developed closed-loop control system and the critic weight estimation errors are proved to be uniformly ultimately bounded. Finally, the effectiveness and applicability of the event-based control method are verified by an illustrative numerical example and a practical example.

This article addresses the adaptive bipartite event-triggered output consensus issue for heterogeneous linear multiagent systems. We consider both cooperative interaction and antagonistic interaction between neighbor agents in both fixed and switching topologies. An adaptive bipartite compensator consisting of time-varying coupling weights and dynamic event-triggered mechanism is first proposed to estimate the leader's state in a fully distributed manner. Different from the existing methods, the proposed compensator has three advantages: 1) it does not depend on any global information of the network graph; 2) it avoids the continuous communication between neighbor agents; and 3) it is applicable for the signed communication topology. Assume that the system states are unmeasurable, and we thus design the state observer. Based on the devised compensator and observer, the distributed control law is developed such that the bipartite event-triggered output consensus problem can be achieved. Moreover, we extend the results in fixed topology to switching topology, which is more challenging in that state estimation is updated in two cases: 1) the interaction graph is switched or 2) the event-triggered mechanism is satisfied. It is proven that no agent exhibits Zeno behavior in both fixed and switching interaction topologies. Finally, two examples are provided to illustrate the feasibility of the theoretical results.

In this article, the H∞tracking control problem for a class of discrete time-delay systems is studied using databased adaptive dynamic programming (ADP) algorithm. First, the controlled system and the reference system are combined to form an augmented discrete time-delay system. Second, we transform it into a discrete time-delay system represented by system measured data, which allows the system state to be completely replaced by the data generated during system operation. Third, a novel data-based Bellman equation is derived according to the Bellman optimality principle. Then, an ADP-based H∞tracking control method is designed by means of the measured data. The simulation example demonstrates the effectiveness of the data-based ADP control method proposed in this article.

In this paper, a novel adaptive dynamic programming (ADP) algorithm is developed for the infinite-horizon (H∞) optimal control problems with unknown continuous-time (CT) nonlinear systems subject to external disturbances. To facilitate the implementation of the algorithm, generalized fuzzy hyperbolic models (GFHMs) are utilized to establish an identifier-critic architecture, where the identifier is designed to reconstruct the unknown system dynamics, and the GFHM-based critic network is employed to approximate the value functions. The CT H∞optimal control issue is converted into a two-player zero-sum game and the corresponding Hamilton-Jacobi-Isaacs equation is derived. The learning procedure of the critic design is adaptively implemented with the help of the reconstructed model, thus the requirement of the complete system dynamics is relaxed. Furthermore, by the means of Lyapunov direct method, the uniform ultimate boundedness stability analysis of the closed-loop control system is explicitly provided. Finally, to compare the control performances and disturbance attenuation properties of the proposed method and the existing ADP algorithms, two numerical examples are given.

In this paper, a decentralized neural network (NN) output feedback fault tolerant control (FTC) problem is addressed for a class of multi-input multi-output systems with actuator fault. In order to avoid the noncausal problem, the original system is transformed into an input-output expression in accordance with the diffeomorphism theory. Then, in order to establish a quick response to the fault, the fault tolerant controller with minimum learning parameters has been designed such that the semiglobal uniform ultimate boundedness of all the variables in the resulting closed-loop systems can be guaranteed. Finally, the output feedback FTC approach is applied to the interconnected CSTRs, and the comparisons with existing methods are provided to show the effectiveness of the proposed strategy.

In this paper, the stability problem of sampled data Takagi-Sugeno fuzzy control systems with packet dropouts is investigated. A sampling-dependent time-varying Lyapunov function (SDTVLF) is constructed to analyze the stability problem of the system and a switched system approach is proposed to model the packet dropouts phenomenon. On this basis, by dividing the sampling input available interval and unavailable interval into several segments, the matrix functions of the SDTVLF are chosen to be continuous piecewise linear. Then, by using the proposed SDTVLF approach, computable convex conditions are obtained for the sampling input unavailable interval and the sampling input available interval in framework of dwell time. By confining the sampling input unavailable interval with an upper bound and confining the sampling input available interval with a lower bound, the SDTVLF is always decreasing in all sampling input available and unavailable interval, which can make the sampled control system tolerate a larger packet dropout rate. A numerical example is provided to show the efficiency of the proposed results.

