Fuzzy adaptive finite-time consensus tracking control for nonlinear multi-agent systems

Abstract

This paper focuses on the finite-time consensus tracking control problem of nonlinear multi-agent systems. Dynamics of each agent has completely unknown nonlinear terms that cannot be directly used for control design. Therefore, fuzzy logic systems are employed to approximate these nonlinear functions. Furthermore, a finite-time fuzzy adaptive consensus tracking protocol is proposed for a class of nonlinear multi-agent systems by using integral-type Lyapunov functions. The developed adaptive backstepping design scheme successfully avoids the singularity problem of the derivatives of virtual control signals. It is shown that with the presented control protocol, the consensus tracking errors converge to a small neighbourhood of the origin in finite time, and the other signals of multi-agent systems are bounded. Finally, a numerical example is used to verify the effectiveness of the proposed control protocol.

Keywords:

• Nonlinear multi-agent systems
• fuzzy adaptive control
• finite-time
• integral-type Lyapunov function
• backstepping

Disclosure statement

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

Additional information

Funding

This work was supported by National Natural Science Foundation of China [grant number 61673227, 61873137].

Notes on contributors

Lili Zhang

Lili Zhang received the BA degree in mathematics and applied mathematics from Liaocheng University, Liaocheng, China, in 2018. She is currently pursuing the MA degree in systems theory with the Institute of Complexity Science, Qingdao University, Qingdao, China. Her current research interests include nonlinear control systems, multiagent systems, and adaptive fuzzy control.

Bing Chen

Bing Chen received the BA degree in mathematics from Liaoning University, Shenyang, China, in 1982, the MA degree in mathematics from the Harbin Institute of Technology, Harbin, China, in 1991, and the PhD degree in electrical engineering from Northeastern University, Shenyang, in 1998. He is currently a Professor with the Institute of Complexity Science, Qingdao University, Qingdao, China. His current research interests include nonlinear control systems, robust control, and adaptive fuzzy control.

Chong Lin

Chong Lin (SM’06) received the BSci and MSci degrees in applied mathematics from Northeastern University, Shenyang, China, in 1989 and 1992, respectively, and the PhD degree in electrical and electronic engineering from Nanyang Technological University, Singapore, in 1999. He was a Research Associate with the Department of Mechanical Engineering, University of Hong Kong, Hong Kong, in 1999. From 2000 to 2006, he was a Research Fellow with the Department of Electrical and Computer Engineering, National University of Singapore, Singapore. Since 2006, he has been a Professor with the Institute of Complexity Science, Qingdao University, Qingdao, China. He has authored more than 60 research papers and co-authored two monographs. His current research interests include systems analysis and control, robust control, and fuzzy control.

Yun Shang

Yun Shang received the BS degree in applied mathematics from Qingdao University, Qingdao, China, in 2003, and the MS degree in mathematics from Capital Normal University, Beijing, China, in 2006. She is currently pursuing the PhD degree in systems theory with the Institute of Complexity Science, Qingdao University. She has been with the College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, since 2006. Her current research interests include nonlinear control systems, multi-agent systems, and adaptive neural control.