Distributed Output-Feedback Adaptive Fuzzy Leader-Following Consensus of Stochastic Nonlinear Interconnected Multiagent Systems

In this article, the distributed output-feedback adaptive fuzzy leader-following consensus is investigated for a class of high-order stochastic nonlinear multiagent systems (MASs) with unknown control gains and uncertain interactions from other agents. First, a novel dynamic gain filter is constructed to compensate unmeasured states and compared with the existing results, the number of dynamic variables is greatly reduced. Then, the tuning function method is used to construct adaptive laws to compensate unknown parameters. The uncertain interaction functions are decomposed and approximated by the fuzzy logic systems. Next, using the backstepping method, the dynamic gain filter-based distributed adaptive fuzzy leader-following protocol is designed, which only needs the dynamic variables' information of neighbors, and the problem on computing the mutually dependent inputs in some existing results is completely avoided. Finally, the numerical simulation results are given to illustrate the effectiveness of the proposed method.

Automated summary

This article investigates the distributed output-feedback adaptive fuzzy leader-following consensus for a type of high-order stochastic nonlinear multiagent systems. A novel dynamic gain filter is constructed to compensate unmeasured states, adaptive laws are constructed using the tuning function method, and the uncertain interaction functions are decomposed and approximated by fuzzy logic systems. By designing a dynamic gain filter-based distributed adaptive fuzzy leader-following protocol using the backstepping method, the problem of computing mutually dependent inputs in existing results is completely avoided.