This article proposed a novel event-triggered integral sliding-mode control (ISMC) strategy for nonlinear system with disturbances via robust adaptive dynamic programming (ADP) considering control constrains. A mixed event-triggered ISMC scheme with two different trigger parts is developed and the optimal performance of sliding-mode dynamics with input constrains is ensured. By guaranteeing that the system trajectory converges to the sliding-mode surface with removing the input disturbances, a discontinuous part triggering rule is presented together with the existence analysis of a lower trigger interval time bound. Then, the optimal event-triggered control of sliding-mode dynamics is converted into a discounted factor-based H∞constrained control problem under continuous part triggering condition. To solve the event-triggered HJI equation, a critic-only neural network (NN)-based ADP scheme is developed by applying a concurrent learning. The NN weight is updated by analyzing the uniformly ultimately bounded (UUB) stability of sliding-mode dynamics considering the event-triggered condition via the Lyapunov technique. Finally, the validity of our control strategy is verified by simulation.

This article presents a novel event-driven guaranteed cost control method for nonlinear systems subject to actuator faults. For the purpose of handling the problem of actuator faults and obtaining the event-driven approximate optimal guaranteed cost control approach for general nonlinear dynamics, the reinforcement learning (RL) algorithm is utilized to develop a sliding-mode control (SMC) strategy. To begin with, the unknown faults can be estimated by designing a fault observer. Meanwhile, an SMC technique is presented aiming at countering the effect of abrupt faults. In addition, the optimal performance of the equivalent sliding mode dynamics is considered, then an event-driven guaranteed cost control mechanism is implemented by using RL principle. In the control process, a general cost function, which has a simpler structure, is given to reduce the computation complexity. At the same time, a modified cost function is approximated to obtain optimal guaranteed cost control by using a single critic neural network (NN). In addition, a modified weight update law for critic NN is presented to relax the persistence of excitation (PE) condition. Moreover, a newly triggering condition, which is easy to be implemented, is designed, and the critic NN update law makes sure that the system states are stable. Furthermore, in light of the Lyapunov analysis, it is demonstrated that the developed event-driven control method guarantees the uniformly ultimately bounded (UUB) property of all the signals. Finally, three simulation results are given to validate the designed control method.

In this paper, a data-based control method based on adaptive dynamic programming (ADP) algorithm is proposed for a class of discrete-time (DT) systems in the case of multiple delays. Data-based ADP method is implemented by virtue of the measured input and output data. The condition of the existence of the corresponding equivalent multiple delays system is derived according to the characteristics of time-delay system. A novel data-based state equation is developed that only composed of input and output data, which is very meaningful in practical applications. By using the data-based ADP method, the output feedback control problem is solved only by measuring the input and output of the system with multiple delays. The convergence proofs of the designed policy iteration and value iteration algorithms are given, respectively. The simulation example is presented to demonstrate the validity of the proposed data-based ADP method.

In this paper, a novel integral reinforcement learning method is proposed based on value iteration (VI) to design the$H_{\infty }$controller for continuous-time nonlinear systems subject to input constraints. To confront the control constraints, a nonquadratic function is introduced to reconstruct the${L_{2}}$-gain condition for the$H_{\infty }$control problem. Then, the VI method is proposed to solve the corresponding Hamilton–Jacobi–Isaacs equation initialized with an arbitrary positive semi-definite value function. Compared with most existing works developed based on policy iteration, the initial admissible control policy is no longer required which results in a more free initial condition. The iterative process of the proposed VI method is analyzed and the convergence to the saddle point solution is proved in a general way. For the implementation of the proposed method, only one neural network is introduced to approximate the iterative value function, which results in a simpler architecture with less computational load compared with utilizing three neural networks. To verify the effectiveness of the VI-based method, two nonlinear cases are presented, respectively.

In this paper, a novel event-sampled robust optimal controller is proposed for a class of continuous-time constrained-input nonlinear systems with unknown dynamics. In order to solve the robust optimal control problem, an online data-driven identifier is established to construct the system dynamics, and an event-sampled critic-only adaptive dynamic programming method is developed to replace the conventional time-driven actor-critic structure. The designed online identification method runs during the solving process and is not applied as a priori part for the solutions, which simplifies the architecture and reduces computational load. The proposed robust optimal control algorithm tunes the parameters of critic-only neural network (NN) by event-triggering condition and runs in a plug-n-play framework without system functions, where fewer transmissions and less computation are required as all the measurements received simultaneously. Based on the novel design, the stability of system and the convergence of critic NN are demonstrated by Lyapunov theory, where the state is asymptotically stable and weight error is guaranteed to be uniformly ultimately bounded. Finally, the applications in a basic nonlinear system and the complex rotational-translational actuator problem demonstrate the effectiveness of the proposed method.

