Command Filter-Based Adaptive Fuzzy Fixed-Time Tracking Control for Stochastic Nonlinear Systems with Input Saturation and Dead Zone

This thesis consider the tracking issue for a category of nonstrict-feedback stochastic nonlinear systems (NFSNSs) with input saturation (IS) and dead zone (DZ) in the presence of unmodeled dynamics and dynamic disturbances by designing an adaptive fuzzy fixed-time controller based on the fixed-time command filter (FTCF). The fuzzy logic systems (FLSs) and the dynamic signal are employed to solve the unknown nonlinear functions and unmodeled dynamics separately. A non-affine smooth function is employed to approximate the non-smooth IS and DZ nonlinearities and is transformed into an affine form on account of the mean-value theorem. At the same time, a FTCF is utilized to dispose of the "computational explosion" problem, and the compensation of the filtering error is considered. Compared to existing results, the bound and convergence time of the output of the FTCF are further provided. It is proved that all the closed-loop variables are fixed-time bounded in probability (FTBIP), and the designed control strategy has robustness to the unmodeled dynamics. Meanwhile, the tracking error converges to a small neighborhood of the origin. Two simulation examples exhibit the effectiveness and superiority of the proposed control scheme.