This paper investigates the security path-tracking control problem for the connected vehicle swarm with uncertain dynamics, specifically emphasizing its resilience against dual-channel deception attacks. To ensure the security and reliability of data transmission against such cyber threats, a novel attack compensation mechanism is proposed to eliminate the impact of deception attacks by securing data transmission against signal tampering in both sensor-controller and controller-actuator channels. We design a fuzzy state estimator to reconstruct uncompromised signals from tampered data, thereby constructing a security control strategy resilient to deception attacks. Considering practical network bandwidth constraints, a fuzzy adaptive dynamic event-triggered path-tracking control strategy is developed, which integrates the proposed attack compensation mechanism and state estimator to mitigate the impact of cyber attacks. Sufficient conditions are established to guarantee the security path-tracking performance. With the Lyapunov stability theory, it is proved that error systems are uniformly ultimately bounded, and the Zeno phenomenon can be effectively prevented. Simulation results validate the effectiveness of the theoretical approach.

This work considers the security formation control for the autonomous mobile robot swarm with uncertain nonlinearity under dual-terminal false data injection attacks on both actuators and sensors. A feasible attack compensation mechanism is developed to eliminate the influence of tampering on the transmitted signals of actuators and sensors. Reconstruction of uncompromised signals is achieved through an adaptive neural network state estimator. Considering the limited network bandwidth, an event-triggered adaptive neural network security formation control strategy is proposed by integrating the compensation mechanism and the state estimator. Sufficient conditions are derived to ensure the uniformly ultimate boundedness of the formation error system. It is rigorously proven that the proposed method can avoid the Zeno phenomenon. Finally, simulation verification demonstrates the validity of theoretical results.

In this paper, an adaptive time-varying formation optimal tracking control strategy is presented for nonlinear heterogeneous multi-agent systems (MASs) with performance constraints and unknown dynamics. A prescribed-time performance function is designed to quantify the tracking error constraints, while an error transformation function is introduced to eliminate initial value constraints and address singularity issues. Notably, the settling time and initial conditions of the performance function are independent of both the initial tracking error and system parameters. To facilitate reinforcement learning (RL)-based optimal formation control, a performance index function is formulated that incorporates the transformed tracking error, control input, and an exponentially discounted term. This design effectively avoids infinite integral values and ensures minimization of the index function. Moreover, stability analysis demonstrates that the time-varying formation tracking errors converge to the desired accuracy within a prescribed time, regulated by a sliding-mode control mechanism. Finally, numerical simulations on two practical examples validate the feasibility and effectiveness of the proposed control strategy.

This paper focuses on the design of controller for trajectory tracking and obstacle avoidance in robot formations operating within confined spaces. Specifically, to achieve obstacle avoidance, we approximate obstacles and robots as convex polyhedra and utilize the Lagrangian dual function to design an enhanced discrete-time control barrier function (DCBF). Moreover, a discrete-time control Lyapunov function (DCLF) algorithm is employed to maintain the formation while tracking a predefined trajectory. This enables the natural unification of DCBFs with DCLFs within a quadratic program (QP) framework. The proposed method eliminates the nested optimization problem in the QP framework, and the solving time is significantly reduced. Theoretical analysis demonstrates the feasibility of the proposed method. Finally, simulations and hardware experiments are conducted to validate its effectiveness.

This article investigates the optimization consensus and constraint issues of multi-agent systems within a given time. By adopting a barrier function and the improved non-negative function, a new finite-horizon cost function is constructed. Based on this cost function, a simple single-critic network framework is proposed, which avoids the complex interaction and iterative update process between the actor and critic networks, thereby reducing the computational complexity. Under this framework, the temporal differential (TD) signal generated by the integral Bellman equation and the terminal constraint (TC) error are used to jointly guide the update of the critic learning law, and a concurrent learning technique is employed to adaptively adjust the weights without requiring persistent excitation conditions. The proposed adaptive dynamic programming (ADP) control strategy does not rely on the system model information of the controlled plant, enhancing the applicability of the algorithm in unknown dynamic environments. Furthermore, by incorporating the prescribed-time (PT) criterion into the optimization design process, a collaborative control protocol with explicit time constraints is designed, which strictly ensures that the controlled system achieves consensus within the specified time. At the same time, by adjusting the parameters of the PT criterion, the convergence rate and transient performance can be flexibly controlled. Finally, simulation results verify the effectiveness of the designed control algorithm and illustrate the impact of key parameters on system performance.

For interconnected nonlinear systems, the global continuous-time/event-triggered decentralized output feedback prescribed-time (PT) stabilization problem is discussed in this paper. A strategy employing dual time-varying gains with distinct properties is introduced to facilitate the design of the observer and controller. Using these gains within a nonrecursive design approach, a continuous-time decentralized output feedback PT controller is developed to achieve PT stabilization. Further, an event-triggered decentralized output feedback PT controller is constructed to reduce communication resources while fulfilling the control objective. A numerical example and a hardware-in-the- loop simulation of a 2-degree-of-freedom (DOF) helicopter system are employed to illustrate the availability of the decentralized output feedback PT controller.

This article investigates the finite-time stability (FTS) problem of switched nonlinear systems (SNSs) involving partially unstable modes under hybrid switching signals. In spite of significant challenge caused by unstable modes in SNSs, the qualitative analysis of FTS can still be conducted through the comprehensive utilization of switching information. To be specific, by constructing the discontinuous multiple Lyapunov function (DMLF) coupled with persistent dwell time (PDT)-based hybrid switching, a novel feasible solution for the FTS of SNSs is established. The “discontinuous” property of DMLF implies that certain state attenuation can be artificially designed at the switching instant to compensate for system divergence, and the proposed PDT-based switching employs respectively the τ -portion and theT-portion between stable and unstable subsystems to achieve FTS. Then, the FTS criteria are proposed in terms of stabilizing and destabilizing states during theT-portion, and the hybrid switching design scheme is established for stabilizing or destabilizing state during theT-portion. In particular, the overall settling-time upper bounds depending on both the initial state and the specific dwell time are further derived. Finally, two numerical examples including an application to the finite-time synchronization of switched complex dynamical networks are presented to demonstrate the practicality and effectiveness of the proposed theoretical results.

Existing approaches to multi-person pose tracking often suffer from low-confidence detections due to inter-instance and intra-instance occlusions, as well as non-canonical poses. In this work, we propose a novel solution by addressing two critical aspects: incomplete joint temporal dependencies and spatio-temporal voxelization. First, we introduce a method for extracting hierarchical relationships between joints based on human dynamics, enabling the model to reason about occlusions within the spatial topology of the human body. This hierarchical approach tackles incomplete joint visibility by leveraging the interdependencies between joints in both space and time. Second, we present a spatio-temporal occupancy network for multi-person pose tracking. By stacking 2D pose data over time to create a spatio-temporal voxel grid, the model captures temporal relationships between instances and joints, enhancing spatio-temporal correlations and learning keypoint distributions under occlusions or non-canonical poses. Extensive experiments on the PoseTrack2017, PoseTrack2018, and PoseTrack21 dataset demonstrate that our method improves multi-person pose tracking performance, achieving state-of-the-art mAP.

This article investigates the adaptive tracking control strategy for a class of nonlinear systems subjected to false data injection (FDI) attacks, incorporating an improved event-triggered mechanism. A significant breakthrough of this study lies in the challenge that, after FDI, not all states of the system can be utilized for stability design, thereby making it more complicated to achieve tracking control. This article eliminates the restrictive assumption, required in some existing results, that the attack signal at the first step must be known. Instead, we propose to estimate the tracking error directly. This approach not only facilitates the tracking control of nonlinear systems but also enhances the generalizability and practical applicability of the solution. To conserve system resources, an improved event-triggered condition is proposed that utilizes the triggered attacked-output. Consequently, the controllers and adaptive laws are implemented using the sampled states rather than continuous real-time states, thereby minimizing unnecessary computations and communications. By constructing Lyapunov functions, the proposed control strategy ensures that all signals in the closed-loop system are globally bounded. Finally, the simulation results are displayed to validate the effectiveness of the proposed control strategy.

This article concentrates on the attack detection and active attack defense strategies for discrete-time linear cyber-physical systems (CPSs) with unknown but bounded (UBB) disturbance and noise in the presence of both actuator and sensor attacks. First, a novel zonotopic observer is constructed to estimate the set-valued state and actuator attack by introducing augmentation techniques. To mitigate the effects of uncertainty and enhance estimation accuracy, the $H_{\infty }$ technique is introduced to construct the observer. Unlike most existing works, the constructed observer simultaneously estimates the system state and actuator attacks. Then, by combining the designed observer with reachability analysis, a set-valued abnormal detector and a residual-based abnormal detector are designed to detect actuator and sensor attacks, respectively. In addition, by incorporating the obtained state reachable sets and the $H_{\infty }$ technique, an active attack defense mechanism is designed to mitigate the impact of attacks on system performance. The proposed defense strategy does not introduce any performance loss in the absence of attacks. Finally, the superiority of the developed method is demonstrated by its application to a numerical simulation and an autonomous aircraft system.

This article proposes an adaptive fixed-time control strategy for uncertain high-order nonlinear cyber-physical system subject to deception attacks on both the sensor-to-controller (S-C) and controller-to-actuator (C-A) communication channels. First, to mitigate the impact of dual-channel attacks, mathematical tools are employed to decouple the high-order term induced by C-A channel attacks, and a robust controller is designed based on the compromised state information. Second, novel Lyapunov functions and an adaptive mechanism are constructed to transform nonlinear uncertainties into a linearly parameterized form with unknown parameters. This transformation effectively compensates for unknown control coefficients and mitigates the severe nonlinear growth caused by high-order dynamics under attacks. Third, a control strategy independent of the system's powers is developed, which relaxes the requirement for prior knowledge of system powers and avoids singularities. Finally, the effectiveness and feasibility of the proposed strategy are confirmed via simulation results.

This article addresses the problem of prescribed-time fault-tolerant tracking control for a class of nonlinear multiagent systems (MASs) subject to parameter uncertainties and external disturbances. To improve tracking precision, the trajectory reconstruction approach based on cubic spline interpolation is proposed, which effectively reconstructs the discrete reference signals. Then, a class of prescribed-time regulators is meticulously designed to formulate the fault-tolerant tracking controller, ensuring that the outputs of the controlled system converge to the reconstructed trajectory with arbitrary accuracy within the prescribed tracking time. Finally, stability analyses and a simulation example are presented to demonstrate the effectiveness of the proposed prescribed-time fault-tolerant tracking control strategy, validating its theoretical significance and practical applicability in engineering systems.

This article investigates the distributed approximate optimal control problem for pursuit-evasion differential games (PEDGs) of multiagent systems (MASs). Initially, interactions between pursuer agents and the evader agents are formulated using a divide-and-conquer algebraic graph approach, where all agents desire to maintain cohesion with their teammates. Subsequently, a state event-triggered mechanism (ETM) is introduced to conserve communication resources. Meanwhile, a polymeric hierarchical sliding mode surface (HSMS) incorporating local neighbor errors is constructed such that the system response rate is improved. To enhance team coordination, a novel dynamic target allocation algorithm is designed to execute the rational allocation among pursuers. Furthermore, based on the adaptive dynamic programming (ADP) with a single-critic neural network (NN) architecture, the HSMS-based event-triggered optimal control policies are further designed via solving the coupling Hamilton-Jacobi-Bellman (HJB) equations. Finally, a simulation conducted in the representative two-pursuer-two-evader scenario is presented to validate the effectiveness of the proposed control scheme.

This article investigates the issue of composite anti-disturbance and attack control for networked systems under unknown actuator attacks and external disturbances. First, a double-ended event-triggering mechanism is designed to reduce unnecessary data transmission in the feedforward and feedback channels. Second, an augmented observer is designed to estimate the system states and disturbances. The intermittent estimation signal is then used to compensate for external disturbances through a novel trigger sampling mechanism. Third, the fuzzy logic system is used to approximate the unknown attack signal, and an adaptive output feedback controller is employed to mitigate its impact on the system. Based on these three key techniques, a novel functional dependent on the sampling instants is constructed to analyze semi-global uniform ultimate boundedness of the closed-loop system and obtain the criterion condition of low conservatism. Additionally, the event-triggering matrix and gains are explicitly solved by matrix transformation. Finally, the feasibility and effectiveness of the proposed method are validated via a visual servo control system and a third-order system.

Applying deep reinforcement learning (DRL) to solve quantum control problems has become a popular research direction. However, the exploration capability and reward design for the learning agent, which usually affect the DRL’s application performance, has not been sufficiently emphasized. In this article, we propose a particle-assisted DRL (PDRL) method to address the above concern by enhancing exploration capabilities and designing appropriate reward functions for efficient quantum state manipulation. In PDRL, each episode in the quantum learning process is characterized by three kinds of events, i.e., unidentifiable, identifiable, and successful events. To improve exploration, exploration particles and feedback particles are employed in the early learning phase when episodes end in identifiable and successful events, respectively. To assign rewards, three event-based reward functions are provided for the DRL’s agent, exploration particles and feedback particles, respectively. Numerical results on single-qubit, two-qubit, and many-qubit systems validate the effectiveness of PDRL. Comparative results with existing DRL methods demonstrate the superior performance of PDRL for quantum state manipulation.

Robust multi-object tracking (MOT) is hindered by the instability of appearance features under visual corruptions such as occlusion and motion blur. These perturbations introduce high-variance noise into feature trajectories, weakening temporal representations and causing identity switches. We address this challenge by learning more stable appearance representations under feature corruption. To this end, we propose the Causal Interaction Module (CIM), a causal architecture that follows a filter then reconstruct design for online tracking. A temporal filtering stage summarizes the historical feature trajectory into a stable anchor, and a contextual enhancement stage uses that anchor to refine frame-level features before association. Integrated into standard trackers, CIM improves association robustness while preserving the host tracking formulation. Experiments on multiple MOT benchmarks and corruption stress tests show consistent gains, especially on association-related metrics.

Connected autonomous vehicles (CAVs) platoons rely on V2V communication and onboard sensing to maintain safe inter-vehicle spacing, yet cyberattacks on links and sensors can inject false data and destabilize platoon control. This paper proposes a hierarchical Secure Tiny Model Predictive Control (Secure TinyMPC) framework for real-time resilient platooning. A verifiable secret sharing (VSS)-based security layer distributes state shares across communication links to reconstruct trusted states and to support distributed, online detection and estimation of communication and radar attacks. A low-computation TinyMPC control layer then uses these trusted states to rapidly compute control inputs suitable for resource-limited onboard hardware. Simulation studies under diverse communication attacks and radar measurement tampering demonstrate accurate spacing regulation, fast speed convergence, and effective real-time distributed attack detection.

This article investigates iterative learning control of robot arms with initial errors and actuator faults. Resorting to backstepping technique, an actual controller that removes the iterative convergemt sequence is constructed to simplify the design. New updating laws with equilibrium factor are developed to treat the nonstrictnegative difference of composite energy function. A contraction mapping-based composite energy function method is executed to provide the convergence of errors. Finally, the validity of the proposed method is verified through a robot arm example.

This article investigates the problem of observer-based prescribed-time tracking control (OPTC) for a class of nonlinear systems with uncertain nonvanishing disturbances. First, a prescribed-time observer (PTO) with a well-designed time-varying high gain is constructed to achieve zero-error observation of the uncertain nonvanishing disturbances within the prescribed observation time. Subsequently, a two-stage controller design framework is proposed to construct the desired control input, which consists of two distinct components: the first-stage controller and the second-stage controller. The first-stage controller is established to compensate for the effects of the system’s nonlinear dynamics, thereby providing a relatively satisfactory tracking performance. The second-stage controller, on the other hand, is constructed based on a sophisticated switching mechanism of the control gains, which not only ensures that the tracking error converges to zero within the prescribed tracking time but also guarantees a smooth transition between the two-stage controllers. Finally, the simulation example involving a disturbed single-link flexible-joint robotic manipulator is presented to demonstrate the effectiveness of the proposed control strategy.

To enable electric semi-trailer trucks (STTs) to adapt to path-tracking scenarios with varying-curvature, this article proposes a dynamic event-triggered mechanism (DETM)-based robust switching control method to enhance the overall tracking accuracy and stability. First, the vehicle’s nonlinear dynamics model, brush tire model, and preview dynamics model are augmented into a coupled lateral dynamics model, which is linearized and then transformed into a linear parameter varying (LPV) model with time-varying motion state and tire parameters. Second, a robust switching controller is developed, incorporating two steering modes: a tractor-trailer coordinated steering system and a tractor independent steering system. Simultaneously, a DETM is introduced between the sensors and the controller to conserve communication resources. Then, considering the stability and robustness of the DETM-based robust switching controller against linearization errors, model uncertainties, environmental disturbances, DETM, and mode switching, a codesign criterion for the DETM and controller is established using Lyapunov stability theory, linear matrix inequality (LMI) techniques, and average dwell time (ADT) theory. Finally, hardware-in-the-loop (HIL) tests under comprehensive driving conditions validate the effectiveness and reliability of the proposed method.

This article is devoted to data-driven optimal control of linear discrete systems with sensor fault via a performance triggering approach. A quadratic inequality is introduced to equivalently describe systems subject to a fault. A barrier function is provided by the input and output to treat the gain constraint. An optimal control law is built on the adaptive dynamic programming (ADP) method to improve control performance. A dynamic triggering mechanism is constructed by instantaneous data and performance index to balance triggering frequency and control performance, especially under an emergent situation. Sufficient conditions are supplied to ensure the ultimately uniform boundedness of the closed-loop systems. An illustrative example is presented to verify the validity of the proposed strategy.

This article proposes a decentralized resilient adaptive intermittent output feedback fault-tolerant control (FTC) method for interconnected nonlinear systems with sensor deception attacks, multiple actuator faults, and input saturation. First, we constructed a new composite observer using intermittent compromised output signals and fuzzy logic systems (FLSs) to estimate the state variables in the face of deception attacks and unknown external disturbances. On this basis, the reasonable adaptive laws are cleverly devised to handle multiple unpredictable actuator faults. Then, in order to deal with unknown gains caused by deception attacks, a projection operator-based compensation mechanism is proposed. In addition, the “explosion of complexity” in the traditional backstepping design process is solved through command filtering technology. At the same time, an error compensation system and an auxiliary system are designed to reduce the errors caused by command filtering and solve the input saturation problem, respectively. The results indicate that the proposed control scheme can ensure the boundedness of all signals in the closed-loop system, and the tracking errors converge to a bounded compact set. Finally, the effectiveness of the proposed strategy was verified through numerical simulation.

This work concentrates on the predefined-time and predefined-accuracy tracking controller design problem for a nonstrict-feedback nonlinear system under asymmetric output constraints. Unlike finite-time or fixed-time control strategies in related literature, the great advantage of our presented controller design algorithm is that the algorithm can specify the convergence time and the accuracy of the system in advance, depending on the user’s requirements. First, an extremely important performance of RBF NNs is employed to handle the design obstacle caused by nonstrict-feedback structure. Then, a time-varying nonlinear transformation function is introduced to handle the asymmetric output constraints problems. Second, under the framework of adaptive backstepping design, we propose the predefined-time controller and the asymptotic tracking controller simultaneously, and a continuous switching function is used to transition the predefined-time controller to the asymptotic tracking controller when the resulting tracking error arrives at the specified accuracy. Under our control scheme, it is rigorously demonstrated that all signals of the studied system remain bounded and the tracking error achieves a predefined accuracy within a preset time. Finally, two practical simulation experiments are given to validate the effectiveness of the controller.

This article studies the optimal cluster synchronization (OCS) problem for multiagent systems (MASs) with the unknown perturbation under a multicluster game (MCG) framework. To estimate the unknown perturbation, a novel perturbation observer nested with a parameter adaptive law is first designed. Subsequently, a coupling performance index function relevant to the synchronization error and the control policy with the quadratic form is constructed. By utilizing the distributed adaptive dynamic programming (ADP) technology with a single-critic architecture, the optimal control policy is designed by solving the Hamilton–Jacobi–Bellman (HJB) equation associated with the coupling performance index function. Meanwhile, an adaptive OCS control policy with a single-critic neural network (NN) updating law is further proposed, and it is proven that the designed adaptive OCS control policy constitutes the generalized Nash equilibrium (GNE) point of the MCG. Based on the Lyapunov extension theorem, all signals of the closed-loop MASs are ensured to be bounded. Finally, a simulation example is presented to validate the effectiveness of the proposed adaptive OCS control method.