This paper aims at investigating the master-slave quasi-synchronization of delayed memristive neural networks (MNNs) by proposing a region-partitioning-dependent intermittent control. The proposed method is described by three partitions of non-negative real region and an auxiliary positive definite function. Whether the control input is imposed on the slave system or not is decided by the dynamical relationships among the three subregions and the auxiliary function. From these ingredients, several succinct criteria with the associated co-design procedure are presented such that the synchronization error converges to a predetermined level. The proposed intermittent control scheme is also applied to the event-triggered control, and an intermittent event-triggered mechanism is devised to investigate the quasi-synchronization of MNNs correspondingly. Such mechanism eliminates the events in rest time, and then it reduces the amount of samplings. Finally, two illustrative examples are presented to verify the effectiveness of our theoretical results.

This paper investigates the finite-time decentralized control problem for interconnected systems with discontinuous interconnections. By using the interval type-2 Takagi-Sugeno (IT2 T-S) fuzzy model, a unified IT2 T-S fuzzy interconnected system is provided, in which the global system is described as a fuzzy blending of local subsystems under IF-THEN rules. In addition, based on the differential inclusion theory, the solutions of such discontinuous system are defined in the sense of Filippov. In order to stabilize the considered system in finite time, several decentralized discontinuous state feedback controllers are proposed. Furthermore, by the finite-time stabilization theory and generalized Lyapunov functional method, decentralized control is carried out and several sufficient criteria are derived to ensure the finite-time stabilization of the concerned system. Correspondingly, the settling times for stabilization are given. Finally, the proposed methodology is illustrated by an example.

In this paper, the H∞consensus problem for linear heterogeneous multiagent systems (LHMAS) based on eventtriggered output feedback control is investigated. Two novel event-triggered control schemes including nonperiodic and periodic event-triggered control approaches are provided to make the LHMAS with external unknown disturbance achieve H∞consensus. Therein, the nonperiodic event-triggered control method is designed by combining the event-triggered approach with time-triggered scheme, which can make the sampled instants be decided by the event-triggered condition and provide a fixed lower bound of sampled interval to avoid the Zeno-behavior. Then, based on this approach, the periodic event-triggered control scheme has a further improvement that only the information at fixed periodic interval is used for the event-triggered condition, which means that this method avoids the continuous-time information transfer. Besides, both schemes take output feedback control, which means that only the output information of each agent and leader is needed in this paper. Finally, a numerical example is given to support our results.

This paper studies the H∞consensus problem for linear heterogeneous discrete-time multiagent systems (MASs). For a special kind of nonlinear matrix inequality, a novel method is provided to make these inequalities turn into some equivalent conditions that can be solved by utilizing the LMI toolbox. According to this method, a necessary and sufficient condition of H∞consensus for linear heterogeneous discrete-time MAS with output feedback control scheme and a corresponding iterative algorithm are proposed, respectively. Moreover, by employing these results, a necessary and sufficient condition that makes a general linear system with one kind of constrained controller achieve H∞control is provided. Finally, two numerical examples are given to illustrate the validity of our schemes.

A distributed optimization problem based on a continuous-time multiagent system (MAS) disturbed by general noise is considered in this paper. The general noise, under some relaxed assumptions, which may be a stationary process, is proposed to describe the disturbance among agents more accurately. The noise-to-state (NOS) stability of the concerned MAS is analyzed based on an improved theoretical result of random differential equations. Furthermore, the relative sufficient conditions in the form of linear matrix inequality are developed with less conservatism, from which the minimum estimation error between the optimal solution and the NOS stable state of the proposed MAS with general noise can be obtained by choosing some appropriate distributed optimization parameters. One example is used to verify the effectiveness of the proposed approach.

An adaptive fuzzy tracking control is investigated for a category of multiple-input multiple-output switched uncertain nonlinear systems in strict-feedback form. The unknown nonlinear function and switching signals with average dwell time are contained in the systems. The control method is designed by generalized fuzzy hyperbolic model and backstepping technique. Through adding a first-order filter into conventional backstepping method, the phenomenon of “explosion of complexity” is eliminated. In the controller design process of each subsystem, only one adaptive parameter is needed to adjust online. Compared with other existing control methods, the proposed control design is more simple and calculated amount is greatly decreased. It is guaranteed that the whole closed-loop system is stable by using Lyapunov function and average dwell-time methods. The effectiveness of the proposed control method can be demonstrated in the simulation part.

