Fault-Tolerant Synchronization for Memristive Neural Networks With Multiple Actuator Failures.

By using the fault-tolerant control method, the synchronization of memristive neural networks (MNNs) subjected to multiple actuator failures is investigated in this article. The considered actuator failures include the effectiveness failure and the lock-in-place failure, which are different from previous results. First of all, the mathematical expression of the control inputs in the considered system is given by introducing the models of the above two types of actuator failures. Following, two classes of synchronization strategies, which are state feedback control strategies and event-triggered control strategies, are proposed by using some inequality techniques and Lyapunov stability theories. The designed controllers can, respectively, guarantee the realization of synchronizations of the global exponential, the finite-time and the fixed-time for the MNNs by selecting different parameter conditions. Then the estimations of settling times of provided synchronization schemes are computed and the Zeno phenomenon of proposed event-triggered strategies is explicitly excluded. Finally, two experiments are conducted to confirm the availability of given synchronization strategies.

Distributed Robust Fault Estimation for Multiagent Systems Based on Transition Variable Estimator.

This article investigates the distributed robust fault estimation for a class of multiagent systems with actuator faults and nonlinear uncertainties. To estimate the actuator faults and system states simultaneously, a novel transition variable estimator is constructed. Compared with existing similar results, the fault estimator existing condition is not necessary for designing the transition variable estimator. Furthermore, the bounds of the faults and their derivatives can be unknown in designing the estimator for each agent in the system. The parameters of the estimator are calculated by using Schur decomposition and linear matrix inequality algorithm. Finally, the performance of the proposed method is demonstrated through experiments of wheeled mobile robots.

Data-Driven Event-Triggered Adaptive Dynamic Programming Control for Nonlinear Systems With Input Saturation.

This article is devoted to data-driven event-triggered adaptive dynamic programming (ADP) control for nonlinear systems under input saturation. A global optimal data-driven control law is established by the ADP method with a modified index. Compared with the existing constant penalty factor, a dynamic version is constructed to accelerate error convergence. A new triggering mechanism covering existing results as special cases is set up to reduce redundant triggering events caused by emergent factors. The uniformly ultimate boundedness of error system is established by the Lyapunov method. The validity of the presented scheme is verified by two examples.

Multistability and Robustness of Competitive Neural Networks With Time-Varying Delays.

This article is devoted to analyzing the multistability and robustness of competitive neural networks (NNs) with time-varying delays. Based on the geometrical structure of activation functions, some sufficient conditions are proposed to ascertain the coexistence of ∏i=1n(2Ri+1) equilibrium points, ∏i=1n(Ri+1) of them are locally exponentially stable, where n represents a dimension of system and Ri is the parameter related to activation functions. The derived stability results not only involve exponential stability but also include power stability and logarithmical stability. In addition, the robustness of ∏i=1n(Ri+1) stable equilibrium points is discussed in the presence of perturbations. Compared with previous papers, the conclusions proposed in this article are easy to verify and enrich the existing stability theories of competitive NNs. Finally, numerical examples are provided to support theoretical results.

Iterative Learning Control of Constrained Systems With Varying Trial Lengths Under Alignment Condition.

This brief is concerned with iterative learning control (ILC) of constrained multi-input multi-output (MIMO) nonlinear systems under the state alignment condition with varying trial lengths. A modified reference trajectory is constructed to meet the alignment condition by adjusting the reference trajectory to be spatially closed. Resorting to the barrier composite energy function (BCEF) approach, an adaptive ILC scheme is built to guarantee the bounded convergence of the resultant closed-loop system. Illustrative examples are presented to verify the validity of the proposed iteration scheme.

Neural network-based event-triggered data-driven control of disturbed nonlinear systems with quantized input.

This paper is devoted to design an event-triggered data-driven control for a class of disturbed nonlinear systems with quantized input. A uniform quantizer reconstructed with decreasing quantization intervals is employed to reduce the quantization error. A neural network-based estimation strategy is proposed to estimate both the pseudo partial derivative and disturbances. Consequently, an input triggering rule for single-input single-output systems is provided by incorporating the estimated disturbances, the quantization error bound and tracking errors. Resorting to the Lyapunov method, sufficient conditions for synthesized error systems to be uniformly ultimately bounded are presented. The validity of the proposed scheme is demonstrated via a simulation example.

Event-Based Output Quantized Synchronization Control for Multiple Delayed Neural Networks.

This article concentrates on the global exponential synchronization problem of multiple neural networks with time delay by the event-based output quantized coupling control method. In order to reduce the signal transmission cost and avoid the difficulty of obtaining the systems' full states, this article adopts the event-triggered control and output quantized control. A new dynamic event-triggered mechanism is designed, in which the control parameters are time-varying functions. Under weakened coupling matrix conditions, by using a Halanay-type inequality, some simple and easily verified sufficient conditions to ensure the exponential synchronization of multiple neural networks are presented. Moreover, the Zeno behaviors of the system are excluded. Some numerical examples are given to verify the effectiveness of the theoretical analysis in this article.