The RAW domain image super-resolution faces two critical challenges: the physical impossibility of capturing native high-quality RAW references with a resolution-limited camera and the limitations of neural networks, including inefficient residual layer utilization and spectral bias in feature learning. This paper proposes a strategy combining physics-based imaging simulation and neural networks to jointly address these challenges. First, we develop a rapid imaging simulation system based on our proposed subgraph decomposition technology. It generates camera-specific degraded and clean RAW image pairs at multiple resolutions. Second, we design a LatentKAN network, featuring an iterative feature fusion network that extracts additional beneficial information through stage-wise supervision and a multi-layer Kolmogorov Arnold network that suppresses spectral bias via learnable activation functions. Ultimately, our strategy demonstrates significant advantages, achieving an average 0.8 dB PSNR improvement across all SR scales compared to state-of-the-art methods, thereby establishing a new paradigm for camera-specific super-resolution tasks.

This paper studies the problem of dynamic periodic event-triggered control (DPETC) for networked control systems subject to external disturbances, quantization errors and time-varying delays. The developed DPETC scheme has potential in making full use of scarce network resources. Moreover, to cope with the quantization effect, an improved dynamic quantization scheme is innovated, considering both the encoder and decoder processes to ensure that a finite number of bits are transmitted at each transmission instant. With the help of a novel constructed Lyapunov function, sufficient conditions are derived and an appropriate dynamic periodic event-triggering mechanism is designed to guarantee the input-to-state stability in the neighborhood of the origin. Furthermore, the number of zoom actions of quantizer parameter, the upper bounds on the maximally allowable delay and the maximally allowable transmission interval are also provided. Finally, effectiveness of the theoretical results is illustrated by a numerical example.

It is difficult to ensure tracking performance even for general nonlinear systems under the condition that only intermittent output signals are used. This paper studies the problem of adaptive fuzzy consensus tracking for a class of mismatched nonlinear strict-feedback multi-agent systems (MASs) by using intermittent output signals. Firstly, a novel fuzzy state observer using intermittent output states and fuzzy-logic systems is constructed to estimate the unknown state variables of the system. Secondly, dynamic filtering technology is applied to address the issue of virtual controller non-differentiability caused by intermittent output feedback during the backstepping design process. Furthermore, for the situation where the controller is non-differentiable in stability analysis, an alternative solution can be proposed: initially, a distributed continuous control strategy is developed using conventional continuous output signals, and then the output signals in the previous scheme are replaced with event-triggered signals to design a distributed event-triggered control scheme. It is demonstrated that the designed event-triggered control scheme guarantees that all closed-loop system signals remain semi-globally uniformly ultimately bounded (SGUUB), and the distributed consensus tracking errors can converge to a neighborhood of zero. Finally, the effectiveness of the proposed scheme is verified through simulations involving a group of single-link robots.

In this paper, an observer-assisted model free adaptive predictive control algorithm is designed for nonlinear heterogeneous multi-agent systems under denial-of-service (DoS) attacks. To begin with, consensus error of the system is transformed into an equivalent linear data model at the operating point by means of a dynamic linearisation technique. Thereafter, in order to conserve network communication resources, a dynamic event-triggering mechanism is devised for the consensus error and an observer is incorporated to facilitate the acquisition of a time-varying parameter called "pseudo partial derivative" in the linear data model. Subsequently, to mitigate impact of random DoS attacks, we introduce an attack compensation strategy that allows each agent’s output to swiftly revert to the neighbourhood of leaderless consensus objective in DoS-free intervals. Finally, the pseudo partial derivative parameter estimates, observation errors, and consensus errors are rigorously proved to be bounded in mean using the contraction mapping principle. Simulation results on heterogeneous nonlinear multi-agent systems demonstrate that, compared with conventional event-triggered model free control, the proposed approach improves the convergence rate by 33.82%, reduces steady-state consensus error by 93.80%, and decreases pre-convergence fluctuations by 21.91%. Furthermore, it achieves a 64.00% faster recovery speed and a 95.98% higher recovery accuracy after DoS attacks. Meanwhile, the average communication load is reduced by 59.79%.

This paper focuses on the distributed event-triggered adaptive neural resilient time-varying formation control problem for a class of multiple-input multiple-output nonlinear multi-agent systems, where all network communication links between agents are subjected to denial-of-service (DoS) attacks simultaneously. A second-order resilient time-varying formation estimator is designed to obtain the unknown leader information in DoS attack active intervals. Meanwhile, a state-triggering mechanism (STM) is designed to save system communication resources. Nevertheless, the STM can lead to virtual control laws being non-differentiable. To circumvent the problem, we first design an adaptive neural resilient formation control scheme. Then, based on the adaptive neural resilient formation control scheme, we replace continuous states with intermittent ones. By utilizing a dynamic filtering technique, an event-based adaptive neural resilient formation control scheme is designed. The key technology of control scheme design is to establish an improved first-order auxiliary system to deal with the negative impact of actuator saturation. It is proved that formation tracking errors can converge to a residual set around zero, and all signals in the closed-loop system are semi-globally uniformly ultimately bounded. Finally, simulation results are presented to show the effectiveness of the control scheme.

This paper investigates the event-triggered secure control problem for fuzzy Markov jump systems (MJSs) with time-varying delays. In order for higher resource efficiency, a multi-level probabilistic event-triggered mechanism (MLPETM) is newly established, which reduces the data transmission frequency by investigating possible variations of triggering thresholds. Given the network-induced phenomena, the MLPETM’s output may undergo cyber-attacks, and the controller may not switch synchronously along with the system. Asynchronous secure controllers are thus developed to perform secure control of fuzzy MJSs. Sufficient conditions dependent on fuzzy basis functions, state delays and jumping modes are established to guarantee the dissipative finite-time boundedness of the resultant closed-loop system. Afterward, an easy-to-check fuzzy secure control algorithm is developed to compute the controller gains. Finally, the one-link robotic arm system and the truck-trailer system are borrowed to validate the proposed results. Note to Practitioners—Networked control systems (NCSs) play a key role in real applications like automobile industry, power systems, and autonomous mobile robots. As a key part, the communication network such as Controller Area Network is embedded into the NCSs to perform the communication tasks between different control units. In spite of great benefits from the communication network, both the security and the efficiency issues of the data transmission are challenging for the engineering designer. Besides, the target physical system is usually nonlinear, and is very likely to undergo abrupt changes in the networked environment. In terms of these challenges, this paper studies the asynchronous security control of networked Markov jump systems with time-varying delays and randomly occurring cyber-attacks. A multi-level probabilistic event-triggered mechanism (MLPETM) is newly established, which studies the effect of the stochastic variation regarding the triggering threshold on the triggering performance. Potential cyber-attacks are modelled in a randomly occurring fashion and then absorbed into to the control law which allows operation modes asynchronous to the system modes. Finally, the one-link robotic arm system and the truck-trailer system are borrowed to check the proposed findings. Note that both the transmission security and communication traffic regulation are well concerned, which renders the developed method applicable to practical systems.

This work considers the adaptive output feedback consensus asymptotic tracking control problem for full-state-constrained nonlinear multiagent systems (MASs) under the denial-of-service (DoS) attacks and the actuator faults. Since MASs are regardered as a network, where each agent is considered as a network node, the DoS attacks that cut network communication links between agents are studied in this paper. Moreover, to compensate the unknown actuator fault signal, the Nussbaum function method is introduced into the controller design process for each agent. Due to the researched nonlinear MASs contain unknown nonlinearities and the system states of each agent are unmeasurable, an estimator based on fuzzy logic systems (FLSs) is constructed for each agent. Further, the secure control strategy based on the barrier Lyapunov functions (BLFs) is proposed for the researched nonlinear MASs, which guarantees the consensus tracking errors asymptotically converge to zero, all closed-loop signals remain bounded and the full-state constraint requirements are not broken. In the illustrative example, the proposed secure control strategy is applied to the MASs composed of multiple forced damped pendulums (FDPs).Note to Practitioners— In the framework of network communication, how to eliminate the impact of cyber attacks on nonlinear MASs that rely on communication links to transmit information is critical. Furthermore, due to practical systems are inevitably subject to multiple physical and environmental constraints, the problem of dealing with system constraints can not be ignored. Therefore, a secure control algorithm based on the BLFs is proposed to eliminate the effects of the DoS attacks on the researched nonlinear MASs and ensure that the full-state constraints are not broken. To realize the output feedback control and approximate the unknown nonlinearities of the studied system, the estimators combining with the intelligent approximation techniques based on FLSs are developed. The Nussbaum function is introduced into controller to solve the actuator faults problems for each agent.

This paper studies the transient-state control of linear switched aero-engines with input saturation. Firstly, by formulating the problem as an input-to-state stability (ISS) property investigation of switched linear aero-engine models with saturated control input, a saturated controller coupled with an event-triggered mechanism (ETM) avoiding Zeno and asynchronous phenomena is developed. It shows that the ISS property of switched linear aero-engine models can be guaranteed utilizing the designed strategy. Then, in order to estimate the disturbance bound and the initial value domain, an optimization problem is proposed. Finally, an application study based on the GE-90K aero-engine is carried out to reveal the effectiveness of the proposed strategy. Simulation results show that the aero-engine can be transitioned to a desired steady-state point under the designed controller. In addition, comparisons and discussions demonstrate the advantages and potential significance of the work in practice.Note to Practitioners—Transient-state control is one of the most important problems in aero-engine control and affects the maneuverability of aircrafts. In addition, the saturation of controllers may lead to bad control performance. Thus, this paper is devoted to proposing a new control method for linear switched aero-engine models subject to input saturation. By designing a new controller coupled with an event-triggered mechanism, better transient-state performance compared with some existing methods, can be guaranteed.

This paper investigates the problem of adaptive output feedback control for uncertain nonlinear systems with input/output quantization subject to intermittent denial-of-service (DoS) attacks. When a DoS attack becomes active, the system is unable to obtain output signal. In order to address this challenge, we compensate the output signal by introducing an attack compensator. At the same time, a novel quantization compensator-based (QC) state observer is designed, which uses only the quantized compensated signal to reconstruct the system states. In addition, the problem of over-parameterisation is avoided by using parameter projection technique to design the parameter estimator. By combining Lyapunov stability theory and a modified average dwell time (ADT) approach, the stability of the closed-loop system is ensured. Furthermore, the proposed controller ensures that all closed-loop signals are bounded, and the control performance of mean-square error can be adjusted by appropriately selecting certain design parameters. Finally, the effectiveness of the proposed control strategy is validated through a single-link robot simulation results.Note to Practitioners—Considering that resources are limited, the DoS attacks mentioned in this paper are aperiodic, which means that attackers engage in frequent attacks within a relatively short time frame. This aligns more closely with real-world scenarios of network attacks. When the attack is active, the output signal is not available for the system. In this paper, a new QC state observer is designed, which in turn enables the estimation of the system state signals. Furthermore, this paper considers both input and output quantization, which reduces duplicate transmission of samples and improves the use of communication resources. This makes the work in this paper more relevant to the practical engineering context.

This article investigates the adaptive prescribed-time consensus tracking control problem for nonlinear multi-agent systems (MASs), where the states of systems are unmeasured and the actuators suffer from the deception attacks. Firstly, a novel coordinate transformation technology is developed by introducing a time-varying constraint function, such that the prescribed-time tracking control problem of nonlinear MASs is converted into the constraint problem of the error variables. Then, a new attack compensator is proposed to address the unknown time-varying attack gains caused by the actuator deception attacks. Further, the state observers are designed to estimate the unavailable state variables and fuzzy-logic systems (FLSs) are employed to handle the unknown functions that exist within the systems. In addition, the attack compensator-based controller ensures the boundedness of all signals, while the error variables converge to the predefined region in a specified time. The upper bound of the whole tracking errors in the mean square sense can be decreased by selecting the appropriate design parameters. At last, the simulation example illustrates the availability of the developed control method. Note to Practitioners—In the industry, consensus tracking control of nonlinear MASs exists in many different systems, such as mobile robot networks, intelligent transportation management, surveillance and monitoring. Since the above systems operate in a network environment, the security problems of the systems cannot be ignored. Hence, considering the unmeasured states, the unknown functions, and the unknown time-varying attack gains existing simultaneously in the studied systems, it is a challenging and meaningful task to achieve the desired security control objectives. On the other hand, based on a time-varying constraint function, this article presents an adaptive prescribed-time consensus tracking control scheme for the nonlinear MASs under the deception attacks. It provides a viable strategy for industrial applications.

This paper studies the adaptive fuzzy resilient fixed-time bipartite consensus tracking control problem for a class of nonlinear multi-agent systems (MASs) under sensor deception attacks. Firstly, in order to reduce the impact of unknown sensor deception attacks on the nonlinear MASs, a novel coordinate transformation technique is proposed, which is composed of the states after being attacked. Then, in the case of unbalanced directed topological graph, a partition algorithm (PA) is utilized to implement the bipartite consensus tracking control, which is more widely applicable than the previous control strategies that only apply to balanced directed topological graph. Moreover, the fixed-time control strategy is extended to nonlinear MASs under sensor deception attacks, and the singularity problem that exists in fixed-time control is successfully avoided by employing a novel switching function. The developed distributed adaptive resilient fixed-time control strategy ensures that all the signals in the closed-loop system are bounded and the bipartite consensus tracking control is achieved in fixed time. Finally, the designed control strategy’s validity is demonstrated by means of a simulation experiment.Note to Practitioners—Currently, there are many practical application scenarios for nonlinear MASs, such as intelligent transportation, unmanned aerial vehicle cluster formation, etc. This paper investigates the adaptive fuzzy resilient fixed-time bipartite consensus tracking control problem for a class of nonlinear MASs under sensor deception attacks. In practice, these two situations are common: 1) The topology graph describing the communication relationship of nonlinear MASs is unbalanced 2) The nonlinear MASs is subjected to external malicious cyber attacks. Therefore, a novel coordinate transformation technique is proposed to reduce the impact of sensor deception attacks on the nonlinear MASs, and a partition algorithm is employed to implement bipartite consensus tracking control based on the unbalanced communication topology graph. At the same time, the nonsingular fixed-time control strategy can significantly improve the convergence of the studied nonlinear MASs. Furthermore, the nonlinear nonstrict-feedback model and backstepping design method used in this paper are general and practical.

This paper focuses on the prescribed performance adaptive containment control problem for a class of nonlinear nonstrict-feedback multiagent systems (MASs) with unknown disturbances and full-state constraints. First, the radial basis function neural networks (RBF NNs) technology is employed to approximate the unknown nonlinear functions in the system, and the problem of “explosion of complexity” caused by repeated derivation of virtual controls is solved by using the dynamic surface control (DSC) technology. Then, the nonlinear disturbance observers are designed to estimate the external disturbance, and the barrier Lyapunov functions (BLFs) and the prescribed performance function (PPF) are combined to achieve the control objective of prescribed performance without violating the full-state constraints. The theoretical result shows that all signals in the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB), and the local neighborhood containment errors can converge to the specified boundary. Finally, two simulation examples show the effectiveness of the proposed method.Note to Practitioners—The containment control problem is a hot topic in the field of control, which plays an important role in practical engineering. Especially for this problem of nonlinear MASs, the mathematical models are difficult to be obtained accurately. This paper investigates the prescribed performance adaptive containment control problem for the nonlinear nonstrict-feedback MASs, whose model can be extended to more complex engineering applications, such as unmanned aerial vehicle formations and intelligent traffic management. It is worth noting that external disturbances and state constraint problems often exist in practical applications. Therefore, the disturance observers are designed to compensate for the system disturbances, which can eliminate the impacts of disturbances on the systems. By introducing BLFs, it is ensured that all states of the system are constrained within the specified regions. To sum up, the paper proposes a prescribed performance adaptive containment control strategy, which contributes to the development of containment control for MASs in practical applications.

Path following is a fundamental capability for autonomous surface vessels (ASVs). A typical path-following algorithm comprises two main modules: guidance and control. In the guidance domain, the vector field (VF) approach has been widely adopted due to its demonstrated superiority over alternative guidance strategies. Consequently, it has been successfully applied to a range of unmanned systems, including ASVs, airships, and quadrotors. However, most existing VF guidance laws are constrained to simple geometric paths, such as straight lines and circular orbits. Therefore, their practical applicability is limited in real-world engineering scenarios. To address this limitation, this paper introduces a novel, continuously differentiable VF capable of handling general curved paths, thereby significantly broadening the application scope of the VF methodology. In terms of control, a major challenge for ASVs lies in achieving long-endurance operation while maintaining robustness against uncertainties. The finite-time uncertainty observer (FTUO)-based adaptive dynamic programming (ADP) approach has been shown to be effective in addressing this challenge. Nevertheless, common FTUO-based ADP methods often suffer from drawbacks such as system chattering, observer peaking, and asymptotic convergence. In response to this situation, this paper proposes a modified FTUO-based ADP method for ASVs. This approach eliminates undesirable effects, i.e., chattering and peaking, and further saves control energy. Both simulation and experimental results verify the effectiveness and advantages of the proposed path-following algorithm.

This paper studies the event-triggered time-varying formation control problem for nonlinear multi-agent systems with actuator faults. Based on the neural network approximation technique, a neural observer is constructed to estimate the unmeasured states of systems. Then, a distributed adaptive event-triggered time-varying formation control manner is proposed utilizing the intermittent estimated states information from the agent and its neighbors. To overcome the problem that estimated states triggering leads to virtual control laws is non-differentiable, a distributed continuous control scheme under regular output-feedback is designed firstly, upon which a distributed event-triggered controller is constructed by replacing estimated states with intermittent estimated ones. It is shown that the designed event-triggered output-feedback time-varying formation fault-tolerant controller can compensate for actuator faults, and all signals in closed-loop systems are semi-globally uniformly ultimately bounded. Finally, simulation results of a practical example are given to verify the effectiveness of the proposed control manner.Note to Practitioners—Formation control has broad application prospects in modern military and civilian fields, such as combat aircraft flying formation, satellite formation, autonomous vehicle formation, etc. In formation control systems, when agents occur actuator faults, it may break the original formation and even cause collision between agents. As a result, the security of formation control systems is facing great challenges in practical engineering applications. On the other hand, communication bandwidth is limited in practical engineering systems, and how to make systems quickly form formation under the limited communication bandwidth has become a key topic. Inspired by the above discussions, a distributed state-triggered output-feedback time-varying formation fault-tolerant control scheme is designed in this paper, in which actuator faults are compensated by using adaptive technology. Meanwhile, to sufficiently save the usage of system communication resources, a dual-channel event-triggered mechanism is designed.

This article focuses on a dynamic event-triggered adaptive neural networks backstepping control for a class of uncertain strict-feedback systems with communication constraints. The uncertain terms including external disturbances and unknown nonlinear functions are approximated by radial basis function neural networks, in which the weight update laws are obtained via the gradient descent algorithm, ensuring the local boundedness of the approximation error of neural networks. Then, to enhance the transmission efficiency of control signals, a dynamic event-triggered mechanism is introduced, which enables the dynamic adjustment of threshold parameters in response to the actual tracking performance. It is strictly proved via the Lyapunov stability criterion that the tracking error can converge to a desired small neighborhood of the origin, and all signals in the closed-loop system are bounded. Finally, the validity of the control strategy is demonstrated through a simulation example.Note to Practitioners— In practical network control systems, control signals are typically transmitted continuously or periodically to devices through the communication network in the form of data packets. As communication networks are usually shared by various system nodes, and resources such as communication channel bandwidth and computational capabilities are limited, improving the transmission efficiency of control signals becomes a crucial design problem for controllers in network control systems. Therefore, This study introduces a control method via event-triggered sampling, aiming to enhance sampling efficiency while ensuring the stability and reliability of the system. The proposed control method is suitable for a broad category of strict-feedback nonlinear systems with communication constraints, offering notable advantages such as low-complexity design and straightforward implementation.

