Stationary average-tracking problem of unmatched constant reference signals based on delayed-state feedback

Abstract

Stationary average-tracking problems of unmatched constant reference signals are investigated for first-order multi-agent systems, and an integral-type average-tracking algorithm with delayed-state feedback is constructed. With identical communication delays and different introduced self-delays, sufficient conditions are obtained for the agents converging to the average-value of unmatched reference signals asymptotically by using the frequency-domain analysis method. To our delight, introducing delayed-state feedback improves the robustness to communication delay and unmatched reference signals. Simulation results show the effectiveness of our proposed algorithm.

Keywords:

• Unmatched reference signals
• stationary average-consensus tracking
• first-order multi-agent systems
• communication delay
• delayed-state feedback
• different self-delays

Disclosure statement

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

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [Grant Nos 61973139 and 61473138], Natural Science Foundation of Jiangsu Province [Grant No. BK20151130], Six Talent Peaks Project in Jiangsu Province [Grant no. 2015-DZXX-011], and China Scholarship Council [201606845005].

Notes on contributors

Cheng-Lin Liu

Cheng-Lin Liu received his Bachelor degree in Electrical Engineering and Automation from Nanjing University of Science and Technology, China in 2003, Ph.D. degree in Control Theory and Control Engineering from Southeast University, China in 2008. Since 2008, he has been with Jiangnan University, Wuxi, China, where he is currently a professor at Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of IOT Engineering. From March 2014 to March 2015, he was with School of Electrical and Electronic Engineering at Nanyang Technological University as a visiting scholar. His current research interests include coordination control of multi-agent systems, iterative learning control and nonlinear control.

He Li

He Li received the B.Eng. degree from the College of Automation, Qufu Normaly University, Rizhao, China, in 2016, and the M.Eng. Degree from the School of Automation, Harbin University of Science and Technology, Harbin, China, in 2019. He is currently pursuing the Ph.D. degree with Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Institute of Automation, Jiangnan University, Wuxi, China. His current research interests include multiple Euler-Lagrange systems, adaptive neural networks control, fixed-time consensus tracking.

Liang Shan

Liang Shan received the B.Sc. degree in electrical engineering and the Ph.D. degree in control science and control engineering from the Nanjing University of Science and Technology, China, in 2002 and 2007, respectively. He is currently an Associate Professor with the Nanjing University of Science and Technology. His research interests include intelligence control algorithm, nonlinear system, and control methods of motor servo system.