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

Neural-Network-Based Event-Triggered Adaptive Control of Nonaffine Nonlinear Multiagent Systems With Dynamic Uncertainties

This article investigates the adaptive fault-tolerant tracking control problem for a class of discrete-time multiagent systems via a reinforcement learning algorithm. The action neural networks (NNs) are used to approximate unknown and desired control input signals, and the critic NNs are employed to estimate the cost function in the design procedure. Furthermore, the direct adaptive optimal controllers are designed by combining the backstepping technique with the reinforcement learning algorithm. Comparing the existing reinforcement learning algorithm, the computational burden can be effectively reduced by using the method of less learning parameters. The adaptive auxiliary signals are established to compensate for the influence of the dead zones and actuator faults on the control performance. Based on the Lyapunov stability theory, it is proved that all signals of the closed-loop system are semiglobally uniformly ultimately bounded. Finally, some simulation results are presented to illustrate the effectiveness of the proposed approach.

Adaptive Fault-Tolerant Tracking Control for Discrete-Time Multiagent Systems via Reinforcement Learning Algorithm

(2008). Stochastic Switching Systems: Analysis and Design. Journal of the American Statistical Association: Vol. 103, No. 481, pp. 430-430.

Stochastic Switching Systems: Analysis and Design

In this article, the sliding mode control (SMC) design is studied for a class of stochastic switching systems subject to semi-Markov process via an adaptive event-triggered mechanism. Network-induced communication constraints, semi-Markov switching parameters, and uncertain parameters are considered in a unified framework for the SMC design. Due to the constraint of measuring transducers, the system states always appear with unmeasurable characteristic. Compared with the traditional event-triggered mechanism, the adaptive event-triggered mechanism can effectively reduce the number of triggering than the static event-triggered mechanism. During the data transmission of network communication systems, network-induced delays are characterized from the event trigger to the zero-order holder. The aim of this work is to design an appropriate SMC law based on an adaptive event-triggered communication scheme such that the resulting closed-loop system could realize stochastic stability and reduce communication burden. By introducing the stochastic semi-Markov Lyapunov functional, sojourn-time-dependent sufficient conditions are established for stochastic stability. Then, a suitable SMC law is designed such that the system state can be driven onto the specified sliding surface in a finite-time region. Finally, the simulation study on boost converter circuit model (BCCM) illustrates the effectiveness of the theoretical findings.

Adaptive Event-Triggered SMC for Stochastic Switching Systems With Semi-Markov Process and Application to Boost Converter Circuit Model

In this article, the event-triggered security consensus problem is studied for time-varying multiagent systems (MASs) against false data-injection attacks (FDIAs) and parameter uncertainties over a given finite horizon. In the process of information transmission, the malicious attacker tries to inject false signals to destroy consensus by compromising the integrity of measurements and control signals. The randomly occurring stealthy FDIAs on sensors and actuators are modeled by the Bernoulli processes. In order to reduce the unnecessary utilization of communication resources, an event-triggered control mechanism with state-dependent threshold is adopted to update the control input signal. The main objective of this article is to design a controller such that, under randomly occurring FDIAs and admissible parameter uncertainties, the MASs achieve consensus. By utilizing stochastic analysis method, two sufficient criteria are derived to ensure that the prescribed $H_{\infty }$ consensus performance can be achieved. Then, the desired controller gains are derived by solving recursive linear matrix inequalities. Simulation results are presented to illustrate the effectiveness and applicability of the proposed control method.

Event-Triggered Consensus Control for Multi-Agent Systems Against False Data-Injection Attacks

This paper examines the adaptive event-triggered fault detection problem of semi-Markovian jump systems (S-MJSs) with output quantization. First, we develop an adaptive event-triggered scheme for S-MJSs that is more effective than conventional event-triggered strategy for decreasing network transmission information. Meanwhile, we design a new adaptive law that can dynamically adjust the event-triggered threshold. Second, we consider output signal quantization and transmission delay in the proposed fault detection scheme. Moreover, we establish novel sufficient conditions for the stochastic stability in the proposed fault detection scheme with an $H_{\infty }$ performance with the help of linear matrix inequalities (LMIs). Finally, we provide simulation results to demonstrate the usefulness of the developed theoretical results.

Adaptive Event-Triggered Fault Detection Scheme for Semi-Markovian Jump Systems With Output Quantization

This article is concerned with the problem of the containment control with respect to continuous-time semi-Markovian multiagent systems with semi-Markovian switching topologies. Two kinds of classical control schemes, which are dynamic containment control and static containment control schemes, are utilized to address the proposed synthesis problem. Based on the linear matrix inequality (LMI) method, the dynamic containment controller and static containment controller are designed to plunge into the studied semi-Markovian multiagent systems, respectively. Moreover, the random switching topologies with the semi-Markovian process, the partly unknown transition rates, and the generally uncertain transition rates are taken into account, which can be applicable to more practical situations. Finally, the simulation results are provided to illustrate the effectiveness of the proposed theoretical results.

Containment Control of Semi-Markovian Multiagent Systems With Switching Topologies

This article studies the fault detection problem for continuous-time fuzzy semi-Markov jump systems (FSMJSs) by employing an interval type-2 (IT2) fuzzy approach. First, the continuous-time FSMJSs model is designed and the parameter uncertainty is addressed by the IT2 fuzzy approach, where the characteristic of sensor saturation is taken into account in the control system. Second, the IT2 fuzzy semi-Markov mode-dependent filter is constructed, which is employed to deal with the fault detection problem. Then, by using the Lyapunov theory, it can be guaranteed that the constructed fault detection model based on this filter and IT2 FSMJSs is stochastically stable with $H_{\infty }$ performance. Moreover, the quantization strategy is applied to the fault detection plant to dispose of the problem of limited network bandwidth. Compared with the existing literature, the differences mainly lie in two aspects, one is that the IT2 fuzzy method is utilized for FSMJSs to tackle the parameter uncertainty of system, and the other is to detect the fault signal of IT2 FSMJSs by using the fault detection system that is constructed based on the IT2 fuzzy semi-Markov mode-dependent filter and IT2 FSMJSs. Finally, two simulation examples are provided to illustrate the effectiveness and the usefulness of the proposed theoretical method.

Linchuang Zhang

Papers

Adaptive Event-Triggered Fault Detection Scheme for Semi-Markovian Jump Systems With Output Quantization

Containment Control of Semi-Markovian Multiagent Systems With Switching Topologies

Fault Detection for Fuzzy Semi-Markov Jump Systems Based on Interval Type-2 Fuzzy Approach

Fault estimation for a class of nonlinear semi‐Markovian jump systems with partly unknown transition rates and output quantization

Event-triggered fault detection for nonlinear semi-Markov jump systems based on double asynchronous filtering approach