This paper investigates the problem of fault detection for uncertain switched fuzzy systems with unmeasurable premise variables. A dissipativity-based fault detection observer is designed. The observer order can be adjusted within a certain range. By solving a set of linear matrix inequalities, the sufficient condition can be obtained to ensure that under arbitrary switching signal, the residual dynamic is strictly (Q, S, R)-κ-dissipative. Furthermore, the elaborate H∞. performance and the elaborate H_ performance are obtained, which can be treated as expansions of the traditional H∞. performance and H_ performance. The parameter uncertainties are decoupled from the residual. Simulation results are presented to verify the proposed techniques.

This paper investigates the finite-time stabilization for a class of interconnected systems with nonlinear discontinuous interconnections in which the time-varying delay are considered. By utilizing the universal approximation ability of the fuzzy model, a unified interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy-model-based interconnected delayed system is provided. Then, in order to solve the existence of solution for the concerned system with discontinuous right-hand side, the Filippov solutions are defined based on differential inclusion theory and set-valued analysis. Furthermore, by the IT2 T-S fuzzy model approach, a delayed state feedback controller equipped with discontinuous term and time-varying delays term is proposed. According to the classical finite time stability theory and generalized Lyapunov approach, finite-time stabilization for the discontinuous interconnected delayed system is achieved, and the estimate of settling time is given. Moreover, when the detailed information of time-varying delays is unknown, the finite-time stabilization is also realized via another improved controller, which only depends upon the upper bound of time-varying delays. Finally, the proposed methodologies are illustrated by a numerical example.

In this paper, by incorporating the event-triggered mechanism and the adaptive dynamic programming algorithm, a novel near-optimal control scheme for a class of unknown nonlinear continuous-time non-zero-sum (NZS) differential games is investigated. First, a generalized fuzzy hyperbolic model based identifier is established, using only the input-output data, to relax the requirement for the complete system dynamics. Then, under the event-based framework, the coupled Hamilton-Jacobi equations are derived for the multiplayer NZS games. Then, the adaptive critic design method is employed to approximate the optimal control policies; thus, an identifier-critic architecture is developed to obtain the event-triggered controller. By the virtue of the Lyapunov theory, a state-dependent triggering condition, which is different from the existing works, is developed to achieve the stability of the closed-loop control system both for the continuous and jump dynamics. Finally, two numerical examples are simulated to substantiate the feasibility of the analytical design.

This paper investigates the problem of designing a robust controller for continuous polynomial fuzzy systems with external disturbances. The input matrix Bi(x) of the considered system is not equal to one another. To solve this problem, first, we rewrite every column vector of input matrix Bi(x) using the vectors of the basis matrix B(x). Second, by use of the column vector of B(x), the subsliding mode surface sp(t) is designed. Based on the rewritten system, a novel sliding mode surface s(t) is devised. The sliding mode surface parameter matrix can be characterized in terms of the solution of the provided sum of squares conditions. Then, a sliding mode controller is proposed to stabilize the considered system. It could make the state of the considered system drive onto s(t) and avoid the state escaping from the surface in the subsequent time. A lemma is provided to obtain the final result. Finally, practical and numerical examples are provided to demonstrate the validity of the proposed approach.

In this paper, a fuzzy reinforcement learning (RL)-based tracking control algorithm is first proposed for partially unknown systems with actuator faults. Based on Takagi-Sugeno fuzzy model, a novel fuzzy-augmented tracking dynamic is developed and the overall fuzzy control policy with corresponding performance index is designed, where four kinds of actuator faults, including actuator loss of effectiveness and bias fault, are considered. Combining the RL technique and fuzzy-augmented model, the new fuzzy integral RL-based fault-tolerant control algorithm is designed, and it runs in real time for the system with actuator faults. The dynamic matrices can be partially unknown and the online algorithm requires less information transmissions or computational load along with the learning process. Under the overall fuzzy fault-tolerant policy, the tracking objective is achieved and the stability is proven by Lyapunov theory. Finally, the applications in the single-link robot arm system and the complex pitch-rate control problem of F-16 fighter aircraft demonstrate the effectiveness of the proposed method.

