Conventional fault-tolerant control (FTC) schemes typically assume the exponent of the faulty input to be 1, overlooking its impact on actuator power. In this article, we propose a novel FTC strategy that extends the exponent to any positive odd integer, thus capturing higher-order fault effects. In addition, by integrating a Gaussian function to modify the constraint boundaries, a novel suction-cup-type prescribed performance function is proposed. Unlike existing prescribed performance functions, this design uses a suction cup module to regulate output overshoot without requiring asymmetric design. This design is globally effective, eliminating the initial feasibility conditions. Simulation results validate the effectiveness of the proposed scheme.

This article proposes a novel observer-based adaptive neural network-based resilient consensus control approach to address hybrid cyberattacks, disturbances, and nonlinear dynamics in nonlinear leader-following multiagent systems (MASs). Specifically, a dimension expansion methodology is developed to dynamically model and compensate for false data injection (FDI) attacks, while denial-of-service (DoS) attacks are probabilistically characterized via Bernoulli variables, forming a comprehensive hybrid attack mitigation strategy. Then, a cascaded observer is designed, integrating dimension-extended system modeling with disturbance decoupling to simultaneously estimate system states and external disturbances with high precision. Furthermore, an adaptive neural network-based approximation scheme is employed to handle system nonlinearities, eliminating the conservatism of Lipschitz-based methods while enhancing robustness in complex environments. Finally, the simulation result validates that the proposed control method achieves resilient consensus of leader-following MASs under hybrid cyberattacks, disturbances, and nonlinear dynamics.

Formation control of differential mobile robots (DMRs) has attracted considerable interest owing to its broad potential applications in industrial transportation, service robotics, and autonomous cooperative tasks. Accordingly, this work investigates the leader–follower finite-time formation control for DMRs under a directed communication graph. To address the kinematic underactuation problem, the heading angle is excluded from the control design, and a coordinate transformation is introduced to derive a generalized system representation for DMRs. A chattering-free fixed-time observer is developed to estimate the input to the leader, and a nonlinear distributed protocol is proposed to achieve practical finite-time formation control. Finally, two experiments are conducted under three scenarios to validate the effectiveness of the proposed protocol for real-world applications.

This paper proposes a fuzzy control scheme that enforces a unified prescribed performance for full-state errors in input-saturated systems with external disturbances. The proposed scheme is characterized by three key innovations: First, a series of functional transformations are designed to guarantee multiple performance behaviors within a unified control framework, enabling desired behaviors through parameter selection without controller redesign. Second, a novel performance function for virtual errors completely eliminates strict initial value constraints, thereby removing offline verification computations and streamlining the design/implementation process. Third, an error-driven mechanism is developed to prevent singularities induced by input saturation and disturbances. Unlike existing flexible prescribed performance control methods that rely on a control input-driven mechanism, this mechanism offers two distinct advantages: it directly adjusts only a single boundary for a more straightforward adjustment, and operates without dependency on auxiliary system integration. This design eliminates adjustment delays while minimizing performance degradation caused by boundary relaxation. Simulations confirm the scheme’s efficacy and superiority.

This study examines the output tracking control of discrete-time networked semi-Markov jump systems (SMJSs) under cyber-attacks in the framework of finite-time control methodology. Different from most networked systems that employ single-channel communication, this work considers the case of multi-channel communication in the controller-to-actuator networks. Aimed at better reflecting the practical situation, a type of hybrid attacks is taken into consideration, which is a mixture of denial-of-service attacks and false data injection attacks. Subsequently, the dynamic characteristics of hybrid attacks among multiple channels are modeled by two stochastic processes. The goal is to design a state feedback controller such that the resulting closed-loop system is not only finite-time boundedness with dissipative performance but also has robustness against hybrid attacks. Finally, the effectiveness of the proposed novel controller design method is verified by an illustrative example.