This paper investigates tracking control of nonlinear systems in state-triggering setting. Since the trigger mechanism may result in system states being discontinuous throughout the whole control operation, the controller design process and stability analysis algorithms based on conventional backstepping design are not applicable for such discontinuous systems due to the non-differentiability of the virtual control. To tackle this dilemma, the first-order filter is introduced to smooth the discontinuous virtual control. Nonlinear impulse dynamics technique is adopted to verify the boundedness of the error variables generated by the filter in stability analysis. Furthermore, the adaptive parameters are solely updated at triggering points to alleviate the communication burden. Particularly, all states are required to be updated synchronously with the established concurrent state-triggering mechanism. It is indicated that with the developed backstepping-based state-triggering control scheme, the entire closed-loop signals are bounded, the system output tracks a prescribed reference trajectory and Zeno behaviour is absent. Finally, the simulation study illustrates the applicability of the proposed control scheme.Note to Practitioners—The study of tracking control for nonlinear systems is prevalent in various practical engineering applications, such as the gripping operation of a single-link manipulator, the orbiting of a spacecraft, and the cruising process of an unmanned aerial vehicle. Moreover, with the rapid development of networking, communication resources are often limited. Continuous information transmission in the sensor-to-controller channel can generate communication blockage and degrade transmission efficiency. Therefore, it is crucial to utilize the limited bandwidth resources to solve the tracking control problem of nonlinear systems. In this paper, we propose a backstepping-based state-triggering control scheme, where the triggered states and triggered adaptive parameters are used for the whole control design. The proposed scheme accomplishes the tracking control task well, especially in applications with limited bandwidth and energy. It is more consistent with requirements of the actual engineering.

In this article, a distributed optimal security control method is proposed for multiagent systems (MASs) containing multiple attackers and defenders within a differential adversarial game framework. Initially, the control inputs of the defenders’ systems are considered to suffer from denial-of-service (DoS) attacks from the attackers, then the coupled performance index functions associated with the state errors are constructed. By using the distributed adaptive dynamic programming (ADP) technology, a modified radial basis function neural network (NN) is implemented such that the coupled performance index functions are approximately identified, and by solving the coupled Hamilton-Jacobi-Bellman (HJB) equation, an ADP-based optimal security control policy with a single-critic NN updating law is further proposed. Meanwhile, it is proven that the proposed optimal security control policy constitutes the Nash equilibrium point of the differential adversarial game. Finally, a simulation example is given to validate the effectiveness of the proposed distributed optimal security control method.

This paper focuses on the membership-functions-dependent reachable set (RS) synthesis of a class of Takagi-Sugeno (T-S) fuzzy singular multi-agent systems subject to deception attacks. To do this, with the help of the T-S fuzzy model, the nonlinear system model is transformed into a quasi-linear system. Then, the control method in the linear system is used to solve this kind of nonlinear problem. Firstly, two suitable non-singular value matrices M and N are obtained by singular value decomposition, and then the singular system is decomposed into algebraic subsystem and differential subsystem by matrices M and N. Secondly, a consensus topology with communication delay under deception attack is designed. In this cases, the sufficient conditions of RS for the simultaneous existence of external disturbances and deception attacks are given, and the specific value of the RS is obtained by solving the linear matrix inequality using the matrix decoupling method. Finally, the effectiveness of the proposed method is verified by taking the actual DC motor built on the physical platform and a class of nonlinear single-species bio-economic system as examples.Note to Practitioners—In the context of contemporary huge industry and engineering, while rapid development, security is also one of the issues that must be considered. However, the modern engineering system has the characteristics of complexity and variability, and a single module cannot operate it completely, so multiple separate modules need to work together through a unique connection. For this reason, security issues will become much more difficult. In addition, in modern network engineering, there are often some criminals who destroy the system, resulting in its normal operation, economic losses, serious situations, or endanger personal safety. Such behavior is usually called network attacks. Deception attacks are part of network attacks, which can harm the system by spreading false information. Therefore, in researching security issues, deception attacks need to be paid attention. Based on the above analysis, the membership-functions-dependent reachable set synthesis of a class of Takagi-Sugeno fuzzy singular multi-agent systems subject to deception attacks is studied in this paper.

This article considers adaptive hierarchical sliding mode control (HSMC) for hyperbolic partial differential equation (PDE)-ordinary differential equation (ODE) cascade systems. Unlike existing PDE-ODE cascade systems, the ODE subsystem studied in this paper involves unknown nonlinear functions and unknown gain functions, and thus fuzzy logic systems (FLSs) are introduced to approximate them. Meanwhile, a projection algorithm is proposed to ensure that the designed controller does not have singularity issues. Subsequently, with the aid of integration by parts and Volterra integral (VI) transformation, the original system is equivalently transformed into a new target one. For this target system, an adaptive fuzzy control approach based on hierarchical sliding mode (HSM) is developed. Compared with the commonly used backstepping method, this method simplifies the controller design steps while also eliminating the algebraic loop and “complexity explosion” issues. In addition, a triggering mechanism with a switching threshold is introduced to alleviate communication burden. Ultimately, the proposed control algorithm ensures that all signals including system states and actuator states are bounded, and this fact is verified through a Josephson junction circuit simulation.

This article studies the dynamic event-triggered adaptive finite-time tracking control issue for a robotic manipulator (RM) system with disturbances. First, a new global prescribed performance function (PPF) is designed based on a scaling function such that the tracking error evolves within the constrained bounds and the restriction related to the initial conditions is removed. Then, the finite-time command filter (FTCF) is used to avoid the direct derivations of virtual controllers and the singularity issue of the conventional backstepping technique. Moreover, the filtering errors caused by the FTCF are removed by the designed error compensation mechanism. A novel dynamic event-triggered mechanism (DETM) using the dynamic auxiliary variable is designed to save communication resources. The proposed control scheme can guarantee that all signals of the RM are globally bounded within a finite time, and the tracking error can asymptotically reach zero. Finally, a simulation example and several comparative simulations show the validity of the proposed scheme.

This article primarily studies the adaptive output-feedback consensus tracking control issue for nonlinear multiagent systems (MASs) with unknown control directions against deception attacks. First, a composite observer combining the state observer and the disturbance observer is developed to concurrently estimate the states of confronting deception attacks and unmeasurable disturbances. Moreover, to resolve the unknown gains resulting from deception attacks, the adaptive attack compensator is proposed. Furthermore, in view of the logarithm Lyapunov function in the final step of the design process and the intelligent approximation technique, a new composite-observer-based adaptive consensus tracking control strategy is constructed. The suggested control strategy ensures the boundedness of all the closed-loop signals while also achieving synchronous tracking of the leader's output by the followers. Last but not least, the effectiveness of the suggested control strategy is validated through two simulation examples.

This article is concerned with the event-triggered fault-tolerant control (FTC) for uncertain nonlinear cyber-physical systems (CPSs) by only exploiting the triggered faulty output. During the control design process, the unknown system dynamics, the time-varying sensor, and the actuator faults are considered simultaneously. Based on the event-triggered mechanism, the first-order filter technique and the nonlinear impulsive dynamics approach, an adaptive neural event-triggered output feedback FTC scheme is established. More specifically, one triggering condition is established for both the measurable output and the state estimations, with the adaptive parameters being triggered at the same instants. Another triggering condition is established for the controller, eliminating the need for real-time monitoring of control information and thereby reducing the computational burden. Then, a neural state observer is designed from triggered faulty output and triggered state estimations. The first-order filter technique is introduced to handle the non-differentiability of virtual controls stemmed from the event-triggered mechanism. The nonlinear impulsive dynamics approach is employed for stability analysis of the discontinuous error dynamics. It is proved that, with the proposed scheme, all the closed-loop signals are bounded, meanwhile the system output converges to the origin asymptotically, and the Zeno behavior is excluded. Finally, simulation results present the feasibility and effectiveness of the seeking schemes.

This article presents several adaptive event-triggered control (ETC) strategies for a class of uncertain nonlinear cyber-physical systems (CPSs) under constant or time-varying full-state constraints, as well as deception attacks via the senior network. Initially, the system under investigation is reformulated into a new system that encompasses both the original system state and the compromised state, enabling the use of compromised states for feedback control. Subsequently, two innovative asymptotic integral barrier Lyapunov functions (IBLFs) are developed by directly imposing constraints on the compromised state, thereby eliminating the necessity to convert state constraints into error constraints as required by traditional BLFs-based methods. Furthermore, controllers designed using relative/switched threshold event-triggered strategies ensure that all signals within the entire closed-loop system remain bounded, that the constant or time-varying full-state constraints are not breached, and that asymptotic stability is attained without Zeno behavior. Ultimately, simulation results validate the efficacy of the proposed strategies through a practical example.

In this article, the problem of adaptive dynamic event-triggered nonsingular predefined-time (PT) tracking control for a third-order fully heterogeneous vehicle platoon system is investigated. First, an improved nonsingular PT adaptive tracking controller is constructed by introducing a piecewise continuous function within the control signal in each step of the backstepping procedure, which avoids the singularity problem of the traditional PT control signals in the existing literature and guarantees the existence of the values for all the terms within the control signals in the real number field. Second, a class of universal barrier Lyapunov function (UBLF) is adopted to set restrictions on distance, which ensures that collisions are avoided and communication connectivity is maintained. In addition, based on a dynamic auxiliary variable, the communication resources are saved with the use of the dynamic event-triggered control scheme. Finally, through the PT stability criterion, it is proven that the PT stability of the whole vehicle platoon is ensured, and the effectiveness of the control algorithm is verified by the simulation results.

This paper investigates the event-triggered trajectory tracking control for unmanned surface vessels with prescribed performance subjected to asymmetric time-varying state constraints. In order to realize the prescribed-time control task, a performance function is introduced and the fuzzy logic systems are adopted to estimate the unknown nonlinearities of the USVs model. Subsequently, a flexible event-triggered mechanism is designed to minimize the wastage of communication resource, taking into account the continuous updating of the actual control input. A practical virtual control signal is devised and used for backstepping design. On this basis, an event-triggered adaptive fuzzy trajectory tracking control algorithm with prescribed performance is developed by combining the defined performance function and the barrier Lyapunov function approach, which not only solves the asymmetric time-varying state constraints of the USVs, but also ensures that the USVs achieve the predetermined tracking performance, meanwhile, all the closed-loop signals can be maintained bounded. Finally, the simulation results are performed to demonstrate the feasibility of the developed control scheme.

This article principally presents the adaptive fuzzy dynamic event-triggered output-feedback consensus tracking control problem for constrained nonlinear multiagent systems encountering false data injection attacks, using monotone tube boundary functions (MTBFs). First, the composite observer is designed to concurrently estimate the unpredictable system states and disturbances. Then, a novel parameter integration approach is constructed via the fuzzy approximation technique to reduce the computational burden, where the uncertainty terms (state errors, composite errors, fuzzy weights, and injection attacks) can be converted into a linear parameterized form with merely one unknown scalar. In addition, a set of MTBFs is designed to prevent the excessive overshoot or jitter of system errors in the transient-state performance. Furthermore, an adaptive dynamic event-triggered control strategy is constructed on the basis of the auxiliary dynamic variable. All the closed-loop signals are shown to be bounded, and the followers can achieve the synchronous tracking of the leader's output. In the end, the simulation results demonstrate the efficiency of the proposed control strategy.

In this article, we propose a dynamic event-triggered adaptive prescribed-time output feedback tracking control strategy for nonlinear systems with unknown external disturbances under denial-of-service (DoS) attacks. The presence of malicious intermittent DoS attacks makes the output signal and states of the system unavailable, which in turn leads to more difficulty in the design of the controller. To overcome the above difficulty, we construct a novel composite observer based on the attack compensator, whereby the system states can be reconstructed. Moreover, with the help of the time-varying constraint function, the prescribed-time tracking control problem for nonlinear systems is transformed into the constraint problem of tracking error. At the same time, a new dynamic event-triggered adaptive prescribed-time safety fuzzy controller is built and remains applicable in the systems that operate continuously after the predefined time. The adaptive fuzzy safety control method proposed in this article ensures that the tracking error converges to the user-specified region in the predefined time, all signals of the closed-loop system remain bounded under intermittent DoS attacks, and the repeated transmission of samples from the controller to the actuator can be further reduced, generating fewer events and saving communication resources. Finally, the simulation results of the single-link robotic arm demonstrate the rationality and effectiveness of the developed control algorithm.

The bipartite formation allows agents to achieve two formation structures in opposite directions and finds wide applications in social as well as natural situations. This article investigates the fast finite-time bipartite formation control problem for time-delay nonlinear multiagent systems operating in an obstacle environment. A fuzzy adaptive formation control strategy is proposed utilizing the leader-following method, under which the desired formation is achieved at a fast convergence rate. Since the practical actuator output is usually limited and susceptible to faults, its antisaturation and fault-tolerance capacities are thus considered in the controller construction. Moreover, an obstacle avoidance mechanism is embedded in the control strategy utilizing the artificial potential field method, ensuring the security operation of the formation. An improved Lyapunov–Krasovskii functional is constructed for the stability analysis, which makes full use of the time-delay information of the system. The fast finite-time convergence of the formation error system and the feasibility of obstacle avoidance behavior are verified with the Lyapunov stability theory and the designed energy function. Finally, the proposed control strategy is applied to the bipartite formation control task of a mobile robot swarm, and sufficient simulation results are presented to demonstrate the effectiveness of the theoretical analysis.

This study addresses the sampled-data control issue for a class of continuous-time switched affine systems (SASs). Specifically, this research aims to relax the constraints on the number of equilibrium points to better characterize the convergence region of continuous-time SASs under the framework of the nonuniform sampling. A sampled-data switching signal is designed to drive the system state toward a union of convergence regions encompassing the shifted points. The shifted points refer to specific points in the state space toward which the system's trajectory is directed or guided. By introducing a switching multishifted-point-dependent Lyapunov function, sufficient conditions are derived for the practical stability of the presented SASs. Moreover, the number of shifted points is determined by the designer, whose value is calculable by resolving the derived stability conditions. Finally, the effectiveness and advancement of the proposed approach are illustrated via a dc–dc buck–boost converter.

This paper proposes an adaptive robust preview control method for path tracking in tractor-trailer trucks, aiming to effectively mitigate trailer sway and attenuate stochastic disturbances while adapting to variable vehicle velocity and path curvature. First, we establish the path tracking control system for the tractor-trailer truck, incorporating the hitch angle and hitch angle rate into the system state to minimize trailer sway. On this basis, an adaptive robust preview control method is developed, integrating feedforward and feedback control to improve tracking performance and robustness. The feedforward controller is designed to proactively minimize tracking errors. In the feedback control, an adaptive observer-based$H_{\infty } $robust feedback controller is developed to limit the effects of stochastic disturbances, where an innovative preview distance model, considering variable vehicle velocity and path curvature, is applied to achieve preview control. Then, based on the$H_{\infty } $theory, the design criterion of the proposed feedback controller is established, where bilinear matrix inequality (BMI) is transformed into linear matrix inequality (LMI) to solve the observer and controller gains. Additionally, the fuzzy algorithm is performed to enable the feedback controller to adapt to variable velocity. Finally, we evaluate the effectiveness of the proposed method using a hardware-in-the-loop platform. Simulation results confirm its superior robustness and performance compared to the existing baseline control method.

Neural network adaptive iterative learning control (ILC) is developed in this article to treat strict-feedback nonlinear systems with unknown state delays and input saturation. These delays are treated by constructing the Lyapunov-Krasovskii (L-K) functions for each subsystem. A command filter is employed to avoid the derivative explosion caused by continuous differentiation of the virtual controller. Corresponding auxiliary systems are designed and integrated into the backstepping procedure to compensate input saturation and the unimplemented part of the filter. Hyperbolic tangent functions and radial basis function neural networks (RBF NNs) are employed to treat singularity and related unknown terms, respectively. The convergence of the resultant strict-feedback systems is ensured in the framework of composite energy function (CEF). Finally, a simulation example is adopted to substantiate the validity of the proposed algorithm.

An adaptive event-triggered consensus control strategy for nonlinear multiagent systems without leaders against deception attacks is presented, where a more general deception attacks is considered in this article. The impact of deception attacks on systems cannot be ignored and the most significant difficulty for researchers is the exact state variables are unavailable. Therefore, a new observer that can observe the unavailable real states of the system is designed. Furthermore, this article presents an unusual coordinate transformation technology, where the states of the observer and the compromised states are considered simultaneously, to remove the effect of the malicious deception attacks. The event-triggered mechanism is introduced to construct a controller can ensure that resource waste is minimized and the utilization is improved as much as possible. Under the proposed control strategy, all signals in the closed-loop system are proven to be semi-global bounded and the stability for the studied systems under deception attacks is ensured. Conclusively, we conduct simulation experiments using the MATLAB software to verify the effectiveness of the proposed strategy.

This study considers a dynamic event-triggered fuzzy${\mathcal {H}}_{\infty }$consensus problem for multiagent systems (MAS) characterized by parameter uncertainties and external disturbances in interval type-2 (IT2) fuzzy form. The proposed control strategy aims to minimize data transmission by triggering control events dynamically. Stability analysis is conducted by Lyapunov stability theory, and more mathematical tools are introduced to relax the sufficient condition and demonstrate the${\mathcal {H}}_{\infty }$consensus. The developed control scheme ensures the consensus among agents, enhancing the overall system performance in the presence of uncertainties and disturbances. Finally, three examples exhibit the validity of the suggested controller.

This article addresses the practical prescribed-time fault-tolerant tracking control (PFTC) problem for a class of nonlinear systems characterized by uncertain control gains and external disturbances. A novel prescribed-time regulator (PTR) is proposed to construct the controller, thereby achieving the tracking control objective with arbitrary accuracy within the prescribed tracking time. The phenomenon of complete actuator faults is also considered, and a fault-tolerant control strategy with immediate actuator switching is designed to mitigate their impact. Furthermore, a self-regulating control gain based on an error-feedback mechanism is introduced to restrain the control input while improving the overall control performance. To overcome the issue of “complexity explosion” arising from the derivation of virtual controllers, a class of nonlinear filters is designed to estimate the values of the virtual controllers. The PFTC strategy proposed in this article ensures the following: 1) all the signals in the closed-loop system remain bounded; 2) the tracking error is practical prescribed-time tracking stable; and 3) after detection of actuator faults, the tracking error reconverges to the desired range within the prescribed recovery time. Lastly, a simulation example is provided to demonstrate the effectiveness of the proposed control strategy.

This article introduces a finite-time adaptive bipartite consensus tracking control algorithm leveraging an improved nonlinear mapping (NM), capable of implementing the prescribed asymmetric full-state constraint requirements for nonlinear heterogeneous multiagent systems (MASs) under actuator deception attacks. First, a locally optimal bipartition strategy is proposed in this article to enable bipartite consensus tracking control within unbalanced directed graph. Unlike most previous full-state-constrained controls employing the barrier Lyapunov function (BLF), the proposed algorithm is derived from building a state-dependent NM function that can achieve one-to-one mapping for the original function and constructing a new coordinate transformation, leading to the solution of full-state-constrained performance control without the feasibility conditions on the control signals. By means of the proposed practical finite-time stability criterion, the novel design aims at constructing finite-time controllers that can ensure the existence and nonsingularity of the finite-time term in each control signal, which are oblivious in the existing results on finite time control. What’s more, the designed controller even under actuator deception attack achieves that:1)the bipartite consensus tracking errors converge to a small area containing zero in a finite time;2)the prescribed asymmetric full-state constraints are obeyed. At last, the availability of the proposed design solution is clarified by virtue of single-link robot systems.

This article investigates an adaptive bipartite consensus tracking control algorithm for a class of heterogeneous nonaffine nonlinear multiagent systems (MASs) with prescribed finite-time tracking performance under an unbalanced communication topology. In the case of an unbalanced digraph, a novel locally optimal bipartition strategy is proposed to transform the unbalanced communication topology into a structurally balanced one, thereby enabling the implementation of bipartite consensus tracking control. To achieve the expected tracking performance, the design philosophy focuses on developing a prescribed finite-time performance function (PFTPF), capable of preassigning the convergence time and accuracy precisely beforehand. The explored adaptive control algorithm can ensure that the whole signals concerning the closed-loop MASs remain bounded while the bipartite consensus errors converge to a predetermined range around zero within the prescribed finite time. Ultimately, the simulation results on robotic systems prove the availability of the developed design solution.

