Fuzzy Adaptive Optimal Consensus Fault-Tolerant Control for Stochastic Nonlinear Multiagent Systems

This article investigates the problem of adaptive fuzzy optimal distributed consensus control for stochastic multiagent systems (MASs) with full-state constraints and nonaffine nonlinear faults. Fuzzy logic systems are employed to identify the unknown nonlinearities. To solve the problem of optimal state constraint control, a barrier Lyapunov function based optimal cost function is designed. By introducing Butterworth low-pass filter into control design, the deleterious effects raised by nonlinear fault can be compensated. By utilizing adaptive dynamic programming algorithm in critic–actor construction, a fuzzy adaptive distributed optimal consensus fault-tolerant control method is proposed, which can ensure that all signals of the controlled system are semiglobally uniformly ultimately bounded in probability, and outputs of the follower agents keep consensus with the output of leader. In addition, system states are all not exceeded their constrained bound. Finally, simulation results are provided to illustrate the feasibility of the developed control method and theorem.

I. Introduction

Consensus control is one of the basic problems in the coordinated control of multiagent systems (MASs), and has wide application prospect in the fields of aerospace, robot, and autonomous ocean vehicle, which means that the state or output of the follower agents of MASs tend to consensus with the state or output of the leaders through local communication. In recent years, the consensus control of MASs has been become into the hot research direction in the fields of control engineering and some critical results have been received, see [1]–[4]. The authors in [1] researched the observer-based distributed consensus control for multiagent networks. The works in [2] and [3] studied the consensus control problems for second-order MASs and the work in [4] proposed the consensus control for linear MASs with directed topology.