This paper proposes a novel prescribed performance control scheme for stochastic micro-electro-mechanical system (MEMS) gyroscopes, addressing three critical issues overlooked by existing methods: control torque oscillation during rapid convergence, deviation in steady-state tracking errors in a global asymmetric design, and violation of monotonic constraints due to insufficient input capability. To tackle these challenges, the paper proposes a quadratic prescribed performance function design, a local asymmetric constraint design, and a customized non-monotonic design. These innovations effectively resolve the technical difficulties and establish comprehensive performance specifications for stochastic MEMS gyroscopes. The proposed scheme ensures boundedness in probability for all closed-loop signals and convergence of the tracking error to an arbitrarily small residual within a prescribed time. Simulation results confirm the effectiveness and superiority of the scheme.

This article addresses the periodic event-triggered adaptive output feedback control problem for networked system with unknown nonlinear dynamics. Based on the output-dependent periodic event-triggered mechanism (PETM), a nonlinear observer is designed to estimate system states, where the fuzzy-logic systems-based approximation method and adaptive technique are employed to approximate and compensate for uncertainties. To enhance resource utilization efficiency in communication channels, a new observer and parameter estimators-dependent parallel PETM is proposed to schedule intermittent packet transmission. Then, a digital controller is designed to reduce frequent control updating. By constructing novel piecewise Lyapunov functional, it is proven that the underlying system states, the observation error signals and parameter estimation signals are semiglobally uniformly ultimately bounded. In addition, the proposed control method is applied to solve the stabilization problem of networked interconnected systems. Finally, a numerical simulation is performed to show the efficiency of the developed control method.

This research proposes a neural network-based super-twisting controller for robot joints. A modified fast nonsingular terminal sliding surface is introduced, which not only avoids singularity but also increases the convergence rate of the sliding mode control. To address the challenge of system uncertainty modeling, a type-2 fuzzy single hidden layer recurrent neural network (T2FSHLRNN) is proposed. The T2FSHLRNN, configured as a weighted combination of a type-2 fuzzy neural network and a single hidden layer network, demonstrates strong global learning ability. Leveraging its internal and external double-layer feedback mechanism, the network can incorporate both current and previous error information during the approximation process, effectively improving the approximation accuracy and reducing system chattering. Furthermore, an adaptive gain function is proposed and an adaptive terminal super-twisting controller based on T2FSHLRNN (ATSC-T2FSHLRNN) is developed. The system’s stability under unknown disturbance is ensured using Lyapunov synthesis. Based on this, the online parameter learning algorithm for T2FSHLRNN and the variable gains of ATSC are derived. Simulation confirms the effectiveness of the proposed ATSC-T2FSHLRNN.

This article presents a novel learning-based collaborative control framework to ensure communication security and formation safety of nonlinear multiagent systems (MASs) subject to denial-of-service (DoS) attacks, model uncertainties, and barriers in environments. The framework has a distributed and decoupled design at the cyber-layer and the physical layer. A resilient control Lyapunov function-quadratic programming (RCLF-QP)-based observer is first proposed to achieve secure reference state estimation under DoS attacks at the cyber-layer. Based on deep reinforcement learning (RL) and control barrier function (CBF), a safety-critical formation controller is designed at the physical layer to ensure safe collaborations between uncertain agents in dynamic environments. The framework is applied to autonomous vehicles for area scanning formations with barriers in environments. The comparative experimental results demonstrate that the proposed framework can effectively improve the resilience and robustness of the system.

This article focuses on the Pareto optimal issues of nonlinear game systems with asymmetric input saturation under dynamic event-triggered mechanism (DETM). First, the safe control is guaranteed by transforming the system with safety constraints into the one without state constraints utilizing barrier function. The united cost function integrating nonquadratic utility function is constructed to provide the foundation to achieve the Pareto optimal solutions. Then, the adaptive dynamic programming method with concurrent learning is proposed to approximate the Pareto optimal strategies wherein both current and historical data are utilized. To further lessen the consumptions of computation/communication resources, the DETM is integrated into the adaptive algorithm framework which can avoid Zeno phenomena. All the signals of the closed-loop system are proved to be uniformly ultimately bounded. Finally, the simulation results are given to validate the effectiveness of the proposed method from several aspects.