This paper investigates the issues of day-ahead and real-time cooperative energy management for multienergy systems formed by many energy bodies. To address these issues, we propose an event-triggered-based distributed algorithm with some desirable features, namely, distributed execution, asynchronous communication, and independent calculation. First, the energy body, seen as both energy supplier and customer, is introduced for system model development. On this basis, energy bodies cooperate with each other to achieve the objective of maximizing the day-ahead social welfare and smoothing out the real-time loads variations as well as renewable resource fluctuations with the consideration of different timescale characteristics between electricity and heat power. To this end, the day-ahead and real-time energy management models are established and formulated as a class of distributed coupled optimization problem by felicitously converting some system coordinates. Such problems can be effectively solved by implementing the proposed algorithm. With the effort, each energy body can determine its owing optimal operations through only local communication and computation, resulting in enhanced system reliability, scalability, and privacy. Meanwhile, the designed communication strategy is event-triggered, which can dramatically reduce the communication among energy bodies. Simulations are provided to illustrate the effectiveness of the proposed models and algorithm.

The limited fault information caused by small fault data samples is a major problem in wind turbine (WT) fault detection. This paper proposes a small-sample WT fault detection method with the synthetic fault data using generative adversarial nets (GANs). First, based on prior knowledge, a rough fault data generation process is developed to transform the normal data to the rough fault data. Second, a rough fault data refiner is developed by GANs to make the rough fault data more similar with the real fault data. Moreover, to make the generated data better suited to the WT conditions, GANs are improved in both the generative model and the discriminative model. Third, artificial intelligence (AI)-based WT fault detection models can be well trained by using only the generated data in the condition of small fault data sample. Finally, three groups of generated data evaluation experiments and four groups of WT fault detection comparative experiments are conducted using real WT data collected from a wind farm in northern China. The results indicate that the method proposed in this paper is effective.

In most current nondestructive testing systems, a magnetic flux leakage (MFL) method is widely used in various industry fields, where the structural integrity of specimens is of vital importance. The estimation of defect size in specimen from the MFL measurements is a key and difficult problem. The traditional methods have low precision because feature extraction procedure relies on prior knowledge and the ability of designer. Inspired by the idea of convolutional neural network (CNN), a novel visual transformation CNN (VT-CNN) is proposed in this paper to overcome the limitation of traditional method in a feature extraction procedure. By adding a visual transformation layer according to the characteristics of the MFL measurements, the VT-CNN can distinguish the defect feature with different sizes more accurately. Moreover, since the VT-CNN method is designed based on the deep learning theory, more industrial big data with accurate label should be used to train the network. Due to the difficulty of making real industrial big data, a novel mesher magnetic dipole model is designed to simulate this industrial process. A large simulated MFL measurement of irregular defects produced by this model can increase the number of training samples and improve the robustness of the network. Experiments to estimate natural corrosion defects on real industrial pipelines are performed to validate the proposed framework. The experimental results are illustrated in detail, which highlights the superiority of the proposed method in industrial applications.

This paper investigates the economic dispatch problem of microgrids in a distributed fashion. To address this issue, a delay-free-based distributed algorithm is presented to optimally assign the whole energy demand among local generation units with the objective of minimizing the agminated operation cost. By implementing the proposed algorithm, each component can find its own optimal operations by only requiring local computation and communication. As a result, it enhances the system robustness, flexibility, privacy, etc. More importantly, the time-varying delays model is considered and embedded into the design of our distributed algorithm, such that the components can employ the delays information to achieve the collaborative operation, which are more general and applicable for practical power systems. In addition, we have proved that the proposed algorithm can converge to the global optimal point under some sufficient conditions. Finally, several simulations are provided to demonstrate the correctness and effectiveness of the proposed algorithm.

This paper investigates the robust control issues of nonlinear multiplayer systems by utilizing adaptive dynamic programming (ADP) methods and fills a gap in the ADP field, where actuator uncertainties for multiplayer systems are still not addressed. Two types of actuator uncertainties including bounded nonlinear perturbation and unknown constant actuator fault are taken into consideration. First, a data-driven reinforcement learning (RL) approach is derived to learn the optimal solutions of multiplayer nonzero-sum games. Then, based on the obtained optimal control policies, two robust control schemes are developed to handle these two different types of uncertainties, respectively, and the associated stability analysis is also provided. To implement the proposed iterative RL approach, a single neural network (NN) architecture with least-square-based updating law is given, which reduces the computation burden compared with the traditional dual NN architecture. Finally, two numerical examples are shown to test the feasibility of our proposed schemes.

