Event-based adaptive fuzzy control for stochastic nonlinear systems with prescribed performance

This paper presents an event-based prescribed adaptive fuzzy control scheme for stochastic nonlinear systems. Unlike most existing prescribed performance control schemes, the proposed scheme introduces two tangential envelope functions, one barrier function, and a time-varying scaling transformation. This scheme eliminates the initial boundary constraints on the tracking error and ensures convergence within autonomous asymmetric boundaries in a prescribed time frame. Furthermore, a new event-triggered mechanism is introduced to alleviate the conservative inequality limitations on the trigger parameter design. Additionally, the control scheme integrates a single-parameter-based fuzzy approximator and a command-filtered backstepping design, effectively compensating for approximations and filtering errors while ensuring boundedness in probability for all closed-loop signals. Simulation results confirmed the effectiveness and superiority of the proposed scheme.