In this work, via proposing a novel$\tan $-type asymmetric barrier Lyapunov function (ABLF), we developed a command filtered adaptive fault tolerant control scheme for a class of uncertain nonlinear systems with predefined-time asymmetric and time-varying output constraints. First, such a class of constraints under interest is more practical. Unlike most of the existing constraints that need to be addressed at the moment of inception, the initial value requirement of the constrained object in this work is released. A predefined setting time can be installed in advance through the implementation of a shifting function embedded within the proposed ABLF. Second, the problem of unknown actuator faults is considered where the actuator faults can be both multiplicative and additive. Third, by combining the command filter and backstepping techniques, a twice-transformation design method is presented to construct the desired controller, under which the errors between the virtual control laws and the outputs of the command filters can be compensated thoroughly. Aside from avoiding the “explosion of complexity” issue, the designed controller also achieves the requisite tracking performance. Meanwhile, under such controller, the effect of the actuator faults can be estimated. Finally, the validity of the given scheme is evaluated through the use of a practice example.

This article investigates the leader-follower consensus for nonlinear multiagent systems (MASs) and proposes an adaptive dynamic programming (ADP)-based hierarchical Stackelberg-Nash optimal game control method. Initially, a coupled performance index function associated with consensus errors is constructed. As the positive-definite function with the quadratic form is allocated to the constructed consensus errors-based performance index function, the original system stabilization problem is converted into the issue of seeking an optimal control strategy profile for the leader and followers. Under the hierarchical Stackelberg-Nash differential game framework, the optimal control strategies are derived in sequence and further proved to compose the equilibrium points of Stackelberg-Nash differential games. Afterward, based on the ADP technique, a modified single-critic neural network (NN) is implemented and the coupled Hamilton-Jacobi–Bellman (HJB) equation is approximately identified. Under the proposed control scheme, the leader-follower consensus of the considered MAS can be achieved while consuming less control cost. Meanwhile, all signals of the MAS are ensured to be uniformly ultimately bounded. Finally, a numerical simulation and an application to the electrode regulating system of the three-phase electric arc furnace are given to verify the effectiveness of the proposed control method.

This article investigates the adaptive neural network (NN) tracking control problem for nonlinear networked control systems (NCSs) with finite-time prescribed performance (FTPP) subject to intermittent denial-of-service (DoS) attacks. It is noticeable that when the DoS attacker is active, the controller does not receive any information, which makes the controller fail to work. To tackle the challenge, an adaptive NN switching state observer is first built to estimate the unmeasurable states. Second, an FTPP function is constructed to boost the transient and steady-state performances of NCSs. Third, under the framework of the backstepping technique, an adaptive command filter is established by combining the dynamic adaptive technique with the switching state observer, which handles the “complexity explosion” problem and improves the robustness of NCSs. Besides, it is rigorously proved mathematically that the boundedness of all signals in the closed-loop system and the designed controller compels the tracking error to fall into the predefined boundary within a finite time. Finally, an application-oriented example of the single-link robotic arm system is utilized to demonstrate the viability of the proposed control method.

A practical fixed-time adaptive fuzzy control strategy is investigated for uncertain nonlinear systems with time-varying asymmetric constraints and input quantization. To overcome the difficulties of designing controllers under the state constraints, a unified barrier function approach is employed to construct a coordinate transformation that maps the original constrained system to an equivalent unconstrained one, thus relaxing the time-varying asymmetric constraints upon system states and avoiding the feasibility check condition typically required in the traditional barrier Lyapunov function based control approach. Meanwhile, the “explosion of complexity” problem in the traditional backstepping approach arising from repeatedly derivatives of virtual controllers is solved by using the command filter method. It is verified via the fixed-time Lyapunov stability criterion that the system output can track a desired signal within a small error range in a predetermined time, and that all system states remain in the constraint range. Finally, two simulation examples are offered to demonstrate the effectiveness of the proposed strategy.

This paper investigates the attack-compensated output control problem for a class of Markov jump cyber-physical systems (MJCPSs) subject to mismatched modes. A sub-domain-awareness adaptive probabilistic event-triggered mechanism (APETM) is innovatively developed to fully enhance the control performance of the networked control system. Here, sub-domain-awareness means the network’s communication ability, which may be affected by time delay and network resource. To defend against the cyber-attacks imposed on the communication channel, a predictor-based compensator is constructed to mitigate the impact of attacks on the control performance. Different from the existing works, the mismatch degree between the controller and the system, governed by a random variable obeying any discrete-time distribution over$[0, 1]$, is changing dynamically. A mismatched output feedback controller is designed based on the APETM and attack-compensator, and an augmented closed-loop system is obtained. Both time delay-and triggering threshold-dependent Lyapunov functionals are introduced to perform the asymptotical stability analysis. With a separation technique, a solving algorithm is provided which converts the non-convex analysis conditions into traceable ones. Finally, simulations are conducted to validate the proposed results through a mass-spring-damping system model.Note to Practitioners—Cyber-physical systems (CPSs) have found many applications in the fields such as smart grid, gas distribution systems, and automated manufacturing systems. The open communication network deployed in CPSs face two main challenges including the security of data transmission and the efficiency of resource utilization. Owing to the practical environment and other factors, system parameters may change abruptly, which can be well defined by a Markov model. In the resultant Markov jump system, it is typically assumed that the mode mismatch degree between the controller and the system remains the same, which is unrealistic in a time-varying network environment. Motivated by these observations, this paper investigates the secure output control of Markov jump CPSs (MJCPSs) subject to mismatched modes and unknown cyber-attacks. An adaptive probabilistic event-triggered mechanism is innovatively proposed, which compared with the existing mechanisms strikes a higher level balance between the communication burden and the control performance. And attack-compensated mismatched output controllers are developed to stabilize MJCPSs and meanwhile preserve the security level. Finally, a mass-spring-damping system is adopted to verify the application effectiveness of the proposed results. Note that communication traffic control and secure control are finely involved based on CPSs, making our proposed results more applicable to practical systems.

In this paper, for a class of uncertain nonlinear systems, a low-computation design scheme for fault detection and performance recovery based on deferred replacement actuators is proposed. Different from the existing two mainstream adaptive fault-tolerant control schemes, the proposed method does not require prior knowledge of fault models, nor does it require multiple actuators working in parallel simultaneously to mitigate the impact of faults. The disadvantage of the former is that most of the considered fault models cannot cover all fault types, while the latter adds unnecessary wear and tear to actuators that do not generate faults. In order to overcome the shortcomings of the existing fault-tolerant control, a method with deferred actuator replacement is proposed. By designing a fault detection function and a shifting function, new error variables are reconstructed to achieve performance recovery. In addition, a computationally efficient design scheme is established through the idea of constraint control. Unlike the existing advanced technology, firstly, dealing with the problem of complexity explosion from a new perspective and ensuring the rationality of assumptions; secondly, analyze the controllability of system states during the deferred replacement stage. Finally, simulation results on a twin otter aircraft system verify the effectiveness of the proposed scheme.Note to Practitioners—For widely used modern control systems such as aircraft systems, self-driving systems, etc., it is extremely difficult to diagnose and repair the sudden fault behavior in a short time, and in this process, the adverse impact caused by the serious decline of the system performance is huge. Inspired by the practical application of airbus states in the flight control system, a fault-tolerant control scheme of deferred replacement actuators is designed in this paper. In addition, by designing a low-computation control scheme, the assumptions that are difficult to achieve in practice are relaxed, and the structure of the controller is very simple without the help of any auxiliary design. After fault detection, the replacement actuators strategy successfully realizes the recovery of the system performance within the specified time.

For the existing tracking control schemes of flexible-joint robots, precise sensor measurement is an implicit premise. However, idealized sensors are difficult to achieve due to manufacturing technology or other external factors. To this end, this paper further investigates the tracking control problem for flexible-joint robots with unknown measurement sensitivity. Specifically, for such multi-input multi-output Euler-Lagrange systems with completely unknown system dynamics, a novel measurement values-based adaptive control method is proposed by fusing sensitivity information and system variables into Lyapunov function candidates, where the restriction on system states in other the approximation lemma-based results is removed, since unknown nonlinearities are scaled by the structural characteristics of system variables. Above all, even if there are measurement errors, satisfactory tracking performance can be obtained by adjusting the design parameters, which is proved by rigorous theoretical analysis. Finally, hardware experiments further verify the effectiveness of the proposed method.Note to Practitioners—This work is motivated by the trajectory tracking control problem for flexible-joint robots under imprecise sensor measurements. Due to manufacturing technology limitations and component aging, there is inevitably a deviation between the measured values of sensors and real values, and this problem may become more prominent as the working environment of flexible-joint robots tends to become more complex. To our knowledge, most of the existing solutions for flexible-joint robots are developed based on precise sensor measurements, and they may fail to achieve satisfactory performance when real state information is not available. Moreover, the prior knowledge about model parameters and measurement sensitivity is difficult or impossible to exactly obtain in practice, which seriously hinders the further application of control methods that are dependent on system dynamics. To this end, this paper proposes a novel tracking control scheme based on measurement information for flexible-joint robots with unknown measurement sensitivity, where the dependence on model information is eliminated with the elaborately constructed Lyapunov function candidates, and the real tracking error is still adjusted to an acceptable range even if there are measurement errors. Preliminary experiments on a flexible-joint robot developed by Quanser company demonstrate the feasibility and effectiveness of the proposed method. In future studies, designing an effective scheme to achieve direct preset tracking control is the focus of the work.

A distributed adaptive secure control algorithm is proposed for asynchronous switching nonlinear multi-agent systems (MASs) in pure-feedback form while considering the sensor attacks and actuator faults, which eliminates the effect of attack signals, actuator faults and uncertainties without knowing the sign of the control gain functions. Since the system states measured by the sensors are corrupted by the malicious attacker, only the post-attack system states can be obtained. Thus, an improved coordinate transformation is constructed by using the post-attack system states and a backstepping method based on the improved coordinate transformation is developed to solve the consensus tracking control problem of the researched systems under sensor attacks. The common Lyapunov function is designed to ensure the stability of all asynchronous switching subsystems of each agent. In the end, a simulation example is presented to illustrate the theoretical results’ validity.Note to Practitioners—This paper deal with the distributed adaptive secure consensus tracking control problem for asynchronous switching nonlinear MASs, whose models can describe many phenomena in nature such as biological processes, robot team formation and sensor networks. It is extremely challenging to achieve the tracking control problem of the researched asynchronous switching nonlinear system in an unsafe network environment, where the unknown sensor attacks signals, actuator faults coefficients, and randomly switched non-affine nonlinearities exist together for the followers. Furthermore, the control design and stability analysis of the researched system is based on the common Lyapunov approach, which makes the developed approach more engineering oriented.

This article presents a co-design method of dynamic periodic event-triggering mechanism (DPETM) and output-based control law for networked control systems (NCSs) subject to inevitable imperfections such as limited network resources, exogenous disturbances and successive packet dropouts. The developed DPETM has the potential to make full use of scarce network resources compared to the periodic sampled-data control on the one hand, and is natural for practical digital platforms instead of a discretized version of continuous event-triggered control (CETC) one the other hand. In this paper, the whole closed-loop system is modeled a unified hybrid system by incorporating bilateral DPETM, successive packet dropouts effects and external disturbances, and an improved hybrid Lyapunov function is constructed for stability analysis. Based on the hybrid framework, some sufficient conditions are presented to guarantee$\mathcal {L}_{2}$-stability of the system while tolerating a maximum allowable number of successive packet dropouts (MANSD). Furthermore, new linear matrix inequality (LMI) conditions for co-design of control law and triggering strategy are derived by introducing some constraints with the free variables and a modified Young’s inequality. In addition, this paper considers two scenarios relating to whether an acknowledgment scheme is employed or not. Finally, the effectiveness of the proposed method is verified by two illustrated examples and comparative studies. Note to Practitioners—With the increasing requirement for reducing unnecessary waste of communication resources in modern industries, it is necessary to consider an alternative paradigm of the time-triggered control. In many digital control applications such as smart power grid, vehicle and remote surgery, the control tasks are often executed periodically, which makes it difficult for the theoretical results of CETC to be directly applied to practical digital platforms. The DPETM proposed in this paper, which only verifies the triggering conditions periodically at each sampling instant, is more suitable for practical digital platforms such as time-sliced embedded architectures compared to CETC. This paper investigates the stability problem for NCSs with DPETM subject to packet dropouts. Different from most of the existing literature on randomness of packet loss, this paper focuses on impact of the continuity of packet dropouts on system performance. Based on the hybrid framework and the Lyapunov approach, a co- design methodology of triggering condition and control law is proposed to ensure that the system satisfies the desired performance and can admit a MANSD.

This paper investigates the deferred output constraint (DOC) issues of high-order random nonlinear systems. The DOC indicates that the system output is constrained within a given range after the system running for a period of time, rather than being constrained at the beginning. This leads to an obstacle that the well used barrier Lyapunov function method is invalid because it is not defined at the initial stage. For this reason, a prescribed-time scaling function and a barrier function are introduced. Then, to more accurately describe the disturbances in nature, this paper considers a more common stochastic process than white noise, which is called colored noise. In addition, a dynamic-memory event-triggered mechanism (DMETM) that takes into account the influence of historical information of internal dynamic variables on trigger conditions is proposed to reduce the waste of communication resources. With the assistance of Lyapunov stability theory and backstepping approach, it is shown that our developed DMETM-based control scheme can guarantee that all signals are bounded in the presence of colored noises. Ultimately, two simulation examples are included to demonstrate the effectiveness of the proposed theory.Note to Practitioners—Since higher-order nonlinear systems can describe nonlinear features in system dynamics, some complex actual systems such as induction motorcycles and space vehicles can be modeled as such systems. In engineering applications, controlled systems are usually affected by random noises from external environment, thus colored noises are introduced in this paper. Compared with white noises, colored noises can describe disturbances in nature more accurately and reasonably. In addition, to ensure that practical systems such as car operation systems and robotic arm systems can work safely, their DOC issues need to be considered. However, the traditional barrier Lyapunov functions (BLFs) cannot solve the DOC problems because they are not defined in the initial stage, hence the prescribed-time scaling and barrier functions are proposed. Meanwhile, to circumvent the problem of limited communication bandwidth, a DMETM with delay terms of internal variables is devised.

In this paper, the robust consensus tracking problem is investigated for the heterogeneous nonlinear multi-agent systems (MASs) with weighted communication graph and time-varying input delays. Different from other existing works, the heterogeneous MASs have more general topology graph which contains a spanning tree. Further, the assumption of leader system is relaxed to contain the disturbance term. Hence, the intelligent technologies are used to approximate the unknown quantities from neighbor systems and leader system. To deal with the difficulties caused by the input delays, the compensation system, double integral type and novel constructed exponential type Lyapunov-Krasovskii (L-K) functionals are proposed to design the adaptive controllers. It should be known that the novel constructed exponential type L-K functional is simpler to design, and not only can satisfy the stability requirements but also can counteract the input delay. Besides, the uniform hysteretic quantizer is used to save the communication costs, and the dynamic surface filter is considered to optimize the controller structure for each agent. Under the proposed control protocol, the tracking errors are shown to achieve satisfactory control accuracy. Finally, the numerical example is provided to verify our strategies.Note to Practitioners—This paper considers the consensus tracking problem for the heterogeneous nonlinear MASs with weighted communication graph and time-varying input delay. The model is widely applied in the flocking control field, such as transportation, communication, manufacturing sector and so on. As the delay causes system performance degradation, it is more meaningful for realistic scene to consider the continuous and non-differentiable controller with unknown time-varying delay. Furthermore, the designed controller can keep the control accuracy, and the computation and communication costs are efficiently reduced.

This paper investigates the event-triggered-based adaptive bipartite finite-time tracking control problem of nonlinear nonstrict-feedback coopetition multi-agent systems (MASs) with the time-varying disturbances. First, the major design difficulties generated by the entirely unknown nonlinear functions containing all states are solved by utilizing the approximation property of radial basis function neural networks (RBF NNs) and the structural feature of Gaussian functions. Then in the backstepping procedure, the issue of “explosion of complexity” is handled by combining the adaptive neural approach and the command filter technique, which simplified the complexity of the controllers for all the agents. Meanwhile, the novel compensation signals are designed, which skillfully eliminate the error influence caused by the filters. Moreover, to save the communication resources, the relative threshold event-triggered control (ETC) scheme is presented for the designed controllers, and there is no Zeno phenomenon. Overall, it is shown that the new proposed control approach drives the tracking errors to the desired neighborhood of the origin in an almost fast finite time, and all the signals in the closed-loop systems are almost fast finite-time bounded. Finally, the numerical and practical simulation results are both given to show the validity of the obtained design methodNote to Practitioners—This paper presents the event-triggered adaptive bipartite finite-time tracking control scheme for the nonlinear nonstrict-feedback coopetition MASs with the time-varying disturbances, which can be used to model most practical systems such as sensor network, submarine underwater robot and space satellites. Although the bipartite control problem has been studied a lot, the bipartite finite-time tracking control problem for nonlinear nonstrict-feedback coopetition MASs with the time-varying disturbances is still open and challenging. The proposed approach guarantees that the tracking errors can converge to the desired neighborhood of the origin in an almost fast finite time. In addition, the ETC strategy is proposed to address the limited power consumption. A remarkable point for this paper is that the obtained results are demonstrated by the simulation examples, which shows the validity of the proposed approach.

In this paper, an adaptive finite-time bipartite consensus tracking control strategy is presented for a class of heterogeneous nonlinear nonstrict-feedback multi-agent systems (MASs) with output constraints. Firstly, to deal with the time-varying output constraints problem, an improved tan-type nonlinear mapping (NM) function is presented for the first time. And based on the improved NM function, a novel tracking error is constructed to design controller for each agent, which guarantees the bipartite consensus tracking is achieved while constraints requirement is not violated. Then, a state observer is designed to estimate the unmeasurable states of each agent. Moreover, in the case of unbalanced directed topological graph, a partition algorithm (PA) is employed to implement bipartite consensus tracking control. The developed distributed adaptive finite-time control strategy ensures that all the signals in the closed-loop system are bounded and the bipartite consensus tracking control is achieved in finite time. Finally, the validity of the designed control strategy is demonstrated by a simulation experiment.Note to Practitioners—At present, nonlinear MASs are widely used in practice, such as robots formation control, vehicular platoon systems control, etc. This paper investigated the adaptive finite-time bipartite consensus tracking control problem for a class of heterogeneous nonlinear nonstrict-feedback MASs with output constraints. In the scenarios of practical application, these two situations are common: 1) The communication topology graph of nonlinear MASs is unbalanced. 2) The output of each agent is constrained. Therefore, this paper presents an improved tan-type NM method to deal with the time-varying output constraints problem, and a partition algorithm is employed to implement bipartite consensus tracking control based on the unbalanced communication topology graph. Meanwhile, the nonsingular finite-time control strategy effectively improves the convergence of the studied nonlinear MASs. In addition, the system model and backstepping technology used in this paper are general and practical.

This paper investigates the adaptive finite-time consensus tracking control problem for a class of nonlinear nonstrict-feedback multi-agent systems (MASs) with output constraints. Firstly, to deal with the output constraints problem, an improved tan-type nonlinear mapping (NM) function is presented for the first time. Then, the singularity problem that exists in finite-time control is successfully avoided by employing a novel switching function. A modified tracking error involving the dependent variable of the designed NM function is constructed to guarantee that the outputs of all agnets achieve consensus tracking simultaneously and satisfy the constraints requirement. In addition, a switching threshold event-triggered control (ETC) strategy is applied to design controller for each agent, which combines the advantages of fixed threshold strategy and relative threshold strategy, and saves system communication resources well. The developed distributed adaptive finite-time control protocol ensures that all the signals in the closed-loop system are bounded and the consensus tracking control is achieved in finite time. Finally, the effectiveness of the designed control strategy is verified by a simulation experimentNote to Practitioners—Due to the wide application of nonlinear MASs in practice, such as unmanned aerial vehicles (UAVs) formation control, robots formation control and so on, the adaptive finite-time consensus tracking control problem for a class of nonlinear nonstrict-feedback MASs is studied in this paper. In practical applications, the practical fast finite-time strategy can effectively increase the system convergence, the NM method not only solves the output constraints problem well, but also overcomes the conservativeness of traditional barrier Lyapunov functions. Then, in order to reduce the communication burden of the nonlinear MASs, a switching threshold ETC is considered. In addition, the system model and backstepping technology used in this paper are general and practical.

