Neural Network-based Event-triggered Adaptive Asymptotic Tracking Control for Switched Nonlinear Systems

摘要

In this paper, an adaptive event-triggered asymptotic tracking control problem is addressed for switched nonlinear systems with unknown control directions. In existing control schemes, the proposed controller is not directly aimed at the original system, which affects the control performance. Different from the existing control schemes, based on the original system, an event-triggered control law is constructed in this paper. The proposed event-triggered controller guarantees that the tracking error sigma(1) asymptotically converges to the origin. Finally, the effectiveness of the proposed controller design scheme is proved by simulation examples.