This article conducts the issue of event-triggered reduced-order filtering for continuous-time semi-Markov jump systems with imperfect measurements as well as randomly occurring uncertainties (ROUs). Specifically, the sojourn-time-dependent transition probability matrix (TPM) is presumed to be polytopic and a quantizer is introduced to quantize output signals aiming to reflect the reality. Both ROUs and sensor failures are generated by individual random variables belonging to be mutually independent Bernoulli-distributed white sequences. First, sufficient conditions for the existence of the event-triggered reduced-order filter are obtained by utilizing the dissipativity-based technique to ensure the asymptotical stability with a strictly dissipative performance of the filtering error system. The time-varying TPM is then fractionalized, which enhances the results as stated. Furthermore, the required reduced-order filter parameters are obtained by introducing slack symmetric matrix as well as cone complementarity linearization algorithm. The effectiveness of the suggested event-triggered reduced-order filter design method is shown through simulation results.

This article addresses the problem of event-triggered dynamic output feedback controller design for networked Takagi-Sugeno (T-S) fuzzy systems subject to actuator failure and deception attacks. In order to save network resources effectively, two event-triggered schemes (ETSs) are introduced to test whether the measurement output and control input are transmitted under network communication. While the ETS brings advantages, it also leads to a mismatch between the premise variables of the system and the controller. To solve this problem, an asynchronous premise reconstruct method is considered, which relaxes the condition of the previous results that the premises of the plant and the controller are synchronous. Furthermore, two crucial factors, including actuator failure and deception attacks, are taken into consideration simultaneously. Then, the mean square asymptotic stability conditions of the resultant augmented system are derived by utilizing the Lyapunov stability theory. Besides, controller gains and event-triggered parameters are co-designed with the help of linear matrix inequality techniques. Finally, a cart-damper-spring system and a nonlinear mass-spring-damper mechanical system are presented to verify the theoretical analysis.

This article investigates the asynchronous distributed finite-time$H_{\infty }$filtering problem for nonlinear Markov jump systems over sensor networks under stochastic attacks. The stochastic attacks, called two-channel deception attacks, exist not only between the Markov jump plant and the sensors but also among the sensors. It is assumed that the mode of the filter relies on, but is asynchronous with, that of the Markov jump plant. First, we establish a filtering error system that combines the Markov jump plant with the asynchronous filtering system. Then, we present an asynchronous distributed filter, which ensures the filtering error system mean-square finite-time bounded and satisfies a prescribed$H_{\infty }$performance level under the two-channel attacks. Finally, an example is given to illustrate the effectiveness of the presented filter.

This paper addresses the problem of simultaneous fault detection and control (SFDC) for Markov jump systems (MJSs) over sensor networks. In order to save the bandwidth of the network, round-Robin protocol is adopted for the communication between the sensors and fault detection filter. First, to fully utilize the data of sensor networks, a new distributed system is developed especially for SFDC, and a residual system, which integrates the MJSs with the distributed SFDC system, is also established. Then, by constructing a mode-dependent Lyapunov functional, the conditions for distributed SFDC are derived, which can make the residual system asymptotically mean-square stable and meanwhile satisfy the prescribed H∞performance requirements. Finally, comparison analyses are made by examples to illustrate the effectiveness and superiority of the proposed new design approach.

Filter design for nonlinear systems, especially time delayed nonlinear systems, has always been an important and challenging problem. This brief investigates the filter design problem of nonlinear systems with multiple constraints: time delay, actuator, and sensor faults, and a new adaptive neural network-based filter design method is proposed. Comparing with the existing works where there is a shortcoming that the designed filters contain unknown time delay(s), the design method proposed in this brief overcomes the shortcoming and only the estimation of the unknown time delay exists in the filter. Furthermore, not only the system states can be estimated, but also the unknown time delay with actuator and sensor faults can be estimated in this brief. Finally, simulation results are given to show the effectiveness of the proposed new design method.

This paper suggests a position-tracking algorithm for the outer-loop through the invariant dynamic surface approach for balancing robot applications. The main feature is to devise a dynamic surface representing the target position-tracking performance with variable cut-off frequency. The proposed controller makes the dynamic surface invariant while updating the closed-loop cut-off frequency accordingly with the self-tuner. The closed-loop properties are rigorously analyzed. The experimental verification result shows that the proposed controller establishes the 48% enhancement of the circular tracking performance in comparison with the feedback linearization method, where the LEGO Mindstorms EV3 is used.