This paper investigates the prescribed-time control for nonlinear systems with sensor faults and unknown control directions. Firstly, the prescribed-time control is realized based on scale function and descending power coordinate transformation. Then, effective nonlinear terms are designed in the controllers to compensate for state pollution caused by sensor faults. Furthermore, the Nussbaum gain function is introduced and it can be proven that the derivative of its variable is bounded, which is of significance to analyze the invariant set. The proposed control scheme guarantees that system states converge to zero within the specified time in advance and all signals of the closed-loop system are bounded. Finally, a numerical simulation and a practical simulation based on the Nomoto ship model intuitively show the feasibility of the scheme.Note to Practitioners—This study is motivated by achieving the prescribed-time control for nonlinear systems with sensor faults and unknown control directions. With the improvement of automation, the prescribed-time control that the convergence time can be arbitrarily specified in advance has been applied to many time-critical fields, such as missile guidance, emergency braking, and so on. It is noted that hypersonic weapons, uncalibrated visual servo control, and other practical models have the characteristic of unknown control direction, and most of the existing prescribed-time control schemes are not suitable for these practical systems. In addition, since the hovercraft power systems and aircraft systems are in a harsh environment for a long time, it can easily lead to sensor faults resulting in major accidents. In this study, based on the descending power coordinate transformation, the prescribed-time controllers with unique nonlinear terms are designed to deal with the above challenges.

This paper proposes an adaptive bipartite secure consensus asymptotic tracking control scheme based on event-triggered strategy for the nonlinear multi-agent systems (MASs) under denial-of-service (DoS) attacks. First, by incorporating the concept of shortest path into the hierarchical algorithm, the bipartite consensus control problem in the presence of unbalanced communication topology is successfully addressed. Further, an anti-attack bipartite control strategy by designing the improved forms of the tracking errors and virtual controllers is proposed under DoS attacks. Then, a modified event-triggered mechanism based on the relative threshold strategy is proposed, which can ensure that the bipartite consensus tracking errors converge to zero asymptotically by utilizing the Barbalat’s Lemma. Under the Lyapunov stability theory, it is guaranteed that the outputs of all agents can reach agreement with an identical magnitude but opposite signs and all the signals of the closed-loop MASs are bounded. Finally, the simulation results on the model of damping pendulums are given to verify the efficiency of the proposed secure controller.Note to Practitioners—In this paper, the adaptive bipartite secure consensus asymptotic tracking control problem is considered for nonlinear MASs subject to DoS attacks, whose models can describe many critical applications, such as bidirectional formation of unmanned vehicles and robots. The research on the secure tracking control problem will be rather complicated yet challenging if the DoS attacks of the communication channels are taken into account in the insecure complex network environment. In addition, the event-triggered mechanism is used to reduce the usage of communication resources, which makes the proposed control scheme more easier to implement and more friendly for control engineers.

This work proposes an adaptive neural network asymptotic tracking control strategy, capable of ensuring flexible fixed-time prescribed transient behavior for switched nonlinear systems with external disturbances and input saturation. Unlike most existing prescribed performance controls with input constraints that ignore the contradiction in fulfilling the input constraint and the output constraint, the proposed strategy realizes a trade-off between them by constructing a modified fixed-time prescribed performance function. The performance function boundary will increase with the error between the actual input and the desired control input, which can sacrifice the user-specified performance while saturation exists and fix it while no saturation exists, lowering the risk of singularity. Together with the designed performance function, a Zeno-free event-triggered controller is built to enforce that the output error converges asymptotically and satisfies the performance function boundaries within a fixed time. Besides, the complexity explosion problem is circumvented through the command-filtered technique. Finally, two case studies are provided to validate the established control strategy.Note to Practitioners—Due to physical restrictions, there are various system input and output constraints in practical engineering, which severely hampers the application of both traditional and cutting-edge control algorithms, particularly in the chemical, boiler, robot, and other sectors. Regrettably, the existing approaches only consider input and output constraints separately and assume that they can be implemented simultaneously, which is unrealistic in many practical systems. Hence, this work focuses on developing a novel adaptive fixed-time prescribed performance control strategy by introducing a modification signal, which can not only comprehensively balance input and output constraints but also achieve the asymptotic tracking of the system output. Meanwhile, an event-triggered mechanism is introduced to deal with the problem of limited communication bandwidth.

In this paper, a novel hierarchical sliding-mode surface (HSMS)-based adaptive event-triggered optimal control (ETOC) method is proposed for switched nonlinear systems using single-critic adaptive dynamic programming (ADP). Initially, an ameliorative HSMS containing all system states is constructed to improve the system response rate. With positive quadratic functions being assigned for the developed HSMS-based cost function, the original stabilization problem is further transformed into an array of ETOC problems. Subsequently, by implementing a single-critic neural network (NN) whose weights are tuned based on an aperiodic manner and the historical data, a series of event-triggered Hamilton-Jacobi-Bellman (HJB) equations are approximately solved. Meanwhile, the condition for the persistence of excitation existing in the traditional actor-critic dual NNs framework is relaxed. Furthermore, the uniformly ultimate boundedness (UUB) of all signals in the closed-loop system is guaranteed by the Lyapunov extension theorem. Finally, a practical continuous stirred tank reactor plant is provided to substantiate the efficacy of the proposed HSMS-based adaptive single-critic ETOC method.Note to Practitioners—Switched nonlinear systems have attracted much attention due to their flexibility in characterizing multi-mode features in practice. The control synthesis and stability analysis of switched nonlinear systems have been the research focus in the control field. However, with the development of adaptive dynamic programming (ADP), it is no longer limited to the study of control synthesis and stability of switched systems. How to improve the response rate and achieve optimal control of switched nonlinear systems with less control cost becomes critical. On the other hand, the persistent information transmission of control signals not only occupies more bandwidth, but also causes severe transmission delays, thus it is important to conduct the signal transmission only when necessary. This paper discusses the optimal control problem for switched nonlinear systems. Meanwhile, an event-triggered adaptive dynamic programming optimal control policy based on a hierarchical sliding-mode surface is proposed, which consumes less control cost while improving the system response rate and saving communication resources.

This paper investigates a zero-sum game (ZSG)-based fault-tolerant tracking control problem for a class of interconnected nonlinear systems, in which the faults of each subsystem can affect the other subsystems through couplings. First, a static control policy is developed to transform the fault-tolerant tracking control problem into a ZSG problem of a deduced error system, where the controllers and the faults are regarded as players with opposite interests. Then, based on the sliding mode control technology and the concept of hierarchical design, a novel robust control policy is proposed to regulate the tracking error while minimizing a special cost function. Subsequently, a critic neural network (NN) is trained online to derive the solution of the coupling HJI equation. The critic NN weights are tuned on the basis of the gradient descent approach as well as the historical stored data, such that the persistence of excitation conditions are no longer required. Finally, a simulation example is presented to demonstrate the validity of the proposed control strategy.Note to Practitioners—Since complex modern engineering systems are difficult to be controlled by a single component, the research on adaptive control of interconnected nonlinear systems has become the mainstream trend. In addition, for the practical applications of interconnected systems, fault problems are becoming more common. These faults may make the system difficult to operate normally. Hence, how to guarantee the normal work of the control system when subject to faults has become a hot topic. Furthermore, several engineering applications often take production cost as an important index, and thus it is necessary to design a control strategy that can achieve control objectives with the minimum cost. This paper discusses the optimal fault-tolerant control problem for interconnected nonlinear systems. Meanwhile, a hierarchical sliding-mode control mechanism is designed to improve system robustness.

In this article, two novel adaptive fault-tolerant control schemes for a class of nonlinear strict-feedback cyber-physical systems (CPSs) with deception attacks are presented. Deception attacks, such as false data-injection attacks, which destroy sensor networks, make the outputs and states of the CPSs unavailable. It is very difficult and challenging for a designer to achieve the tracking control under the circumstance of cyberattacks. To realize the tracking control for the studied CPSs, we propose a new coordinate transformation technology without precedent, where it takes the attack gains into account and uses the compromised states to design the corresponding controllers. In the backstepping design process, Nussbaum functions are presented to alleviate the influence of the unknown attack gains. Furthermore, we consider the actuator faults problem, which includes the loss of effectiveness and the bias fault. By skillfully designing the adaptive laws, the effect of actuator faults is completely eliminated. It is theoretically proved that the first proposed tracking control scheme can guarantee all signals in the closed-loop system are bounded and the output can track the desired reference signal. In addition, the second adaptive control scheme is also developed for the CPSs under the actuator faults and a more general assumption on the deception attacks is proposed simultaneously. Finally, the feasibility of the new proposed methods is verified by MATLAB simulation analysis.

This article presents a new event-triggered adaptive finite-time control strategy using a fuzzy state observer for a class of nonlinear cyber-physical systems (CPSs) under malicious deception attacks with a more general form. Compared with the traditional assumptions on the deception attacks in the existing results, a more general assumption on deception attacks is given in this article. During the design process, real system states are initially estimated by developing an improved state observer, which effectively addresses the problem of state unavailability. Then, a coordinate transformation technology, in which the estimated states of observer are considered, is presented to stabilize the studied system. By constructing the singularity-free finite time virtual controls, the singularity problem in the traditional finite time design algorithms is cleverly avoided. Furthermore, to minimize communication overhead, a final finite-time controller is established by using a relative threshold event-triggered scheme. The developed event-triggered adaptive finite-time control strategy guarantees that all signals in the closed-loop system are semi-globally bounded in finite time without Zeno behavior. Finally, the correctness of the proposed control strategy is validated through two simulation results.

This article introduces an adaptive dynamic event-triggered technique for addressing the output tracking control problem of uncertain switched nonlinear systems with prescribed performance. First, a switching dynamic event-triggered mechanism (SDETM) is established to alleviate network burden and conserve computational resources. A notable aspect is the inclusion of asynchronous switching between the switching subsystems and controllers. Second, a state-dependent switching law ensuring a dwell-time constraint is designed, which avoids the frequent switching phenomenon within any finite time interval. Third, an SDETM and an adaptive dynamic event-triggered controller are developed to confine the output tracking error within predefined decaying boundaries, while ensuring that all the signals of the closed-loop switched system remain within bounded regions. Finally, the validity and applicability of the developed control scheme are demonstrated through a one-link manipulator example.

The present study proposes an adaptive fuzzy tracking control strategy for switched nonlinear systems, capable of effectively addressing false data injection (FDI) attacks and input saturation, while achieving flexible prescribed performance control as well as semi-global uniform ultimate boundness for the resultant system. Compared to the previous work, the provided control strategy exhibits two notable strengths: 1) it introduces a novel modified fixed-time pregiven performance function to effectively balance input saturation and output constraint and 2) the detrimental impacts resulting from FDI attacks are successfully mitigated by implementing the fuzzy logic systems approximation technique in the backstepping procedure. A set of switching fuzzy observers are established to estimate the unobservable states while a first-order differential filter is utilized to handle the complexity explosion problem. Finally, the mass-spring-damper system is given to substantiate the developed approach.

This article proposes an adaptive fixed-time output feedback tracking control scheme for a class of nonstrict- feedback stochastic nonlinear systems subjected to unmodeled dynamics and dynamic disturbances. To address the uncertainty arising from nonlinear terms, fuzzy logic systems (FLSs) are used as a universal approximator. Moreover, a state observer is utilized to estimate the unmeasured states. Then, the dynamic signal is introduced into the design process of the controller to solve unmodeled dynamics. Through stability analysis, the proposed new output feedback tracking controller guarantees the boundedness of all the signals, and the tracking error converges to a small residual set within a fixed time. The results of the simulation illustrate the effectiveness of the developed controller.

In this paper, an adaptive fuzzy bipartite formation tracking control strategy of nonlinear multi-agent systems (MASs) is developed under a directed communication topology. Fuzzy logic systems are utilized to approximate the unknown nonlinear dynamics. Then, considering MASs with both cooperative and competitive relationships, the proposed bipartite formation tracking strategy allows each agent to operate according to their specific advantages. By employing a nonlinear state transformation function, the original constrained outputs of the MASs are converted into unconstrained ones. Meanwhile, a prescribed-time shifting function is introduced to handle the deferred output constraints, by which the singularity problem generated from the denominator of nonlinear state transformation functions is avoided. In addition, considering the communication bandwidth is limited, a distributed dynamic self-triggered control (DSTC) mechanism is constructed to improve the transmission efficiency. Different from the traditional self-triggered control strategy, the proposed DSTC strategy allows triggered intervals to be adjusted dynamically according to bipartite formation tracking errors, which enables the DSTC strategy to compromise communication burdern and system performances dynamically. Finally, two simulation examples are given to illustrate the validity of the proposed control scheme.

This paper focuses on the adaptive fuzzy practical predefined-time (PT) bipartite consensus tracking control (BCTC) problem for heterogeneous nonlinear multi-agent systems (HNMASs) with actuator faults. First, the fuzzy logic systems (FLSs) are used to approximate the unknown nonlinear functions. Then, the partial loss of effectiveness and bias fault of actuator are considered simultaneously in the HNMASs, which is effectively handled by using adaptive compensation technology. In addition, the developed bipartite consensus tracking control protocol based on a practical predefined-time strategy not only ensures the fast convergence of the studied systems, but also predetermines the convergence time not relied on the initial conditions. The theoretical result shows that all signals of the closed-loop system are semiglobally uniformly predefined-time bounded (SGUPTB), and the BCTC performance is guaranteed within the predefined time. Finally, the simulation example based on the agents of different orders shows the validity of the obtained results.

This article is concerned with the mismatched quantized$H_{\infty }$output-feedback control of fuzzy Markov jump systems via a dynamic guaranteed cost triggering scheme. An event generator and a quantizer are set up at the sensor-to-controller side and the controller-to-actuator side, respectively. The quantization scheme is presented in terms of a multichannel configuration with different decoder/encoder parameters. A guaranteed cost dynamic event-triggered mechanism is built on instantaneous and averaged triggering errors, output cost, and preset bounds. A composite controller consisting of a static output-feedback and a nonlinear compensation is constructed to meet the desired system performance. Based on the Lyapunov stability theory, sufficient conditions are obtained such that the closed-loop system is stochastically stable with the prescribed$H_{\infty }$performance. A structural vertex separation technique and Finsler's Lemma are employed to decouple the control gain, the quantizer parameters, and the Lyapunov variable. Finally, the validity of proposed scheme is verified by a circuit example.

This article explores a novel singularity-free command-filtered adaptive fuzzy finite-time tracking control algorithm for the flexible robotic manipulator with dead-zone input. First, by considering the influence of the residual term in two cases, a novel practical finite-time stability criterion is presented, and the settling time is more accurately characterized, which is applied to the design of the flexible robotic manipulator. The unknown nonlinear function in the robotic manipulator is handled by employing an intelligent estimation technique based on fuzzy logic systems. Unlike the related finite-time work, a new adaptive command-filtered finite-time controller is constructed by fusing a piecewise continuous function in each step of the backstepping process, such that the singularity of the control signal of the finite-time results is completely circumvented. Further, the mathematical model of the dead zone is delicately reconstructed such that the dead-zone input can be rigorously designed using effective mathematical techniques. Finally, the robotic manipulator system demonstrates the validity of the designed finite-time algorithm.

This article concerns innovative adaptive sliding mode control governed by an observer for a time-varying fuzzy system constrained by fractional Brownian motion with$H \in (\frac{1}{2},1)$under Markovian jump with uncertain transition rates. First, for an uncertain signal, a Takagi-Sugeno (T-S) observer is constructed. Furthermore, in accordance with the unique feature of the coefficient matrix for an uncertain block, the observation system is divided into two low-order subsystems through regular transformation. Subsequently, a common and fixed sliding surface function that does not change with jump is constructed; this is one of the characteristics that differ from the characteristics of the integral switching surface designed by many researchers who have studied the Markovian jump. An adaptive controller is also designed and finite-time reachability analysis is performed by using it. Additionally, a stochastic stability study is performed by constructing Lyapunov--Krasovskii functional, introducing slack matrices and utilizing linear matrix inequalities. Finally, a numerical simulation was conducted to verify the reliability of the proposed approach.

This article addresses event-triggered tracking control for nonlinear systems in strict-feedback form with triggered output signal. During control design process, system state variables are unavailable except for output variable. Fuzzy state observer is thus built by triggered output and triggered state estimations to produce the available state information. A compulsory event-triggered mechanism is established to determine the moment of online data transmission. Furthermore, only two triggering conditions are set up in sensor-to-controller and controller-to-actuator channels, respectively. Then a triggered output feedback adaptive controller, with the triggered adaptive laws, is designed via backstepping. Notice that both of the virtual and real control signals are discontinuous at each triggering time. So, nonlinear impulsive dynamics approach is used for the closed-loop stability analysis. It is strictly proved that the proposed triggered output feedback adaptive controller guarantees that all closed-loop signals are bounded and the Zeno phenomenon cannot occur. The illustrative simulation is used to authenticate the effectiveness of our proposed strategy.

This article focuses on the problem of resilient event-triggered$H_{\infty }$filtering for networked switched Takagi–Sugeno (T-S) fuzzy systems in the presence of Denial-of-Service (DoS) attacks. To improve the utilization efficiency of communication resources and resist the influence of attacks, the resilient mode-dependent event-triggered strategy (ETS) is proposed to determine the necessary data transmission. Different from the existing ETSs for the switching linear systems, the proposed strategy not only removes the two adjacent trigger instants that are strictly less than the average dwell time constraint but also releases data immediately at the end of the attack to mitigate its side effects. Based on the transmitted switching signal and measurable output, the switching fuzzy filter is constructed to estimate the target signal. Unlike the traditional switching filter design, the transmission signals are affected by intermittent interruption of DoS attacks and ETS leads to the asynchronous behavior of system mode and filter mode, which increases the difficulty of filtering error system analysis and filter design. To overcome this challenge, a novel multiple Lyapunov function related to attack signals, system modes, and filter modes is established to analyze the exponential stability and$H_{\infty }$performance of the estimation error system. Furthermore, co-design methods regarding filter gains and event-triggered parameters are given. Finally, simulation results are carried out to substantiate the feasibility of the proposed approach.

This paper investigates the problem of data-driven and dynamic periodic event-triggered control (DPETC) for unknown cyber-physical systems (CPSs) against Denial-of-Service (DoS) attacks and successive packet losses. An unified hybrid system framework is proposed to describe the whole CPS, in which DoS attacks are characterized in terms of duration and frequency. Building on the hybrid system framework, we present model-based co-design conditions for a dynamic periodic event-triggering mechanism (DPETM) and controller to ensure$\mathcal {L}_{2}$-stability of the CPSs while tolerating both DoS attacks and a maximal allowable number of successive packet dropouts (MANSDs). By integrating the model-based conditions with the data-based parametric representation and employing a linear fractional transformation (LFT) approach, corresponding data-based sufficient conditions are derived. Finally, numerical simulations of a batch reactor system demonstrate the effectiveness of the proposed co-design approach.

With the wide application of deep learning in condition monitored system prognostics, its inadequate interpretability has always been questioned. This article proposes interpretable remaining useful life (RUL) estimation routine for RUL prediction, which consists of an augmenter network based on ordinary differential equations and an estimator network utilizing feature-temporal attention. The augmenter is implemented to suppress additive noise in original data and infer unobservable health-related variables with embedded formulas. Subsequently, the uncertainty-aware estimator is executed to predict RUL with quantile regressive module, while detecting dominant features and temporal dependencies via attention. Extensive evaluations are carried out on the N-CMAPSS aeroengine dataset. Compared with the baseline approaches, our method obtains 14% improvement for NASA's score and 7% for root-mean-square error. Moreover, the interpretability of our framework is further analyzed, in which the proposed method is able to imply physical constraints, detect abnormal subsystems and identify critical stages of flight with insufficient prior knowledge.

In this article, the human-in-the-loop leader-follower consensus control problem is addressed for multiagent systems (MASs) with unknown external disturbances. A human operator is deployed to monitor the MASs' team by transmitting an execution signal to a nonautonomous leader in response to any hazard detected, with the control input of the leader unknown to all followers. For each follower, a full-order observer, in which the observer error dynamic system decouples the unknown disturbance input, is designed for asymptotic state estimation. Then, an interval observer is constructed for the consensus error dynamic system, where the unknown disturbances and control inputs of its neighbors and its disturbance are treated as unknown inputs (UIs). To process the UIs, a new asymptotic algebraic UI reconstruction (UIR) scheme is proposed based on the interval observer, and one of the significant features of the UIR is the capacity to decouple the control input of the follower. The subsequent human-in-the-loop asymptotic convergence consensus protocol is developed by applying an observer-based distributed control strategy. Finally, the proposed control scheme is validated through two simulation examples.

