Adaptive fuzzy control of uncertain stochastic nonlinear systems with full state constraints

This article studies the problem of fuzzy adaptive control design for a class of stochastic nonstrict feedback nonlinear systems with unknown virtual control coefficients and full state constraints. Firstly, fuzzy logic systems (FLSs) are utilized to approximate the unknown nonlinear dynamics of the system under consideration. Secondly, an asymptotic tracking control scheme is presented by introducing a bounded estimation method and some smooth functions. Meanwhile, by constructing barrier Lyapunov functions (BLFs), it is proved that all variables of the controlled system are bounded; the tracking error asymptotically converges to zero, and all states of the stochastic system do not exceed its boundaries. Finally, an illustrative simulation instance is applied to verify the superior performance of the presented adaptive algorithm. (c) 2021 Elsevier Inc. All rights reserved.

Automated summary

This article investigates the fuzzy adaptive control design for a class of stochastic nonstrict feedback nonlinear systems, introducing a bounded estimation method, smooth functions, and barrier Lyapunov functions to ensure the controlled system's performance and stability. The proposed asymptotic tracking control scheme shows superior performance in an illustrative simulation instance.