This article is concerned with the problem of distributed model predictive control (DMPC) for second-order multiagent systems under event-triggered technique and logarithm quantized communication for a directed topological graph. Considering the limitation of communication bandwidth, a new bounded logarithm quantized communication strategy is proposed to preprocess the information before its transmission, thus reducing the influence of quantization error on the final convergence state. In order to decrease the frequency of control law update and reduce the power consumption, a distributed event-triggered rule is designed to decide when to transmit the information and when to optimize the model predictive control, in which trigger function synthesizes three factors, namely, predictive step, saturation of quantizer, and event-triggered error related with quantized error. The optimal control sequence of DMPC guides the update of controller between two triggering instants. The relationship among the quantization level, event-triggered parameters, and Laplacian matrix is established. Conditions are presented to ensure that all leaders asymptotically converge to a designed formation configuration, while all followers reach to the convex hull of them. Finally, an example is given to illustrate the effectiveness of the proposed methods.

This paper investigates the event-triggered dissipative filtering issue for discrete-time singular neural networks with time-varying delays and Markovian jump parameters. Via event-triggered communication technique, a singular jump neural network (SJNN) model of network-induced delays is first given, and sufficient criteria are then provided to guarantee that the resulting augmented SJNN is stochastically admissible and strictly stochastically dissipative (SASSD) with respect to (Xι, Yι, Zι, δ) by using slack matrix scheme. Furthermore, employing filter equivalent technique, codesigned filter gains, and event-triggered matrices are derived to make sure that the augmented SJNN model is SASSD with respect to (Xι, Yι, Zι, δ). An example is also given to illustrate the effectiveness of the proposed method.

This paper is concerned with the dissipativity analysis and design of discrete Markovian jumping neural networks with sector-bounded nonlinear activation functions and time-varying delays represented by Takagi-Sugeno fuzzy model. The augmented fuzzy neural networks with Markovian jumps are first constructed based on estimator of Luenberger observer type. Then, applying piecewise Lyapunov-Krasovskii functional approach and stochastic analysis technique, a sufficient condition is provided to guarantee that the augmented fuzzy jump neural networks are stochastically dissipative. Moreover, a less conservative criterion is established to solve the dissipative state estimation problem by using matrix decomposition approach. Furthermore, to reduce the computational complexity of the algorithm, a dissipative estimator is designed to ensure stochastic dissipativity of the error fuzzy jump neural networks. As a special case, we have also considered the mixed H∞and passive analysis of fuzzy jump neural networks. All criteria can be formulated in terms of linear matrix inequalities. Finally, two examples are given to show the effectiveness and potential of the new design techniques.

In this brief, the problems of the mixed H-infinity and passivity performance analysis and design are investigated for discrete time-delay neural networks with Markovian jump parameters represented by Takagi-Sugeno fuzzy model. The main purpose of this brief is to design a filter to guarantee that the augmented Markovian jump fuzzy neural networks are stable in mean-square sense and satisfy a prescribed passivity performance index by employing the Lyapunov method and the stochastic analysis technique. Applying the matrix decomposition techniques, sufficient conditions are provided for the solvability of the problems, which can be formulated in terms of linear matrix inequalities. A numerical example is also presented to illustrate the effectiveness of the proposed techniques.

In this paper a neural network model-based predictive control strategy for aircraft systems with unknown parameters is presented. The objective of the paper is to stabilize unknown systems by the adaptive control law through an optimization procedure in which a cost function representing the deviation error between set-points and the predicted outputs obtained from a neural network is minimized. Due to its capability of characterizing dynamic functional relationships and its feedback processing structure, a recurrent neural network is employed as an adaptive estimator for future state values. The neural network training is performed by the dynamic sequential recursive backpropagation learning algorithm, which allows the neural network to be trained online. It is shown that the proposed neural network learning algorithm has potential for designing flight control systems which can compensate for unpredictable changes in an aircraft dynamics over a wide range of flight conditions and other uncertainties.