This study reports a fixed-time tracking control problem for strict-feedback nonlinear systems with quantized inputs and actuator faults where the total number of faults is allowed to be infinite. By taking advantage of radial basis function neural networks (RBFNNs), unknown nonlinear function terms in the system dynamic model can be effectively approached. In addition, based on the sector property of quantization nonlinearities and the structure of the actuator fault model, novel adaptive estimations and innovative auxiliary design signals are constructed to compensate for the influence caused by actuator faults and quantized inputs properly in the fixed-time convergence settings. Then, rigorous theoretical analysis manifests that the proposed control scheme can make the output tracking error converge to a small neighborhood of the origin within a fixed time, and the upper bound of the setting time not only does not depend on initial states of the system but also can be preassigned by selecting parameters appropriately. Meanwhile, all the signals in the closed-loop system remain bounded. Finally, a numerical example and a practical example of a single-link manipulator are presented to demonstrate the effectiveness of the proposed control algorithm.

In this article, the asymptotic tracking control problem for a class of nonlinear multi-agent systems (MASs) is researched by the combination of radial basis function neural networks (RBF NNs) and an improved dynamic surface control (DSC) technology. It's important to emphasize that the MASs studied in this article are nonlinear and nonstrict-feedback systems, where the nonlinear functions are unknown. In order to satisfy the requirement that all items in the controller must be available, the unknown nonlinearities in the system are flexibly approximated by utilizing RBF NNs technique. Moreover, the issue of ``complexity explosion'' in the backstepping procedure is handled by improving the traditional DSC technology, and meanwhile, the influences of the boundary layers caused by the filters in the DSC procedure are eliminated skillfully through the compensation terms. In addition, the relative threshold event-triggered strategy is developed for the designed controllers to reduce the waste of communication resources, where Zeno phenomenon is successfully avoided. It is observed that the new presented control strategy ensures that all the closed-loop systems variables are uniformly ultimately bounded (UUB), and furthermore all the outputs of followers are able to track the output of the leader with zero tracking errors. Finally, the simulation results are presented to show the effectiveness of the obtained design scheme.

Identifying the Hamiltonian of an unknown quantum system is a critical task in the area of quantum information. In this article, we propose a systematic Hamiltonian identification approach via quantum ensemble multiclass classification (HI-QEMC). This approach is implemented by a three-step iterative refining process, i.e., parameter interval guess, verification, and judgment. In the parameter interval guess step, the parameter interval is divided into several sub-intervals and the true Hamiltonian parameter is guessed in one of them. In the parameter interval verification step, cross verification is applied to verify the accuracy of the guess. In the parameter interval judgment step, an adaptive interval judgment (AIJ) algorithm is designed to determine the sub-interval containing the true Hamiltonian parameter. Numerical results on two typical quantum systems, i.e., two-level quantum systems and three-level quantum systems, demonstrate the effectiveness and superior performance of the proposed approach for quantum Hamiltonian identification.

This article studies the hierarchical sliding-mode surface (HSMS)-based adaptive optimal control problem for a class of switched continuous-time (CT) nonlinear systems with unknown perturbation under an actor-critic (AC) neural networks (NNs) architecture. First, a novel perturbation observer with a nested parameter adaptive law is designed to estimate the unknown perturbation. Then, by constructing an especial cost function related to HSMS, the original control issue is further converted into the problem of finding a series of optimal control policies. The solution to the HJB equation is identified by the HSMS-based AC NNs, where the actor and critic updating laws are developed to implement the reinforcement learning (RL) strategy simultaneously. The critic update law is designed via the gradient descent approach and the principle of standardization, such that the persistence of excitation (PE) condition is no longer needed. Based on the Lyapunov stability theory, all the signals of the closed-loop switched nonlinear systems are strictly proved to be bounded in the sense of uniformly ultimate boundedness (UUB). Finally, the simulation results are presented to verify the validity of the proposed adaptive optimal control scheme.

In this paper, a dynamic zero-sum game is formulated to describe the power decision-making process of the sensor/relay and the DoS attacker in cyber-physical systems. The sensor and the relay cooperate with each other to transmit the state estimation to the remote estimator, when the attacker, on the contrary, aims to disturb the wireless communication channels strategically for deterioration of the system performance but can do this taking into account its limited energy. Different from conventional battery-powered nodes, the sensor and the relay can harvest energy from the external environment and store it in their batteries for data transmission. We model the external environment state as a Markov chain to overcome the randomness of the harvested energy. In addition, to tackle the computation complexity of the Nash equilibrium (NE), we restrict our attention to a special case, i.e., the DoS attacker can only launch interference on one of the two communication channels over an infinite time horizon, and provide the corresponding NE strategy of the game using the Markov decision process and the multi-agent reinforcement learning algorithm. Finally, simulation examples are given to illustrate the theoretical findings of the paper.

This article presents an asymptotic tracking controller for high-order uncertain nonlinear systems. Compared to the existing results, some restrictive assumptions are relaxed and the requirement of the variables being bounded is removed. At the same time, a prescribed time tracking control strategy is constructed to make that the system has better-transient performance during operation. Specifically, a global prescribed time function is proposed and an unconstrained variable is constructed to replace the constrained tracking error. Under this framework, an appealing feature of the designed controller is that the tracking error can converge to a specified range within a prescribed time and eventually converge to zero asymptotically starting from any initial conditions, where the prescribed time and accuracy can be directly given. Finally, the simulation results validate the effectiveness of the control strategy.

In this article, a novel adaptive self-triggered controller is constructed to handle the fault-tolerant control problem for nonlinear system with input dead zone through a funnel function method: 1) the actuator failure and the dead-zone nonlinearity are fully analyzed for the modeling purpose, we transform the system into a new model; 2) the self-triggered scheme is set up to update the control signals, which effectively economizes communication resources; 3) due to the existence of unknown control gains and unknown nonlinear functions, the adaptive neural network funnel controllers are designed, such that the tracking error is confined within the funnel boundary and all the signals of the closed-loop system are semi-globally uniformly ultimately bounded; and 4) finally, the applicability of the acquired control algorithm is demonstrated via a rigid robot manipulator system.

Based on energy-based multiple Lyapunov functions approaches, this article concerns with stabilization and$\mathcal {H}_{\infty }$control for switched uncertain port-controlled Hamiltonian (SUPCH) systems with mode-dependent average dwell time (MDADT) switching. To better deal with the uncertainties of each mode, an improved MDADT scheme along with corresponding constraint conditions is established, and the criterion on globally uniformly asymptotical stability is presented for SUPCH systems in unforced form under the new MDADT mechanism. Subsequently, based on mode-dependent state feedback (MDSF) strategies, the sufficient conditions are derived for stabilization of SUPCH systems. Furthermore, to solve$\mathcal {H}_{\infty }$control problem for SUPCH systems with disturbances, a set of MDSF controllers are constructed, and the$\mathcal {H}_{\infty }$control criterion is obtained by the improved MDADT scheme. Finally, as an application, some simulations on a switched nonlinear RLC circuit system with structural uncertainties are carried out by the proposed control strategies.

The security problem for a kind of switched nonlinear systems with denial-of-service (DoS) attacks is addressed here with a novel event-triggered neural network (NN) adaptive control technique. The provided event-triggered control algorithm, compared to the current output feedback control schemes on continuous-time systems, not only conserves communication resources but also defends against DoS attacks throughout the system stabilization process. First, a novel adaptive NN switching observer is proposed by examining the stability of each subsystem in the sleep and active intervals of the attack, which makes the standard backstepping controller workable even in the DoS attacks scenario. Further, an event-triggered secure control strategy is established by creating a common coordinate transformation, which ensures the bound of all the signals in the resultant system under arbitrary switching and prevents the occurrence of Zeno behavior. A first-order filter is developed to fix the complexity explosion problem in backstepping design. Finally, a continuous stirred tank reactor example is presented to support the established method.

In this article, a leader-follower asymptotic consensus control strategy is developed for a class of linear multiagent systems (MASs) with unknown external disturbances and measurement noises. First, the preconditions, the minimum phase condition (MPC) and observer matching condition (OMC), are discussed in detail, and an equivalent result under these two preconditions is given. In this way, the corresponding results from Corless and Tu (1998) are improved. Meanwhile, a reduced-order observer is designed for a constructed augmented system to estimate the system states and noises of each agent. Next, with the help of a traditional interval observer, a novel unknown disturbance reconstruction method is developed, and the reconstruction can converge to the unknown disturbance asymptotically and decouple from the control input. The subsequent asymptotic consensus is accomplished by utilizing an observer-based control scheme, with its design satisfying the so-called separation principle. Finally, two simulation examples are given to verify the effectiveness and show the advantages of the proposed methods.

This work concentrates on the adaptive resilient dynamic surface controller design problem for uncertain nonlinear lower triangular stochastic cyber-physical systems (CPSs) subject to unknown deception attacks based on a switching threshold event-triggered mechanism. The adverse effect of deception attacks on the stochastic CPSs is that the exact system state variables become unavailable. Furthermore, it should be emphasized that the coexistence of unknown nonlinearities, stochastic perturbations, and unknown sensor and actuator attacks makes it a very difficult and challenging event to implement the control design. To get the desired controller, radial basis function (RBF) neural networks (NNs) are introduced so that the design obstacle caused from the unknown nonlinearities can be easily solved. On this basis, in order to save resources and effectively transmit, the event-triggered control scheme based on a switching threshold strategy is further considered. In the backstepping design process, the dynamic surface control (DSC) technique is presented to deal with the issue of "explosion of complexity." By skillfully designing a new coordinate transformation and the attack compensators, the problem of unknown deception attacks is successfully handled. Under our proposed control scheme, all the closed-loop signals are bounded in probability and the stabilization errors converge to an adjustable neighborhood of the origin in probability. Finally, the simulation results on the double chemical reactor show the validity of the proposed design scheme.

This article investigates the adaptive event-triggered output-feedback control problem for a class of switched stochastic nonlinear systems with actuator faults. In the existing works, the developed results on adaptive control for switched stochastic nonlinear systems are almost based on the average dwell-time method, and how to construct a desired adaptive controller in the frame of the mode-dependent average dwell time (MDADT) remains a control dilemma. By presenting a general adaptive control rule based on the MDADT, this article implements the adaptive output-feedback control for the switched stochastic system under interest. In the process of controller design, fuzzy-logic systems, a flexible approximator, are utilized to approximate the unknown nonlinear functions. The dynamic surface design approach is employed to avoid taking derivatives of the constructed virtual controls to decrease the difficulty of complex calculation greatly. Meanwhile, a switched observer is designed to estimate the unknown states. In the frame of backstepping design, an event-triggered-based adaptive output-feedback controller is constructed such that all signals existing in the closed-loop system are ultimately bounded under a class of switching signals with MDADT property. Finally, the simulation results show the validity of the proposed control strategy.

This work focuses on the problem of predefined-time bipartite consensus tracking control for a class of nonlinear MASs with asymmetric full-state constraints. A predefined-time bipartite consensus tracking framework is developed, where both cooperative communication and adversarial communication among neighbor agents are implemented. Different from the finite-time and the fixed-time controller design methods for MASs, the prominent advantage of the controller design algorithm presented in this work is that our algorithm can make the followers track either the output or the opposite output of the leader within the predefined time in accordance to the user requirements. In order to obtain the desired control performance, an improved time-varying nonlinear transformed function is skillfully introduced for the first time to handle the asymmetric full-state constraints and radial basis function neural networks (RBF NNs) are employed to deal with the unknown nonlinear functions. Then, the predefined-time adaptive neural virtual control laws are constructed by using the backstepping technique, while their derivatives are estimated by the first-order sliding-mode differentiators. It is theoretically testified that the proposed control algorithm not only guarantees the bipartite consensus tracking performance of the constrained nonlinear MASs in the predefined time but also remains the boundedness of all the resulting closed-loop signals. Finally, the simulation research on a practical example shows the validity of the presented control algorithm.

This article investigates the neural-network-based adaptive predefined-time tracking control problem for switched nonlinear systems. Neural networks are employed to approximate the unknown part of nonlinear functions. The finite-time differentiators are introduced to estimate the first derivative of the virtual controllers. Then, a novel adaptive predefined-time controller is proposed by utilizing the backstepping control technique and the common Lyapunov function (CLF) method. It is explained by the theoretical analysis that the developed controller guarantees that all signals of the switched closed-loop systems are bounded under arbitrary switchings and the tracking error converges to zero within the predefined time. A simulation is shown to verify the validity of the developed predefined-time control approach.

In this article, a novel switched observer-based neural network (NN) adaptive control algorithm is established, which addresses the security control problem of switched nonlinear systems (SNSs) under denial-of-service (DoS) attacks. The considered SNSs are described in lower triangular form with external disturbances and unmodeled dynamics. Note that when an attack is launched in the sensor-controller channel, the controller will not receive any message, which makes the standard backstepping controller not workable. To tackle the challenge, a set of NN adaptive observers are designed under two different situations, which can switch adaptively depending on the DoS attack on/off. Further, an NN adaptive controller is constructed and the dynamic surface control method is borrowed to surmount the complexity explosion phenomenon. To eliminate double damage from DoS attacks and switches, a set of switching laws with average dwell time are designed via the multiple Lyapunov function method, which in combination with the proposed controllers, guarantees that all the signals in the closed-loop system are bounded. Finally, an illustrative example is offered to verify the availability of the proposed control algorithm.

This article develops a robust packetized predictive control framework to deal with the quantized-feedback control problem of networked systems subject to Markovian packet dropouts and input saturation. In the proposed framework, the Markov chain model of packet dropout is established from the link of the controller to the actuator. To deal with the quantized measurements, a robust packetized predictive control method is presented with a quantized-feedback law. The problem of unreliable transmission is addressed by proposing a packet dropout compensation strategy with a forgetting factor. An augmented Markovian jump system model is established to take the packet dropouts into account. The synthesis of packetized predictive control is then developed by minimizing a worst case cost function with respect to the model uncertainties. The recursive feasibility of the proposed controller design problem and the mean-square stability of the closed-loop systems are proved, respectively. The proposed packetized predictive control method is demonstrated by simulating a four-tank process system.

Nonsmooth nonlinear systems can model many practical processes with discontinuous property and are difficult to be stabilized by classical control methods like smooth nonlinear systems. This article considers the output-feedback adaptive neural network (NN) control problem for nonsmooth nonlinear systems with input deadzone and saturation. First, the nonsmooth input deadzone and saturation is converted to a smooth function of affine form with bounded estimation error by means of the mean-value theorem. Second, with the help of approximation theorem and Filippov's differential inclusion theory, the given nonsmooth system is converted to an equivalent smooth system model. Then, by introducing a proper logarithmic barrier Lyapunov function (BLF), an output-feedback adaptive NN strategy is set up by constructing an appropriate observer and adopting the adaptive backstepping technique. A new stability criterion is established to guarantee that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB). Finally, comparative simulations through Chua's oscillator are offered to verify the effectiveness of the proposed control algorithm.

In this article, an adaptive fuzzy fixed-time fault-tolerant tracking control problem for high-order nonlinear systems (HONSs) with sensor and actuator faults is considered. The fuzzy logic systems are introduced to approximate the unknown nonlinear functions of the HONS. In addition, based on backstepping technology and fixed-time theory, an adaptive fuzzy fixed-time fault-tolerant controller is developed to ensure that all the signals of the closed-loop HONS are bounded. Eventually, a numerical example can be shown to prove the rationality of the developed method

It is known that a controller designed via backstepping contains all virtual control signals constructed at the previous design steps. When this controller is used to generate an event-triggered control signal which is discontinuous, those virtual control signals are simultaneously triggered and the corresponding triggered virtual signals are utilized for the real control operation, they are also discontinuous. This fact results in the noncontinuity of the virtual error dynamics. This problem is ignored in the existing backstepping-based event-triggered control schemes, in which control design and stability analysis are implemented according to the continuous virtual error dynamics, namely, all the virtual control signals are required to be continuous. Based on this observation, this article focuses on the backstepping-based event-triggered fuzzy adaptive control for nonlinear strict-feedback systems with unavailable states. To do so, a concurrent event generator is first introduced, furthermore, the backstepping-based output feedback control design is given according to the discontinuous virtual error dynamics. Then, the Lyapunov approach for nonlinear impulsive systems is used to provide the closed-loop performance analysis. The results show that with the event-based fuzzy adaptive controller, all the closed-loop signals meet bounded requirements and the Zeno phenomenon is prevented. Ultimately, the proposed theoretical results are supported by two simulation examples.

This article investigates the problem of reachable set control for a class of discrete-time Takagi–Sugeno fuzzy singular Markov jump systems (T–S FSMJSs) with time-varying delay and disturbance for the first time. By designing the state feedback and output feedback controllers, and based on the Lyapunov functional, two new sufficient conditions for the reachable set control are established. The state feedback and output feedback controller gains are obtained by optimizing the iterative algorithm. Then, all the state trajectories of the closed-loop T–S FSMJSs are contained by the intersection of ellipsoids. Finally, two examples with practical application background are presented to show the validity and usability of the proposed methods.

This article investigates the hierarchical sliding-mode surface (HSMS)-based adaptive critic tracking control problem for nonlinear multiplayer zero-sum games (ZSGs). First, a generalized fuzzy hyperbolic model-based identifier is employed to approximate the unknown nonlinear functions. According to the derived data-driven model, a static control policy is proposed to transform the considered optimal tracking control problem into an optimal regulation control problem of an induced error system. Subsequently, based on sliding mode control technology and the concept of hierarchical design, a novel optimal feedback control policy is developed to regulate the tracking error by minimizing a performance index function related to the HSMS. The solution to the Hamilton–Jacobi–Isaacs equation of nonlinear multiplayer ZSGs is obtained via the HSMS-based critic neural network. Furthermore, the historical stored data are utilized to conquer the difficulty associated with the persistence of excitation conditions. Based on Lyapunov stability theory, it is strictly proven that the tracking error and the critic neural network weight are uniformly ultimately bounded. Finally, two examples are presented to demonstrate the effectiveness of the proposed control scheme.

This article investigates the finite-time secure filter design of fuzzy switched cyber-physical systems equipped with a resource-constraint network that may undergo false data injection attacks (FDIAs). To strike a higher level balance between the resource consumption and filtering performance, a multidomain probabilistic event-triggered mechanism (MDPETM) is initially developed. And the mode mismatched phenomenon between the filter and the system is characterized through a delayed switching signal. Based on the MDPETM and a virtual delay partitioning approach, fuzzy mismatched secure filters are first devised whose modes could differ from the system. Then, filter-mode-dependent Lyapunov functionals are created to obtain new sufficient criteria such that the filtering error achieves finite-time boundedness with extended dissipativity subject to admissible FDIAs. The filter gains are obtained by solving a set of convex optimization problems. Finally, an application-oriented example is employed to test the effectiveness and advantages of the proposed results.

The security control of a vehicle platoon depends on the network topology and communication quality. Denial of service (DoS) attacks force vehicle formations to destabilize by disrupting digital communications between vehicles. To this end, this paper develops a resilient distributed event-triggered security control strategy to resist the malicious impact of DoS attacks on connected vehicles. By designing a distributed event-triggered mechanism based on sampled data, the controller updating frequency is reduced, thereby promoting utilization rate of network resources. Since the end of the attack is not the sampling instant, this may prolong the duration of the attack. To overcome this problem, a switched sampled-data scheme is proposed to ensure that the control signal acts on the system immediately when the attack ends. Then, a switched system model with artificial delay is established to capture the sampling period, event triggering mechanism and DoS attack purpose. With the analysis methods of switched system and time-delay system, sufficient conditions are obtained to guarantee the same safe distance and speed between vehicles, so that the connected vehicle system can achieve the desired tracking effect. A co-design approach is given with respect to controller gains and triggering parameter. Finally, simulation and experimental studies are provided to demonstrate the effectiveness of the proposed security control method.

