Dynamic Event-triggered Adaptive Control for Uncertain Nonlinear Switched Systems Based on MDADT Method

• Published in: International journal of control, automation, and systems Vol. 21; no. 1; pp. 84 - 93
• Main Authors: Wang, Xueliang, Xia, Jianwei, Zhang, Na, Yu, Miao, Guo, Xiaoxiao
• Format: Journal Article
• Language: English
• Published: Bucheon / Seoul Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers 01.01.2023; Springer Nature B.V; 제어·로봇·시스템학회
• Subjects: Actuators; Adaptive control; Closed loops; Control; Dwell time; Engineering; Feedback control; Mechatronics; Network control; Neural networks; Nonlinear control; Nonlinear systems; Nonlinearity; Regular Papers; Robotics; Switching; Tracking control; Tracking errors; 제어계측공학; mode-dependent average dwell time; neural network; switched nonlinear systems; Dynamic event-triggered mechanism
• ISSN: 1598-6446; 2005-4092
• DOI: 10.1007/s12555-021-0921-3

This article deals with the dynamic event-triggered tracking control problem for nonlinear switched systems with uncertain nonlinearities. By combining neural network control technology, mode-dependent average dwell time (MDADT) switching rule and dynamic event-triggered strategy, a valid adaptive control scheme is established, which guarantees the boundedness of all signals in the resulting closed-loop system and the tracking error eventually converges to a small neighborhood of the origin under a class of switching signals with MDADT property. Unlike the existing tracking control schemes, the proposed dynamic event-triggered strategy reduce some unnecessary transmissions from controller to actuator and thus saving network resources better. Finally, the effectiveness of the proposed control design is verified by a numerical simulation.