Fuzzy adaptive event-triggered predefined time control for stochastic nonlinear systems

Abstract

This article investigates the predefined time fuzzy adaptive event-triggered control (ETC) issue for stochastic nonlinear systems. Fuzzy logic systems (FLSs) are used to recognize unknown nonlinear dynamics, and fuzzy state estimators are designed to solve the problems caused by unmeasurable states. To save network resources and directly activate control behaviors by applying triggered state signals, a dynamically adjustable event-triggered mechanism (ETM) is established. The proposed method can directly preset the control problem of the stabilization time of the system, and the control strategy is easier to implement due to fewer design parameters in this method. An adaptive fuzzy event-triggered predefined time control scheme is proposed by backstepping control technique. The tanh function is introduced in the control design process to prevent the singularity problem. The semiglobally practically predefined time stochastic stabilizable (SPPSS) of the control system is demonstrated through the Lyapunov theory. Finally, simulations are shown to verify the effectiveness of the investigated theory.