This article presents a global adaptive neural-network-based control algorithm for disturbed pure-feedback nonlinear systems to achieve zero tracking error in a predefined time. Different from the traditional works that only solve the semiglobal bounded tracking problem for pure-feedback systems, this work not only achieves that the tracking error globally converges to zero but also guarantees that the convergence time can be predefined according to the user specification. In order to get the desired predefined-time controller, first, a mild semibound assumption for nonaffine functions is skillfully proposed so that the design difficulty caused by the structure of pure feedback can be easily solved. Then, we apply the property of radial basis function (RBF) neural networks (NNs) and Young's inequality to derive the upper bound of the term that contains the unknown nonlinear function and external disturbances, and the designed adaptive parameters decide the derived upper and robust control gain. Finally, the predefined-time virtual control inputs are presented whose derivatives are further estimated by utilizing finite-time differentiators. It is strictly proved that the proposed novel predefined-time controller can guarantee that the tracking error globally converges to zero within predefined time and a practical example is shown to verify the effectiveness and practicability of the proposed predefined-time control method.

The problem of finite-time consensus tracking is investigated for multiagent systems with unknown control gain functions and hysteresis quantized inputs. Existing control methods utilizing command filtered technique have certain limitations for unknown control gains. Motived by this, the study is concerned with establishing a novel control strategy based on a modified command filtered technique with estimation-like terms and key compensation terms. Furthermore, fuzzy logic systems participate in command filter design while processing unknown items. It is theoretically testified that the proposed scheme not only guarantees the finite-time consensus tracking performance but also remains that all the resulting closed-loop signals are bounded. Finally, the simulations on numerical and practical examples prove the validity of the control scheme.

This study reports the adaptive asymptotic tracking control problem for flexible-joint (FJ) robot systems, the output tracking error can be kept within the prescribed range in the initial stage of system operation, as time approaches infinity, the asymptotic tracking result can be obtained. The prescribed performance function and the positive integrable time-varying function are introduced simultaneously in the control design of FJ robot systems for the first time. The control scheme is designed under the frame of the adaptive backstepping method and command filtered technique, which successfully avoids the problem of complexity explosion. The radial basis function neural networks are used to deal with unknown uncertainties and the adaptive laws are designed to approximate the norms of weight vectors and approximation errors. Finally, the feasibility of the proposed scheme is proved by the simulation and the experiment of the 2-link FJ robot on the Quanser platform.

This article addresses the fixed-time control problem for high-order nonlinear systems with unknown nonlinear functions and asymmetric output constraint. First, a continuous state-feedback controller is constructed by revamping the adding a power integrator technique (API), which guarantees that the system output does not exceed the pregiven asymmetric output constraint boundary. Second, with the barrier Lyapunov function (BLF) method and the backstepping technique, a fixed-time stability criterion is constructed. It is rigorously proven that the closed-loop systems are fixed-time stable by the Lyapunov theory. The proposed BLF can not only deal with the symmetric output constraint but also cover the traditional logarithm-type BLF as a special case. Finally, a practical application example of the boiler–turbine unit system is offered to certify the availability of the proposed control algorithm.

This article studies the decentralized event-triggered control problem for a class of constrained nonlinear interconnected systems. By assigning a specific cost function for each constrained auxiliary subsystem, the original control problem is equivalently transformed into finding a series of optimal control policies updating in an aperiodic manner, and these optimal event-triggered control laws together constitute the desired decentralized controller. It is strictly proven that the system under consideration is stable in the sense of uniformly ultimate boundedness provided by the solutions of event-triggered Hamilton-Jacobi-Bellman equations. Different from the traditional adaptive critic design methods, we present an identifier-critic network architecture to relax the restrictions posed on the system dynamics, and the actor network commonly used to approximate the optimal control law is circumvented. The weights in the critic network are tuned on the basis of the gradient descent approach as well as the historical data, such that the persistence of excitation condition is no longer needed. The validity of our control scheme is demonstrated through a simulation example.

In this article, finite-time-prescribed performance-based adaptive fuzzy control is considered for a class of strict-feedback systems in the presence of actuator faults and dynamic disturbances. To deal with the difficulties associated with the actuator faults and external disturbance, an adaptive fuzzy fault-tolerant control strategy is introduced. Different from the existing controller design methods, a modified performance function, which is called the finite-time performance function (FTPF), is presented. It is proved that the presented controller can ensure all the signals of the closed-loop system are bounded and the tracking error converges to a predetermined region in finite time. The effectiveness of the presented control scheme is verified through the simulation results.

This article proposes an adaptive neural-network command-filtered tracking control scheme of nonlinear systems with multiple actuator constraints. An equivalent transformation method is introduced to address the impediment from actuator nonlinearity. By utilizing the command filter method, the explosion of complexity problem is addressed. With the help of neural-network approximation, an adaptive neural-network tracking backstepping control strategy via the command filter technique and the backstepping design algorithm is proposed. Based on this scheme, the boundedness of all variables is guaranteed and the output tracking error fluctuates near the origin within a small bounded area. Simulations testify the availability of the designed control strategy.

This article focuses on the bumpless transfer$H_{\infty }$anti-disturbance control problem for switching Markovian LPV systems under a hybrid switching law. A parameter-dependent multiple piecewise disturbance observer-based bumpless transfer control strategy is put forward to reject multiple disturbances and reduce switching bumps. First, a hybrid switching law making full use of determinacy and randomness is proposed to improve the bumpless transfer anti-disturbance level by introducing a fixed dwell time in random switching. Second, a generalized bumpless transfer anti-disturbance specification is given to describe the switching quality at the switching points of switching Markovian LPV systems. Third, a solvability condition is established for the bumpless transfer$H_{\infty }$anti-disturbance control problem, and a parameter-dependent multiple piecewise disturbance observer-based bumpless transfer controller is designed. Finally, an application example has been supplied to demonstrate the availability of the developed method.

This article studies the energy-to-peak filter design problem for the continuous-time nonlinear singular system. A Takagi–Sugeno fuzzy model is constructed to represent the nonlinear plant. The article focuses on the fuzzy filter design for singular system, such that the formulated filtering error system is admissible (regular, impulse-free, and stable) and ensures the corresponding filtering performance in the singular system. By introducing a novel Lyapunov function, design conditions of the fuzzy filter for the continuous-time singular system are proposed in the light of linear matrix inequalities representations. Finally, a practical example and the relevant simulation results are provided to demonstrate the effectiveness and feasibility of the proposed energy-to-peak filter design approach.

In this article, the resilient$\mathcal H_\infty$filter design problem for continuous-time nonlinear systems is addressed. A Takagi–Sugeno fuzzy model with norm-bounded uncertainties is used to represent the nonlinear plant. Meanwhile, the fuzzy filter to be designed is assumed to have gain variations. A useful matrix inequality decoupling approach is proposed to separate the product terms with different types of uncertainties. Then, the resulting design condition of the resilient$\mathcal H_\infty$filter is described by strict linear matrix inequalities. Compared with the existing fuzzy resilient filtering results, the proposed design method shows a strong advantage of reducing design conservatism. Finally, a simulation example is provided to demonstrate the feasibility and the advantage of the proposed design method.

The aim of this article is to establish generic stability conditions for switched nonlinear systems with mode-dependent average dwell-time (MDADT) switching rules via Takagi–Sugeno (T–S) fuzzy modeling, which cover all unstable modes, all stable modes, and partially unstable modes as special cases. Different from traditional multiple and multiple discontinuous Lyapunov function (MDLF) methods, the proposed novel switching-signal-based multiple discontinuous Lyapunov function (SMDLF) approach divides each mode-running interval dynamically according to the actual switching of the system. The developed approach can not only yield less conservative bounds on the MDADT but can also handle all unstable subsystems, which cannot be done by means of the traditional MDLF. A class of SMDLF is constructed for continuous-time switched T–S fuzzy systems, and relaxed stability conditions are presented for the system with both stable and unstable subsystems, using a larger switching signal space. Then, novel stability conditions are deduced for all stable and unstable subsystems in terms of slow and fast MDADT switching signals, respectively. Finally, comparative simulation examples are provided to verify the advantages and effectiveness of the proposed approach.

This article is concerned with adaptive fuzzy control design of nonlinear strict-feedback systems, in which system functions are unknown and system states are subjected to some constant constraints. The main control issue is to design an adaptive fuzzy controller such that the system output follows the reference signal, meanwhile, all the state variables abide by their constrained requirements. Differing from the existing way to build barrier function, the constructed virtual control signal is employed to build the barrier function. Furthermore, a backstepping-based adaptive fuzzy control design process is presented in this article. Compared to the control schemes in the existing literature on full-state constrained systems, the current control scheme has the following advantages: 1) the built virtual control signals in this article are ensured to satisfy corresponding state constraints, rather than assuming that they do, as in the existing articles; 2) the choice of initial values is independent of the norm of the virtual control signal. So, the conservatism of initial value selection is considerably reduced. It is shown that the proposed adaptive fuzzy controller not only ensures perfect tracking performance, but also ensures that all the states abide by the preassigned state constraints during the operation. At last, simulation study further verifies the efficacy of the proposed control strategy.

A policy-iteration-based algorithm is presented in this article for optimal control of unknown continuous-time nonlinear systems subject to bounded inputs by utilizing the adaptive dynamic programming (ADP). Three neural networks (NNs), called critic network, actor network, and quasi-model network, are utilized in the proposed algorithm to give approximations of the control law, the cost function, and the function constituted by partial derivatives of value functions with respect to states and unknown input gain dynamics, respectively. At each iteration, based on the least sum of squares method, the parameters of critic and quasi-model networks will be tuned simultaneously, which eliminates the necessity of separately learning the system model in advance. Then, the control law is improved by satisfying the necessary optimality condition. Then, the proposed algorithm's optimality and convergence properties are exhibited. Finally, the simulation results demonstrate the availability of the proposed algorithm.

This paper investigates event-triggered control for saturated switched systems with switching signals subject to dual-terminal denial-of-service (DoS) attacks. The original switching signals are sampled and transmitted to sub-system and sub-controller through networks, respectively. However, the openness of network may cause switching signal transmission to be affected by possible DoS attacks. Moreover, the existing dual-terminal mismatched network delays and attacks could lead to asynchronous phenomenon. Considering the impacts of the attacked switching signals and delays, a time-delay closed-loop switched system is developed. Subsequently, by utilizing piecewise Lyapunov functional technique, various cases caused by DoS attacks and different delays are analyzed. Then, novel conditions are derived to ensure the stability of closed-loop switched system, and sufficient conditions for the desired controller gains and event-triggering parameters are presented. Finally, simulation examples are given to verify the effectiveness of the proposed method. The simulation results show that in the case of single-terminal and dual-terminal attacks, switched systems both have good anti-attack characteristics.

This paper investigates the asynchronous event-triggered state-feedback control for discrete-time nonlinear singular Markov jump systems subject to reachable set bounding and stochastic cyber-attacks. An event-triggered mechanism is introduced to regulate data transmission and save network resources. While this mechanism brings advantages, it also causes that the mode information of the system and the controller are not synchronized. On the other hand, network communication has brought convenience to signal transmission, and it has also resulted in the network channel being in an open and unreliable environment, which is vulnerable to cyber-attacks. In order to solve these problems, the secure asynchronous controller is designed to effectively resist the influence of attacks. In addition, by using the reachable set analysis approach, the constructed controller ensures that the estimation error is within the boundary of the ellipsoid, and the existence conditions of the required controller are given by solving the linear matrix inequalities. Finally, a simulation example is given to illustrate the effectiveness of the theoretical results.

This article presents an effective method to address the tracking control problem arising in uncertain switched high-order nonlinear system in strict-feedback form. The system under consideration contains unknown functions, which causally make the asymptotic tracking performance difficult to be achieved. By adopting the adding a power integrator approach in the framework of backstepping, a novel tracking controller is developed to guarantee an asymptotic tracking performance in the presence of the approximation error cased by neural networks (NNs) under arbitrary switching. The main contributions lie in: 1) the article for the first time embeds the backstepping technique in designing a kind of discontinuous controller with proportional integral (PI) compensation and 2) with the help of Filippov’s theory, a new defined system described by differential inclusions can be first obtained by taking some transformations, and then a novel nonsmooth Lyapunov function approach along with its upper right Dini derivative technique is applied to complete the construction of the discontinuous controller. Finally, two simulation examples are exhibited to verify the validity of the proposed design techniques.

In this paper, we propose an optimal event-triggered tracking control scheme for completely unknown nonlinear systems under the adaptive dynamic programming (ADP) framework. A data-driven model based on recurrent neural networks (RNNs) is first constructed to model the system uncertainties including the drift dynamics and the input gain matrix, and the modeling error caused by NN approximation is well eliminated through adding a compensation term in the data-driven model such that the model state can asymptotically track the system state. Apart from the traditional construction of optimal tracking controllers, in this paper, an augmented system is developed and a discounted performance function is considered to achieve the optimality. By employing the Bellman optimal principle, an event-triggered tracking Hamilton–Jacobi–Bellman (HJB) equation is then formulated. The approximate solution of the HJB equation can be obtained by virtue of a critic NN, which significantly simplifies the implementation architecture of ADP. Both the historical state data and the current state data are incorporated into the updating of the weight vector in the critic NN, in this circumstance, the persistence of excitation assumption is not needed anymore. It is strictly proven via Lyapunov stability theory that the tracking error state and the critic NN weight are uniformly ultimately bounded. Simulation results examine the validity of the design scheme.

We study the exponential stability of discrete-time neural networks (NNs) with a time-varying delay which contains a few intermittent large delays (LDs). By modeling the considered discrete-time NN as a discrete-time switched NN which contains two subsystems and one of them may be unstable over the LD periods (LDPs), switching techniques are employed to analyze the problem. Delay-dependent exponential stability conditions to check the frequency and the length of the LDs allowed for guaranteeing the exponential stability are proposed by applying a novel Lyapunov-Krasovskii functional (LKF) with LDP-based terms, Wirtinger-based summation inequality, and reciprocally convex combination technique. Based on these conditions, associated evaluation algorithms are developed. Finally, two numerical examples are provided to demonstrate the effectiveness of the proposed method.

This article investigates the problem ofH∞dynamic output feedback control for a class of discrete-time nonlinear systems with quantization and stochastic communication protocol (SCP) via Takagi-Sugeno rules. A fuzzy Markov jump model is used to describe the output feedback controller with the measurement output signal quantized by the dynamic quantization strategy and the SCP scheduling behavior for the quantized signal transferring to the controller described via a Markov chain. The purpose of the addressed problem is to design a dynamic output feedback controller such that stability andH∞performance for the closed-loop system are guaranteed. For this purpose, the corresponding design conditions for the output feedback controller and the dynamic quantization parameters are presented in terms of solutions to a set of linear matrix inequalities. Finally, two simulation examples are given to prove the effectiveness of the proposed design method.

This paper is devoted to the interval type-2 (IT2) fuzzy exponential stability for nonlinear networked sampled-data control systems with random communication links. By using the input delay approach, the designed networked IT2 fuzzy sampled-data controller can be applied in the continuous-time controlled plant, and the continuous-time Lyapunov-Krasovskii functional (LKF) is constructed. Based on the LKF theory, the stability analysis is conducted in the sense of mathematical expectation, and the sufficient conditions are determined in terms of the linear matrix inequalities. Then, the IT2 fuzzy networked controller is developed such that the networked closed-loop control system is exponentially stable. Finally, the simulation results are used to demonstrate the efficiency of the proposed design. The merits of the proposed algorithm are also shown by some comparative results.

This article considers the problem of event-triggered optimal control for discrete-time switched nonlinear systems with constrained control input. First, an event-triggered condition is given to make the closed-loop switched system asymptotically stable. Second, a novel method, event-triggered heuristic dynamic programming (ETHDP), is applied to derive the optimal control policy. Two neural networks (NNs) are utilized to approximate the value function and control law, respectively. When the event-triggered condition is violated, the weights of the two NNs are updated, which can decrease the networks calculation and transmission load notably. A proof of the convergence of the ETHDP is also carried out. Finally, the effectiveness of the proposed method is verified by an example.

This article is concerned with the exponential stability and ℓ1-gain performance analysis of discrete-time switched positive systems (DTSPSs) under mode-dependent average dwell time (MDADT) switching. A novel linear copositive Lyapunov function, which is both quasi-time-dependent and mode-dependent, is designed for the stability and performance analysis. Stability conditions are developed such that the considered DTSPS is exponentially stable and also attains an attenuation performance. The solved conditions for the controllers design are presented in terms of linear programming (LP), and are both quasi-time-dependent and mode-dependent. Two examples are organized to validate the effectiveness of the proposed scheme finally.

In this article, the issue of adaptive neural fault-tolerant control (FTC) is addressed for a class of uncertain switched nonstrict-feedback nonlinear systems with unmodeled dynamics and unmeasurable states. In such a system, the uncertain nonlinear parts are identified by radial basis function (RBF) neural networks (NNs). Also, with the help of the structural characteristics of RBF NNs, the violation between the nontsrict-feedback form and backstepping method is tackled. Then, based on the small-gain technique, input-to-state practical stability (ISpS) theory, and common Lyapunov function (CLF) approach, an adaptive fault-tolerant tracking controller with only three adaptive laws is developed by designing an observer. It is shown that the designed controller can ensure that all the closed-loop signals are bounded under arbitrary switching, while the tracking error can converge to a small area of the origin. Finally, two simulation examples are provided to demonstrate the feasibility of the suggested control approach.

This article investigates the exponential stability of generalized neural networks (GNNs) with a time-varying delay. Different from the literatures on the similar topic, the considered time delay contains a few intermittent large-delay periods (LDPs). A new approach is proposed to determine how frequent and how long the LDPs are allowed for guaranteeing the exponential stability by using switching techniques. The GNN is first modeled as a switched time-delay system which may include an unstable subsystem. Then, based on a novel Lyapunov–Krasovskii functional with LDP-based terms, a delay-dependent exponential stability criterion and associated evaluation algorithm are developed. Finally, two numerical examples are provided to show the effectiveness of the proposed method.

This paper addresses the trajectory tracking control problem of a class of nonstrict-feedback nonlinear systems with the actuator faults. The functional relationship in the affine form between the nonlinear functions with whole state and error variables is established by using the structure consistency of intermediate control signals and the variable-partition technique. The fuzzy control and adaptive backstepping schemes are applied to construct an improved fault-tolerant controller without requiring the specific knowledge of control gains and actuator faults, including both stuck constant value and loss of effectiveness. The proposed fault-tolerant controller ensures that all signals in the closed-loop system are semiglobally practically finite-time stable and the tracking error remains in a small neighborhood of the origin after a finite period of time. The developed control method is verified through two numerical examples.

This paper is concerned with the problem of interval type-2 (IT2) fuzzy sampled-data H∞control for nonlinear networked control systems with parameter uncertainties, data dropout, and transmission delay. The IT2 fuzzy system used to describe the networked control systems and the IT2 networked sampled-data controller implemented by a zero-order holder is connected in the closed-loop system. By means of the input delay approach, the resulting closed-loop system is converted into a continuous-time delayed system. Subsequently, the continuous-time Lyapunov-Krasovskii functional theory is used to carry out the stability analysis. Some slack matrices and the bound information in membership functions are introduced to obtain the relaxed sufficient conditions formed by the linear matrix inequalities, which guarantee the anticipant H∞performance. Finally, the efficiency and merits of the proposed design method are verified by four practical systems.

The problem of asymptotic tracking control for a class of uncertain switched nonlinear systems under fuzzy approximation framework is solved in this paper. Superior to most existing results based on fuzzy adaptive control strategy that can only achieve bounded error tracking performance, our proposed control scheme can guarantee the local asymptotic tracking performance for the uncertain switched nonlinear systems under consideration. This is accomplished by constructing a nonsmooth Lyapunov function and introducing a novel discontinuous controller with dynamic feedback compensator in the design procedure. Meanwhile, some concepts, such as differential inclusion and set-valued map, are introduced to theoretically verify the local asymptotic tracking performance of the systems with our proposed controller. With the help of set-valued Lie derivative, the common virtual control functions, the desired controller, and the adaptive laws can be precisely constructed. Finally, simulation results are given to show the effectiveness of the proposed method.

This paper investigates a stabilization problem for linear systems subject to saturation and time-varying delays in the control input, where the delay takes both large and small values in an alternating manner. Based on the delay values, the considered system is described by a switched system including a stabilizable subsystem and an unstabilizable subsystem. By using Lyapunov-Krasovskii functional method, a time-dependent switching rule orchestrating the proposed controllers is established to guarantee the regional stabilization of the closed-loop system. To obtain a domain of attraction as large as possible, an optimization problem is formulated in the form of linear matrix inequalities. Numerical examples and simulations are provided to demonstrate the effectiveness of the proposed method.