Event-triggered data-driven control of discrete-time nonlinear systems with unknown disturbance.

This paper is dedicated to event-triggered data-driven control of nonlinear systems with unknown disturbance via model free iterative learning approach. An extended state observer is employed to reconstruct the disturbance in system output. An event-triggered model free iterative learning control strategy is constructed by system input, system output and the reconstructed disturbance. Sufficient conditions are proposed to make the resultant tracking error system be uniform ultimate bounded. Simulation examples are provided to validate the effectiveness of the proposed scheme.

Event-triggered filter design for nonlinear cyber-physical systems subject to deception attacks.

This paper is concerned with an event-triggered filter design for fuzzy-model-based cyber-physical systems with cyber-attacks. Spurious events may be triggered under the conventional event-triggered mechanism (ETM) when the sampling data has a rapid change arising from unpredicted external disturbance. To avoid spurious decisions on data releasing a new ETM is proposed. Furthermore, the communication network is vulnerable to attacks by malicious attackers. Under this scenario, a new resilient filter is designed to ensure the security. Sufficient conditions are established to make the filtering error system asymptotically stable. A numerical example is provided to show the effectiveness of the proposed results.

State estimation for cyber-physical systems with limited communication resources, sensor saturation and denial-of-service attacks.

This paper addresses the issue of the state estimation for cyber-physical systems (CPSs) with limited communication resources, sensor saturation and denial-of-service (DoS) attacks. In order to conveniently handle nonlinear term in CPSs, a Takagi-Sugeno (T-S) fuzzy model is borrowed to approximate it. The event-triggered scheme and quantization mechanism are introduced to relieve the effects brought by limited communication resources. By taking the influence of sensor saturation and DoS attacks into account, a novel mathematical model of state estimation for CPSs is constructed with limited communication resources. By using the Lyapunov stability theory, the sufficient conditions, which can ensure the system exponentially stable, are derived. Moreover, the explicit expressions of the event-based estimator gains are obtained in the form of linear matrix inequalities (LMIs). At last, a simulated example is provided for illustrating the effectiveness of the proposed method.

Nonfragile H∞ output feedback control of linear systems with an event-triggered scheme against unreliable communication links.

This paper studies the event-triggered H∞ static output feedback control of linear systems with unreliable communication. The unreliable phenomenon between the event-triggering unit and the controller is described by a stochastic variable with Bernoulli random binary distribution. To cast the considered problem in the robust control framework, the event-triggering scheme is presented by a time-delay form. A vertex structure separation strategy is utilized to handle control gain with interval variations, which could alleviate computation burden heavily. Resorting to a division of control gain from Lyapunov variable, a new method for the non-fragile H∞ controller synthesis is established in the framework of linear matrix inequalities. Simulations are executed to show the validity of the proposed approach.

A new approach to event-triggered static output feedback control of networked control systems.

This paper is concerned with the event-triggered static output feedback control of networked control systems. The event-triggered mechanism is represented by a time-delay model and some latest techniques are employed to deal with the induced time-delay. Furthermore, a novel strategy is developed to eliminate the coupling among control gain, input matrix and output matrix. With these techniques, a new sufficient condition for system stability is established in the framework of linear matrix inequalities. The effectiveness of the proposed method is shown by two numerical examples.

H∞ filtering of Markov jump linear systems with general transition probabilities and output quantization.

This paper addresses the H∞ filtering of continuous Markov jump linear systems with general transition probabilities and output quantization. S-procedure is employed to handle the adverse influence of the quantization and a new approach is developed to conquer the nonlinearity induced by uncertain and unknown transition probabilities. Then, sufficient conditions are presented to ensure the filtering error system to be stochastically stable with the prescribed performance requirement. Without specified structure imposed on introduced slack variables, a flexible filter design method is established in terms of linear matrix inequalities. The effectiveness of the proposed method is validated by a numerical example.

A Separated Approach to Control of Markov Jump Nonlinear Systems With General Transition Probabilities.

This paper is devoted to the control of Markov jump nonlinear systems with general transition probabilities (TPs) allowed to be known, uncertain, and unknown. With the help of the S-procedure to dispose the system nonlinearities and the TP property to eliminate the coupling between unknown TP and Lyapunov variable, an extended bounded real lemma for the considered system to be stochastically stable with the prescribed H∞ performance is established in the framework of linear matrix inequalities. To handle the nonlinearity incurred by uncertain TP for controller synthesis, a separated method is proposed to decouple the interconnection between Lyapunov variables and controller gains. A numerical example is given to show the effectiveness of the proposed method.