Multiple model-based event-triggered adaptive control of a class of discrete-time nonlinear systems

ABSTRACT

In this study, the problem of event-triggered-based adaptive control (ETAC) for a class of discrete-time nonlinear systems with unknown parameters and nonlinear uncertainties is considered. Both neural network (NN) based and linear identifiers are used to approximate the unknown system dynamics. The feedback output signals are transmitted, and the parameters and the NN weights of the identifiers are tuned in an aperiodic manner at the event sample instants. A switching mechanism is provided to evaluate the approximate performance of each identifier and decide which estimated output is utilised for the event-triggered controller design, during any two events. The linear identifier with an auxiliary output and an improved adaptive law is introduced so that the nonlinear uncertainties are no longer assumed to be Lipschitz. The number of transmission times are significantly reduced by incorporating multiple model schemes into ETAC. The boundedness of both the parameters of identifiers and the system outputs is demonstrated though the Lyapunov approach. Simulation results demonstrate the effectiveness of the proposed method.

KEYWORDS:

• Event-triggered control
• multiple models
• nonlinear systems
• discrete-time systems

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work is supported by the National Nature Science Foundation of China [grant numbers 61633019, 61272020 and 61673268]; Science Fund for Creative Research Groups of the National Natural Science Foundation of China [grant number 61621002]; Zhejiang Provincial Natural Science Foundation of China [grant number LQ19F030005]; Natural Science Foundation of Ningbo City [grant number 2018A610165]; Research Programs of Educational Commission Foundation of Zhejiang Province of China [grant number PY201636903]; Shanghai Sailing Program [grant numbers 17YF1413100 and 17YF1428300].

Notes on contributors

Miao Huang

Miao Huang received the Ph.D. degree from East China University of Science and Technology, Shanghai, China, in 2015. She’s currently doing her postdoctoral research in Zhejiang University (ZJU), Hangzhou, China. Her current research interests include adaptive control, event-triggered control and hybrid systems.

Xin Wang

Xin Wang received the M.Sc. degree and the Ph.D. degree from Northeastern University, Shenyang, China, in 1998 and 2002, respectively. Since 2004, he has been with Shanghai Jiao Tong University. His current interests include multivariable intelligent decoupling control, and modeling, control and optimization of complex industrial processes.

Zhe-Ming Lu

Zhe-Ming Lu received the M.Sc. degree and the Ph.D. degree from Harbin Institute of Technology (HIT), Harbin, China, in 1997 and 2001, respectively. From 1999 to 2007, he was with the HIT, as a Full Professor. He held visiting research positions at University of Freiburg, Germany (2004). From 2009, he has been with the School of Aeronautics and Astronautics, ZJU, as a Full Professor. His current interests include multimedia signal processing, information hiding and complex networks.

Long-Hua Ma

Long-Hua Ma received the M.Sc. degree and the Ph.D. degree from ZJU, Hangzhou, China, in 1993 and 2002, respectively. Since 2013, he has been with the Ningbo Institute of Technology, ZJU, as a Full Professor. His current interests include new energy and electric vehicle energy management and control, control theory and application.

Ming Xu

Ming Xu received the Ph.D. degree from ZJU, Hangzhou, China, in 2011. Since 2017, he has been with the Department of Automation, Ningbo Institute of Technology, ZJU, Ningbo, China. His current interests include system optimization theory and control technology.

Hong-Ye Su

Hong-Ye Su received the Ph.D. degree from ZJU, Hangzhou, China, in 1995. Since 2000, he has been with the State Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, ZJU, Hangzhou, as a Full Professor. His currently interests include the theory and technology of modeling, control and optimization of complex production process.