In this paper, the problems of stability and l2-gain analysis are investigated for discrete-time switched systems with mode-dependent average dwell time (DT). By proposing a novel multiple discontinuous Lyapunov function (MDLF) approach, the conditions of stability and weighted l2-gain are established for nonlinear systems. Next, the corresponding results for linear switched systems are also proposed by using MDLF. It turns out that our proposed results provide smaller bounds on the DT. Some simulation results are given to show the validity of our approaches.

This paper reports an effective quintic polynomial-based trajectory planning approach and a robust tube-based model predictive control (RTMPC) method for an underactuated tractor-trailer vehicle system during lane change maneuver. To begin with, a time-based quintic polynomial function is introduced for implementation of trajectory planning, which takes vehicle system safety, comfort, and traffic efficiency into account. After that, an RTMPC scheme, consisting of a nominal system-oriented model predictive controller and an ancillary feedback control law, is proposed to construct posture controller, such that favorable transient performances can be achieved for the trajectory tracking process. Lastly, the simulation results confirm that the proposed lane change trajectory planning and tracking control methods can effectively perform the lane change behaviors for tractor-trailer vehicles.

Point stabilization control of wheeled inverted pendulum (WIP) vehicles remains a challenge because of the existing nonholonomic and underactuated characteristics. This paper proposes a point stabilization control method for the WIP vehicle based on motion planning, where a point-to-point motion is achieved by performing pivot steering and longitudinal motion successively to avoid the nonholonomic constraint. Specifically, to handle the underactuated problem, a kinematic coupling-based longitudinal trajectory planning approach is presented, by which both precise longitudinal movement and stability of vehicle body can be guaranteed. Moreover, a mathematical model of parameters optimization problem is deduced with strict analysis, and then the particle swarm optimization algorithm is used to obtain the appropriate trajectory parameters, which ensures the good performance of the trajectory planning method. On this basis, the point stabilization of the WIP vehicle can be effectively realized through a commonly used proportional-integral-derivative control approach. Numerical simulation and experimental study are both conducted to demonstrate the feasibility and effectiveness of the proposed method.

This paper investigates the problems of backstepping control for a class of nonsmooth nonlinear systems. With the help of set-valued functions (or maps) and set-valued derivatives, a new Lyapunov criterion is developed for nonsmooth systems. The concept of semi-globally uniformly ultimately bounded (SGUUB) stability that is widely used for smooth nonlinear systems in lower triangular form is, for the first time, applied to the nonsmooth case, and such a concept provides a theory foundation for the subsequent backstepping control design. Then, two types of continuous controllers are designed based on the backstepping technique and adaptive neural network (NN) algorithm. In the light of Cellina approximate selection theorem and smooth approximation theorem for Lipschitz functions, the system under investigation is first transformed into an equivalent model. Then, the first type of controller can be efficiently designed by using the traditional backstepping technique. On the other hand, when the unknown uncertainty is taken into account in the equivalent systems, another type of controller is designed based on adaptive NN control strategy. It is proved that in both cases, all the closed-loop signals are SGUUB by using our proposed controllers. Finally, a numerical example with two types of controllers is supplied to show the availability of the provided control schemes.

This paper investigates the problem of adaptive fuzzy tracking control for a class of switched uncertain nonlinear systems with unstable inverse dynamics via time-dependent switching. Adaptive backstepping technique and fast average dwell time switching are exploited to set up the adaptive fuzzy control scheme for the systems under consideration. It is shown that the designed controllers and switching signals can ensure that all the signals remain bounded and the tracking error converges to a small neighborhood of the origin. Finally, simulation results are presented to demonstrate the effectiveness of the proposed design method.

This paper proposes a solution to adaptive output-feedback control for a class of nonsmooth nonlinear systems. First, the concept of semiglobally uniformly ultimately bounded (SGUUB) stability that has been widely used for smooth nonlinear systems with lower triangular systems is extended to the nonsmooth systems. Then by resorting to set-valued maps and set-valued derivatives, a new Lyapunov criterion ensuring the SGUUB stability is developed for nonsmooth nonlinear systems, which establishes the theory foundation for the subsequent backstepping control design. With the help of Cellina approximate selection theorem and smooth approximation theorem for Lipschitz functions, the system under investigation is first transformed into an equivalent model. In the sequel, exploring some efficient techniques, an adaptive fuzzy output-feedback controller is constructed for the systems under consideration by utilizing an appropriate observer and the approximation ability of fuzzy systems. Finally, a numerical example is given to show the effectiveness of the proposed control method.

This correspondence paper is concerned with observer design problems for a class of unknown input switched descriptor systems under average dwell time switching signals. The switched descriptor system under consideration is first transformed into a general switched linear system, and then a switched reduced-order observer that can asymptotically estimate the system state without suffering the influence from the unknown inputs is developed. Besides, a high-order sliding mode observer is introduced to obtain the estimations of the output derivatives by using the system measured outputs. On the basis of estimations of the states and output derivatives, an unknown input reconstructing method is provided. Finally, two examples are presented to verify the effectiveness of the proposed approaches.

In this paper, the distributed consensus problem for a class of multiple Euler-Lagrange systems is solved. The considered issues are: 1) sampled-data information; 2) transmission delay; and 3) data packet dropouts. The considered mathematical models can describe a large number of practical systems in the actual engineering. In particular, all of the information exchanges are modeled by a sample-and-hold mechanism, which is more reliable and practical in applications. Moreover, this framework can deal with time-varying transmission delays and data packet dropouts by taking into account the limited communication capacity of information exchanges. By utilizing model transformation and applying the Lyapunov-Krasovskii functional method, sufficient conditions are first established with single-packet information exchanges to ensure that the networked Euler-Lagrange systems can achieve consensus under undirected communication topology. Then, the obtained results are further extended to the multiple-packet transmission case. Finally, an example of four two-link manipulators with time-varying transmission delays and data packet dropouts is addressed to verify the effectiveness and applicability of our theoretical results.Note to Practitioners-The motivation of this paper is to investigate a practical networking strategy for the cooperative control of multiple Euler-Lagrange systems that have been widely applied in modeling robotic manipulators, autonomous underwater vehicles, and spacecrafts. Existing approaches are mainly based on the continuous-time communication network, which are difficult to be implemented in the real-world applications. In addition, energy consumption problems for multi-agent systems have received increasing attention in recent years, which also motivate us to carry out the present study. Since continuous-time communications inevitably consume much energy, how to find an effective way to reduce energy cost is an urgent task. Therefore, this paper presents a novel method for communications among multiple Euler-Lagrange systems based on the sampled-data information, where discrete-time communications are used instead of continuous-time communications. Another advantage of the proposed method is that it can significantly reduce the transmission energy consumption. Furthermore, both transmission delays and data packet dropouts are taken into account which make our results more applicable in practice.

This paper deals with adaptive neural tracking control design for a class of switched high-order stochastic nonlinear systems with unknown uncertainties and arbitrary deterministic switching. The considered issues are: 1) completely unknown uncertainties; 2) stochastic disturbances; and 3) high-order nonstrict-feedback system structure. The considered mathematical models can represent many practical systems in the actual engineering. By adopting the approximation ability of neural networks, common stochastic Lyapunov function method together with adding an improved power integrator technique, an adaptive state feedback controller with multiple adaptive laws is systematically designed for the systems. Subsequently, a controller with only two adaptive laws is proposed to solve the problem of over parameterization. Under the designed controllers, all the signals in the closed-loop system are bounded-input bounded-output stable in probability, and the system output can almost surely track the target trajectory within a specified bounded error. Finally, simulation results are presented to show the effectiveness of the proposed approaches.

This paper is concerned with the stability analysis of positive polynomial-fuzzy-model-based control systems with time delay. Each of the polynomial fuzzy model and the polynomial fuzzy controller is allowed to have its own set of premise membership functions, which can improve the design and realization flexibility. Conditions showing the positivity of the system are first obtained. Then, to deal with the difficulty resulting from the mismatched premise membership functions to the stability/stabilization analysis, approximated membership functions representing the original ones are constructed, and the information of membership functions is brought into the stability conditions. This way, the stability conditions can be relaxed, since the extra knowledge on the system is considered. Stability conditions are obtained by using the sum-of squares approach based on the Lyapunov stability theory. A simulation example is provided to verify the effectiveness of this method.

In this paper, an adaptive fuzzy hierarchical sliding-mode control method for a class of multiinput multioutput unknown nonlinear time-delay systems with input saturation is proposed. The studied system is first transformed into an equivalent system. Subsequently, based on sliding-mode control technology and the concept of hierarchical design, a set of adaptive fuzzy hierarchical sliding-mode controllers are designed for the new defined system by using fuzzy systems to approximate uncertain functions and compensate input saturation. Choosing an appropriate Lyapunov-Krasovskii function, it is theoretically proved that all the signals in the closed-loop system together with the proposed sliding surfaces are uniformly ultimately bounded under our designed adaptive fuzzy controllers. Simulation results demonstrate the effectiveness of the proposed design techniques for the systems under consideration.

This paper proposes an adaptive sliding mode control method for a class of nonstrict-feedback nonlinear systems where some widely used restrictions on system structure are relaxed. Based on the calculus principle, the original system is first transformed into a new defined system. Then, by using sliding mode control technology and the concept of hierarchical design, a series of control signals are sequentially designed for the new defined system where radial basis function neural networks are used to approximate the unknown functions. Based on the Lyapunov stability theory, the closed-loop system together with the proposed sliding surfaces is proved to be uniformly ultimately bounded under our designed adaptive neural controller. The main contributions of this paper lie in that some strict restrictions on uncertain system functions are removed; a hierarchical control method is proposed for the considered systems, which can avoid the problem of “causes and consequences” that may be encountered by using traditional backstepping design method; and the proposed control method is also available for underactuated nonlinear systems. Finally, simulation results demonstrate the effectiveness of the proposed design techniques.

In this paper, tracking control problems are investigated for a class of uncertain nonlinear systems in lower triangular form. First, a state-feedback controller is designed by using adaptive backstepping technique and the universal approximation ability of fuzzy logic systems. During the design procedure, a developed method with less computation is proposed by constructing one maximum adaptive parameter. Furthermore, adaptive controllers with nonsymmetric dead-zone are also designed for the systems. Then, a sampled-data control scheme is presented to discretize the obtained continuous-time controller by using the forward Euler method. It is shown that both proposed continuous and discrete controllers can ensure that the system output tracks the target signal with a small bounded error and the other closed-loop signals remain bounded. Two simulation examples are presented to verify the effectiveness and applicability of the proposed new design techniques.

In this paper, an adaptive neural output-feedback tracking controller is designed for a class of multiple-input and multiple-output nonstrict-feedback nonlinear systems with time delay. The system coefficient and uncertain functions of our considered systems are both unknown. By employing neural networks to approximate the unknown function entries, and constructing a new input-driven filter, a backstepping design method of tracking controller is developed for the systems under consideration. The proposed controller can guarantee that all the signals in the closed-loop systems are ultimately bounded, and the time-varying target signal can be tracked within a small error as well. The main contributions of this paper lie in that the systems under consideration are more general, and an effective design procedure of output-feedback controller is developed for the considered systems, which is more applicable in practice. Simulation results demonstrate the efficiency of the proposed algorithm.

In this paper, the problem of switching stabilization for a class of switched nonlinear systems is studied by using average dwell time (ADT) switching, where the subsystems are possibly all unstable. First, a new concept of ADT is given, which is different from the traditional definition of ADT. Based on the new proposed switching signals, a sufficient condition of stabilization for switched nonlinear systems with unstable subsystems is derived. Then, the T-S fuzzy modeling approach is applied to represent the underlying nonlinear system to make the obtained condition easily verified. A novel multiple quadratic Lyapunov function approach is also proposed, by which some conditions are provided in terms of a set of linear matrix inequalities to guarantee the derived T-S fuzzy system to be asymptotically stable. Finally, a numerical example is given to demonstrate the effectiveness of our developed results.

This paper is concerned with the control problem for a class of switched nonlinear systems possibly composed of all unstable modes by using time-controlled switching signals. To tackle the problem, a new mode-dependent average dwell time (MDADT) switching property is proposed, which is different from the existing one in the literature. Then, the stabilization condition under such MDADT switching signals is established for the switched nonlinear systems with possibly all unstable subsystems. By proposing a class of time-scheduled multiple quadratic Lyapunov function and applying T–S fuzzy models to represent the underlying nonlinear subsystems, numerically easily verified stabilization conditions are further derived in the form of linear matrix inequalities. A numerical example is finally provided to illustrate the effectiveness of the obtained theoretical results.

This brief is concerned with synchronization control of a class of neural networks with state-dependent coefficient matrices. Being different from the existing drive-response neural networks in the literature, a novel model of drive-response neural networks is established. The concepts of uniformly ultimately bounded (UUB) synchronization and convex hull Lyapunov function are introduced. Then, by using the convex hull Lyapunov function approach, the UUB synchronization design of the drive-response neural networks is proposed, and a delay-independent control law guaranteeing the bounded synchronization of the neural networks is constructed. All present conditions are formulated in terms of bilinear matrix inequalities. By comparison, it is shown that the neural networks obtained in this brief are less conservative than those ones in the literature, and the bounded synchronization is suitable for the novel drive-response neural networks. Finally, an illustrative example is given to verify the validity of the obtained results.

This brief is concerned with the problem of intelligent tracking control for a class of high-order nonlinear systems with completely unknown nonlinearities. An intelligent adaptive control algorithm is presented by combining the adaptive backstepping technique with the neural networks' approximation ability. It is shown that the practical output tracking performance of the system is achieved using the proposed state-feedback controller under two mild assumptions. In particular, by introducing a parameter in the derivations, the tracking error between the time-varying target signal and the output can be reduced via tuning the controller design parameters. Moreover, in order to solve the problem of overparameterization, which is a common issue in adaptive control design, a controller with one adaptive law is also designed. Finally, simulation results are given to show the effectiveness of the theoretical approaches and the potential of the proposed new design techniques.

In this paper, the problems of state estimation are investigated for switched linear systems with average dwell time (ADT) switching in both continuous-time and discrete-time contexts. First, a set of mode-dependent Luenberger-type observers is designed subject to the ADT switching that is synchronous with the switching of the estimated systems. Then, a more practical case of the delayed observers is also considered, which implies that the switching of the multiple-mode observer to be designed has a lag to the switching of the estimated systems. In this case, the asynchronous switching signals are combined as a preliminary attempt, upon which sufficient conditions for the existence of the Luenberger-type observers and the corresponding ADT switching are formulated in terms of a set of linear matrix inequalities. Finally, the proposed approaches are applied to the state estimation of electronic circuits to demonstrate the effectiveness and applicability.

This paper investigates the consensus problem of multiple Euler-Lagrange systems under directed topology. Unlike the common assumptions on continuous-time information exchanges, a more realistic sampled-data communication strategy is proposed with probabilistic occurrence of time-varying delays. Both of the sampling period and the delays are assumed to be time-varying, which is more general in some practical situations. In addition, the relative coordinate derivative information is not required in the distributed controllers such that the communication network burden can be further reduced. In particular, a distinct feature of the proposed scheme lies in the fact that it can effectively reduce the energy consumption. By employing the stochastic analysis techniques, sufficient conditions are established to guarantee that the consensus can be achieved. Finally, a numerical example is provided to illustrate the applicability and benefits of the theoretical results.

This paper presents the stability analysis of polynomial fuzzy-model-based (PFMB) control systems, in which both the polynomial fuzzy model and the polynomial fuzzy controller are allowed to have their own set of premise membership functions. In order to address the input nonlinearity, the control signal is considered to be bounded by a sector with nonlinear bounds. These nonlinear lower and upper bounds of the sector are constructed by combining local bounds using fuzzy blending such that local information of input nonlinearity can be taken into account. With the consideration of imperfectly matched membership functions and input nonlinearity, the applicability of the PFMB control scheme can be further enhanced. To facilitate the stability analysis, a general form of approximated membership functions representing the original ones is introduced. As a result, approximated membership functions can be brought into the stability analysis leading to relaxed stability conditions. The sum-of-squares approach is employed to obtain the stability conditions based on Lyapunov stability theory. Simulation examples are presented to demonstrate the feasibility of the proposed method.

In this paper, the problem of induced L2disturbance attenuation control is investigated for discrete-time Takagi-Sugeno (T-S) fuzzy delta operator systems with time-varying delays and disturbance input via an input-output approach. A model transformation method is utilized to approximate the time-varying delay in T-S fuzzy delta operator systems. By applying the scaled small gain (SSG) theorem and Lyapunov-Krasovskii functional approach, a sufficient condition is established to guarantee that the closed-loop system is asymptotically stable and has an induced L2disturbance attenuation performance. The existence condition of the dynamic output-feedback controller can be solved via convex optimization problems. Finally, simulation results are given to demonstrate the feasibility and effectiveness of the proposed method.

The consensus problem of networked Euler-Lagrange systems is studied in this paper. Different from the continuous-time communication setting, a novel sampled-data communication strategy is proposed, which is more reliable and applicable in practice. In particular, the sampling period is described by a probabilistic model. Furthermore, the communication network burden is lower since only the coordinate information is required to be exchanged. By efficiently utilizing the communication network to transfer the sampled-data information, an advantage of our consensus protocol is that the communication energy consumption can be efficiently reduced. Based on the Lyapunov-Krasovskii method, sufficient conditions are derived to ensure that the consensus can be achieved. Finally, a two-link manipulator example is provided to demonstrate the effectiveness and advantage of our proposed method.

In this paper, novel stability conditions for Takagi-Sugeno (T-S) fuzzy systems are presented. The so-called nonquadratic membership-dependent Lyapunov function is first proposed, which is formulated in a higher order form of both the system states and the normalized membership functions than existing techniques in the literature. Then, new membership-dependent stability conditions are developed by the new Lyapunov function approach. It is shown that the conservativeness of the obtained criteria can be further reduced as the degree of the Lyapunov function increases. Two numerical examples are given to demonstrate the effectiveness and less conservativeness of the obtained theoretical results.

Dynamic weather conditions, which mainly include rain and snow, make prevailing algorithms for many applications of outdoor video analysis and computer vision lapse. To remove dynamic weather conditions, the authors propose a pixel‐wise framework combining a detection method with a removal approach. Dynamic weather conditions are detected by a strategy‐driven state transition, which integrates static initialisation using K ‐means clustering with dynamic maintenance of Gaussian mixture model. Moreover, a variable time window is presented for removal of rain and snow. Each component of the framework is addressed using detailed descriptions of corresponding algorithms. Experiments demonstrate the effectiveness of the method on detection and removal of dynamic weather conditions.

In this study, the state estimation problem for a class of slowly switched positive linear systems (SPLS) is investigated. A multiple linear copositive Lyapunov function (MLCLF) is preliminarily established, by which the state estimator is designed for the underlying systems with an average dwell-time switching. Then, by reducing MLCLF to the common linear copositive Lyapunov function, the results for the SPLS under arbitrary switching can be easily obtained. Finally, a numerical example is given to show the fesibility of the obtained theoretical findings.

A background estimation system, which integrates pixel-level features with a region-level one and combines short-term and long-term analysis of videos in outdoor illumination variations, is proposed for accurate foreground detection. Firstly, we discuss autocorrelation-based features for identification of the presence of foreground and outdoor illumination variations in short-term sequences, and propose an adaptive threshold learning approach insensitive to inner-pixel fast illumination variation based on histograms of intensity differences between successive frames. Then, we employ a pixel-wise rapid autoregressive model against gradual illumination change for background estimation in long-term sequence. Finally, we devise a texture measure to eliminate the regional effect of fast illumination variation. The effectiveness of our system is demonstrated using experiments on foreground detection in videos with various illumination changes.

Evaluation of different weather conditions provides a first step support for many different applications of outdoor video analysis and computer vision. In this paper, a simple but effective classification method on visual effects of different weather conditions is proposed. Due to the complex manifestations of weather conditions, we firstly provide a two-stage classification scheme. Then, we extract spatio-temporal and chromatic features to represent different weather situations. Using these features, we develop a classifier based on an experiential decision binary tree associated with C-SVM. The experimental results of classification on our newly-built video dataset indicate the effectiveness of our method.