Adaptive neural network asymptotic control design for MIMO nonlinear systems based on event-triggered mechanism

Abstract

This article presents an adaptive neural network event-triggered asymptotic tracking control strategy for multi-input and multi-output (MIMO) nonlinear systems with state constraints and unknown dynamics. The prescribed state constraints are ensured by employing barrier Lyapunov function and the neural networks are utilized to address unknown dynamics. By applying a bound estimation method and some smooth functions, associating with backstepping technique, the asymptotic tracking controller is recursively constructed. Meanwhile, the event-triggered mechanism is introduced into the design process to mitigate data transmission. Finally, the validity of the presented asymptotic controller is elucidated via a practical simulation example.