Event-based adaptive neural network asymptotic control design for nonstrict feedback nonlinear system with state constraints

This article presents an event-triggered adaptive neural network (ANN) asymptotic tracking control scheme for nonstrict feedback nonlinear systems with state constraints. The neural networks are explored to address the unknown dynamics and nonstrict feedback structure. With the help of barrier Lyapunov functions, the state constraints are properly addressed. By employing some well defined smooth functions and backstepping technique, the asymptotic tracking controller is recursively constructed. In addition, event-triggered mechanism is incorporated into the asymptotic tracking design framework to reduce the data transmission. Through Lyapunov stability analysis, the tracking errors can converge to zero asymptotically and the boundedness of the considered systems are guaranteed. Simulation results are given to elucidate the validity of the proposed ANN asymptotic controller.