Adaptive Neural Network Finite-Time Event Triggered Intelligent Control for Stochastic Nonlinear Systems With Time-Varying Constraints

Abstract

Finite-time command-filter event-trigger control based on adaptive neural network is presented in this article for a class of output-feedback stochastic nonlinear system (SNS) with output time-varying constraints and unmeasured states. The adaptive neural network combined with backstepping is utilized to approximate the unknown nonlinear functions of the system. The finite-time command-filter is employed to reduce the difficulty of complex calculation caused by backstepping technique. An adaptive observer is developed to estimate unmeasured states, and a controller is designed to be triggered only when the event-triggered condition is met. The time-varying barrier Lyapunov function is utilized to ensure the output time-varying constraint. The control method proposed in this article not only guarantees the finite-time stability of the system but also meets the output constraint. The effectiveness of the method is demonstrated on the ship maneuvering system with three degrees of freedom.