Adaptive Fuzzy Tracking Control for Stochastic Nonlinear Systems with Full-State Constraints

This study focuses on addressing the challenge of adaptive fuzzy tracking control for stochastic nonlinear systems while considering full-state constraints. To tackle this issue, we introduce a fuzzy logic system to approximate the unknown nonlinear terms in the system. Additionally, a barrier Lyapunov function is utilized to confine the system state within a specific range. In order to design a novel tracking controller, the backstepping design method and the fuzzy control approach are combined. To optimize system performance, a hysteresis quantizer is integrated. To evaluate the effectiveness of the proposed control strategy, two simulation examples are presented. The simulation results demonstrate that the proposed control strategy effectively limits all state variables to a predefined range. Additionally, the tracking error of the system steadily converges to a negligible range near zero.