Finite-Time Adaptive Event-Triggered Control for Stochastic Nonlinear Systems with Output Constraint and Input Saturation

This paper addresses the issue of finite-time event-triggered command filtered adaptive neural network tracking control for a class of stochastic nonlinear systems (SNS) with output constraint and input saturation. The adaptive neural networks are utilized to approximate the unknown nonlinear terms, while the barrier Lyapunov function is employed to confine the system output signal within a predefined range. By integrating the event trigger (ET) mechanism with finite-time (FT) command filtered technique, the system's finite-time stability can be ensured, while also reducing communication costs. Simulation results are provided to demonstrate the effectiveness of the proposed control method.