Adaptive fixed-time output feedback formation control for nonstrict-feedback nonlinear multi-agent systems

Abstract

In this paper, an adaptive fixed-time output feedback formation control problem is investigated for nonlinear multi-agent systems with a nonstrict-feedback structure. In the controller design procedure, the neural network state observer is designed to estimate the unmeasurable state variables. Dynamic surface control (DSC) technique is applied to avoid the repeated differentiation for the virtual control signals. The dynamic surface compensation signals can realise the practical fixed-time bounded. Utilising the classified discussion method, the difficulty of controller design caused by the existence of observer error term is addressed. The technique of transformation of the index set is employed to cope with the related variables of the neighbour states, which simplifies the controller design. Under the presented control mechanism, all closed-loop signals remain bound for a fixed period of time, the formation control performance target between all followers and leader can be achieved. And the formation errors and state observers errors are both bounded such that can converge to a little domain around zero. Simulation results are provided to test the availability of the presented strategy.

Keywords:

• Adaptive technique
• nonlinear multi-agent systems
• fixed-time
• output-feedback

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data Availability Statement

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

Additional information

Notes on contributors

Ke Xu

Ke Xu received her B.Sc. and M.Sc. degrees from Bohai University, Jinzhou, China, in 2019, and 2022, respectively. She is currently pursuing the Ph.D. degree with Beijing Jiaotong University, Beijing, China. Her current research interests include adaptive backstepping control, intelligent control, fixed time control and nonlinear systems.

Huanqing Wang

Huanqing Wang received his B.Sc. degree in mathematics from Bohai University, Jinzhou, China, in 2003, his M.Sc. degree in mathematics from Inner Mongolia University, Huhhot, China, in 2006, and his Ph.D. degree from the Institute of Complexity Science, Qingdao University, Qingdao, China, in 2013. He was a Post-Doctoral Fellow with the Department of Electrical Engineering, Lake head University, Thunder Bay, ON, Canada,in 2014, and was a Post-Doctoral Fellow with the Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada. He has authored or co-authored over 40 papers in top international journals. His current research interests include adaptive backstepping control, fuzzy control, neural networks control, stochastic nonlinear systems. Dr. Wang serves as an Associate Editor for several journals, including Neural Computing and Applications, International Journal of Control, Automation, and Systems, and IEEE Access.

Peter Xiaoping Liu

Peter Xiaoping Liu received his B.Sc. and M.Sc. degrees from Northern Jiaotong University, China in 1992 and 1995, respectively, and Ph.D. degree from the University of Alberta, Canada, in 2002. He has been with the Department of Systems and Computer Engineering, Carleton University, Canada, since July 2002 and he is currently a Professor. Dr. Liu has published more than 300 research articles. His interest includes interactive networked systems and teleoperation, haptics, surgical simulation, robotics, intelligent systems, and context-aware systems. Dr. Liu serves as an Associate Editor for several journals including IEEE/ASME Transactions on Mechatronics, IEEE Transactions on Cybernetics, IEEE Transactions on Automation Science and Engineering, and IEEE Access. He is a licensed member of the Professional Engineers of Ontario (P. Eng), a senior member of IEEE and a Fellow of Engineering Institute of Canada (FEIC).