Fuzzy adaptive predefined-time control for stochastic nonlinear systems with unmodelled dynamics

This paper addresses the problem of fuzzy adaptive predefined-time control for stochastic nonlinear systems subject to unmodelled dynamics. Firstly, for avoiding the negative effect of uncertain dynamics, fuzzy logic systems (FLSs) are incorporated to achieve approximation in the design process of adaptive controller. Then, a dynamic signal function is employed to manage dynamic characteristics not captured by the existing model during signal processing effectively. Next, the singularity issue that may be caused by the derivatives of virtual controllers is helped to be circumvented by the use of a hyperbolic tangent function. Building upon the foundational lemma that guarantees predefined-time system stability, a fuzzy adaptive control strategy is developed for stochastic nonlinear systems. The proposed approach maintains its intended performance in the presence of unmodelled disturbances and uncertainties. It ensures that the system output tracks the desired reference signal within a predefined time and guarantees that all the variables are bounded in probability. Finally, the simulation results serve as compelling evidence for the feasibility of the proposed control algorithm, while also showcasing its superior performance.