Fuzzy Adaptive Tracking Control for State Constraint Switched Stochastic Nonlinear Systems With Unstable Inverse Dynamics

In this article, a novel fuzzy adaptive tracking control scheme is concerned for a class of stochastic state-constrained switched nonlinear systems. The considered stochastic switched nonlinear system contains unknown nonlinearities and unstable inverse dynamics. In the design process, first, fuzzy logic systems (FLSs) are used to approximate the unknown nonlinear dynamics. Second, the stochastic barrier Lyapunov functions (BLFs) are constructed to deal with the state constraint problem. Then, an adaptive fuzzy state-feedback controller is designed by utilizing the $It\hat o$ lemma and average dwell time (ADT) approach, which can guarantee both the control system and unstable inverse dynamics to be bounded in probability and all the states cannot violate their constrained sets. Two simulation examples are provided to show the effectiveness of the proposed control approach.