Finite-Time Neural Network Event-Triggered Dynamic Surface Control for Nonlinear Pure-Feedback Systems
This chapter investigates the finite-time neural network event-triggered control issue for a class of nonlinear pure-feedback systems. The dynamic surface control technique is adopted to address the issue of “explosion of complexity” in the backstepping recursive design. Based on an event-triggered mechanism and the approximation property of neural networks, virtual and actual control signals are designed. Under the theoretical framework of finite-time stability, a novel neural network event-triggered dynamic surface control strategy is proposed. The presented control strategy can guarantee that the closed-loop system is semi-globally practically finite-time stable, and the tracking error converges to a small residual set in a finite time. Finally, the effectiveness of theoretical results is verified by means of simulation studies.
KeywordsNonlinear pure-feedback systems Neural network Event-triggered Finite-time stability
This work was supported in part by the National Natural Science Foundation of China (61627901, 61873311), and the 111 Project (B16014).