This paper deals with the robust control issues for a class of uncertain nonlinear systems with completely unknown dynamics via a data-driven reinforcement learning method. Firstly, we formulate the optimal regulation control problem for the nominal system, and then, the robust controller for the original uncertain system is designed by adding a constant feedback gain to the optimal controller of the nominal system. Then, this scheme is extended to the optimal tracking control by means of augmented system and discount factor. It is also demonstrated that the proposed robust controller can achieve optimality with a new defined performance index function when there is no control perturbation. It is well known that the nonlinear optimal control problem relies on the solution of Hamilton–Jacobi–Bellman (HJB) equation, which is a nonlinear partial differential equation and impossible to be solved analytically. In order to overcome this difficulty, we introduce a model-based iterative learning algorithm to successively approximate the solution of HJB equation and provide its convergence proof . Subsequently, based on the structure of the model-based approach, a data-driven reinforcement learning method is derived, which only requires the sampling data from real system with different control inputs rather than the accurate mathematical system models. Neural networks (NNs) are utilized to implement this model-free method to approximate the optimal solutions and the least-square approach is employed to minimize the NN approximation residual errors. Finally, two numerical simulation examples are given to illustrate the effectiveness of our proposed method.

This paper investigates the fault detection problem for a class of switched nonlinear systems in the T-S fuzzy framework. The unknown input is considered in the systems. A novel fault detection unknown input observer design method is proposed. Based on the proposed observer, the unknown input can be removed from the fault detection residual. The weighted performance level is considered to ensure the robustness. In addition, the weighted performance level is introduced, which can increase the sensibility of the proposed detection method. To verify the proposed scheme, a numerical simulation example and an electromechanical system simulation example are provided at the end of this paper.

This paper addresses the problems of fault estimation and fault-tolerant control for switched fuzzy stochastic systems with actuator fault and sensor fault. A novel observer is proposed to estimate the system states, actuator, and sensor faults, simultaneously. The proposed observer can be treated as an extension of the traditional proportional-integral observer. The estimation information is utilized to design the fault-tolerant controller. Based on the piecewise Lyapunov function and the average dwell time, a set of linear matrix inequalities can be achieved, which ensure that the closed-loop system is mean-square exponentially stable with a weighted H∞performance level. At last, two simulation examples are provided to illustrate the effectiveness of the proposed approach.

This paper is focused on the data-driven model-free adaptive fault detection and estimation (FDE) and fault-tolerant control (FTC) problems for multi-input multi-output (MIMO) discrete-time systems with unknown sensor faults. First, in the light of the compact form dynamic linearization method, the initial systems are transformed into a novel data-based model with only one unknown parameter. Second, a fault estimator is established to detect the sensor faults. Noting that a time-varying residual threshold is developed to determine whether the sensor faults occur or not. Then, the unknown faults are approximated based on the powerful approximation capability of a generalized fuzzy hyperbolic model and the FTC approaches are reconstructed by applying the optimality criterion. In contrast to the previous schemes, the main contributions are twofold: first, it is the first time to solve the FDE and FTC problems for model-free MIMO discrete-time systems; second, the proposed FTC policy is simple to be implemented with reducing computational burden. Finally, two examples are used to demonstrate the effectiveness of the proposed FDE and FTC methods.

This paper studies a distributed machine learning problem by applying a distributed optimization algorithm over an undirected and connected communication network. Each node has its own fuzzy logic system (FLS) based machine whose weights are trained by the proposed FLS-based distributed cooperative learning (DCL) algorithm to reach the optimum of the global cost function. The training process utilizes the data that are distributed among different nodes and cannot be gathered at any node in the network. The main advantages of the FLS-based DCL algorithm are as follows: It has an exponential convergence; it requires a small amount of computation and communication at each iteration step; and the private and confidential information is protected without exchanging raw data between neighboring nodes. These advantages are verified by performing simulation experiments to compare the FLS-based DCL algorithm with the distributed average consensus based learning algorithm, the alternating direction method of multipliers based learning algorithm and the diffusion least-mean square algorithms.

This paper focuses on the problem of sensor fault estimation of switched fuzzy systems with unknown input. By treating the sensor fault as a virtual state, an augmented system can be obtained. A dynamic unknown input observer is designed for the augmented system to estimate the sensor fault and the system state simultaneously. Based on the average dwell time and the piecewise Lyapunov function, the observer parameter calculation method is presented in terms of solutions to a set of linear matrix inequalities. The unknown input is decoupled from the estimation error, and the effect of the disturbance is attenuated by the weighted H∞performance level. At last, simulation results are provided to illustrate the effectiveness of the proposed techniques.