Adaptive event-triggered control with dynamic dwell time of uncertain nonlinear systems

Abstract

Event-triggered control, as networked systems are popular, is increasingly appreciated. Such control could arouse essential saving of communication/computation and would lay the groundwork for networked (control) systems. However, due to the discontinuity and hybrid nature of event-triggered control, the exclusion of Zeno phenomenon is the premise of its workability. Aiming to circumvent the Zeno behaviour more explicitly, we attach a dynamic dwell time to triggering mechanism and propose a new adaptive event-triggered control scheme for uncertain nonlinear systems. Specifically, a set of dynamic gains are first introduced to compensate for large uncertainties and nonlinearities, and the indispensability of a set of gains rather than a gain is elaborated on. Then, from a dynamic-gain-dependent monitoring rule, the positive dwell time is generated so that the Zeno phenomenon can be excluded automatically. In particular, the rule enables system behaviour to be always monitored by evaluating the sampling errors of gains. Based on this, we construct an adaptive controller with linear structure, which allows the execution error can be estimated explicitly, and in turn suppressed by the gains. It is shown that, with the proposed event-triggered control, all the closed-loop system signals are bounded and the system state ultimately converges to zero. Notably, the designed triggering mechanism could save more resources in theory since the event detection and gains monitoring are performed alternately. The effectiveness and superiority of the proposed scheme is illustrated by two simulation examples.

Keywords:

• Uncertain nonlinear systems
• event-triggered control
• dynamic dwell time
• adaptive stabilisation
• Zeno behaviour

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data sharing is not applicable to this article as no new data were created or analysed in this study.

Additional information

Funding

This work was supported by the National Natural Science Foundations of China [62033007].

Notes on contributors

Lei Chu

Lei Chu received B.S. and M.S. degrees in mathematics from Qingdao University of Science and Technology, Qingdao, China, in 2014 and 2018, respectively. She is currently pursuing the Ph.D. degree in control theory and control engineering from Shandong University, Jinan, China. She is pursuing a Ph.D. in control theory and control engineering at Shandong University, Jinan, China.

Yungang Liu

Yungang Liu received the Ph.D. degree in control theory and control engineering from Shang hai Jiao Tong University, Shanghai, China, in 2000. He is currently a Changjiang Scholar Chair Professor with the School of Control Science and Engineering, Shandong University, Jinan, China. He is the Director of the Institute of Artificial Intelligence and Systems and Control, SDU; the Director of the Technical Committee on AI and Machine Vision, Shandong Institute of Electronics. His current research interests include stochastic control, nonlinear control, cooperative control, and adaptive control. Dr. Liu was a recipient of the National Outstanding Youth Science Foundation of China, the Taishan Scholar Climbing Professor of Shandong Province of China. He was a recipient of the Guan Zhaozhi Award in 2004, the National Natural Science Award of China in 2015, and the Excellent Doctoral Dissertation Award of Chinese Association of Automation in 2018.