Event-based disturbance compensation control for discrete-time SPMSM with mismatched disturbances

Abstract

This paper investigates the problem of event-based disturbance compensation control for discrete-time surface-mounted permanent magnet synchronous motor (SPMSM) subject to both mismatched external disturbances and the limited communication bandwidth. The mismatched external disturbances make it impossible to transform the original model into the multiple-step-ahead predictor model by using the existing predictor technology. To tackle such a challenge, a novel backstepping-based disturbance compensation control framework is firstly proposed for the discrete-time SPMSM with known system dynamics. In the proposed framework, the disturbance observer is designed to compensate for the external disturbances, and the novel variable substitution/decoupling technology is proposed to design the event-based controller for the original controlled system. The proposed controller is composed of the event-based feedback control signal and the feedforward disturbance compensation signal, thereby improving the system disturbance rejection ability and mitigating the communication resource. Subsequently, an event-based adaptive neural control scheme is proposed by combining a modified neural disturbance observer. The proposed scheme ensures that the tracking error converges to a small neighbourhood of the origin, all the signals in the closed-loop system are bounded and meanwhile the communication resources are greatly reduced. The effectiveness of the proposed controller is illustrated through the simulation results.

Keywords:

• Adaptive neural control
• event-triggered control
• disturbance observer
• discrete-time system
• surface-mounted permanent magnet synchronous motors

Disclosure statement

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

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This work was supported in part by National Natural Science Foundation of China [grant numbers 61773169 and 61973129], the Guangdong Natural Science Foundation [grant number 2019B151502058], the Guangzhou Science and Technology Project [grant number 201904010295], and the Fundamental Research Funds for the Central Universities.

Notes on contributors

Min Wang

Min Wang received the Ph.D. degree in system theory from Qingdao University, Qingdao, China, in 2009. From 2017 to 2018, she was a Visiting Scholar with the Department of Computer Science, Brunel University London, Uxbridge, U.K. She is currently a Professor with the School of Automation Science and Engineering, South China University of Technology, Guangzhou, China. She has authored or coauthored more than 40 papers published in international journals. Her current research interests include intelligent control, dynamic learning, robot control, and event-triggered control. Prof. Wang is currently an Associate Editor of the INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE, IEEE ACCESS, and CONTROL THEORY & APPLICATIONS.

Lixue Wang

Dr. Lixue Wang received the B.Eng. degree in automation from Weifang University, Weifang, China, in 2016, and the M.Eng. degree in control engineering from the Guangdong University of Technology, Guangzhou, China, in 2019. She is currently pursuing the Ph.D. degree in control science and engineering with the South China University of Technology, Guangzhou, China. Her current research interests include adaptive control, event-triggered control, and deterministic learning theory.

Ruipeng Huang

Ruipeng Huang received the B.Eng. degree in control engineering from Guangdong University of Technology, Guangzhou, China, in 2017, and the M.Eng. degree in control engineering from the South China University of Technology, Guangzhou, China, in 2020. His current research interests include adaptive control, robotic control, and neural learning theory.

Chenguang Yang

Chenguang Yang is a Professor of Robotics. He received the Ph.D. degree in control engineering from the National University of Singapore, Singapore, in 2010. Prof. Yang was a Post Doctoral Fellow with the Imperial College London, London, U.K., and an European Commission sponsored Marie Curie International Incoming Fellow. Prof. Yang is a recipient of the Best Paper Award in the IEEE Transactions on Robotics and over 10 Best Paper Awards from international conferences. His research interests include robotics and automation.