Tracking control of a marine surface vessel with full-state constraints

ABSTRACT

In this paper, a trajectory tracking control law is proposed for a class of marine surface vessels in the presence of full-state constraints and dynamics uncertainties. A barrier Lyapunov function (BLF) based control is employed to prevent states from violating the constraints. Neural networks are used to approximate the system uncertainties in the control design, and the control law is designed by using the Moore-Penrose inverse. The proposed control is able to compensate for the effects of full-state constraints. Meanwhile, the signals in the closed-loop system are guaranteed to be semiglobally uniformly bounded, with the asymptotic tracking being achieved. Finally, the performance of the proposed control has been tested and verified by simulation studies.

KEYWORDS:

• Learning control
• state constraints
• marine surface vessel
• barrier lyapunov function
• adaptive control
• neural networks

Acknowledgments

The authors would like to thank the Editor-In-Chief, the Associate Editor and the anonymous reviewers for their constructive comments which helped improve the quality and presentation of this paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant number 61522302]; the National Basic Research Program of China (973 Program) [grant number 2014CB744206]; the Fundamental Research Funds for the China Central Universities of USTB [grant number FRF-TP-15-005C1]; the Engineering and Physical Sciences Research Council (EPSRC) [grant number EP/L026856/2].

Notes on contributors

Zhao Yin

Zhao Yin received his B.Eng. degree in electric engineering and automation from Hangzhou Dianzi University, Zhejiang, China in 2013. He is currently working toward the M.E. degree in the School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China. His current research interests include neural network control, adaptive control and robotics.

Wei He

Wei He received his B.Eng. degree from College of Automation Science and Engineering, South China University of Technology (SCUT), China, in 2006, and the Ph.D. degree from Department of Electrical & Computer Engineering, the National University of Singapore (NUS), Singapore, in 2011. He worked as a research fellow in the Department of Electrical and Computer Engineering, NUS, Singapore, from 2011 to 2012. He is currently working as a full professor in School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China. He has co-authored 1 book published in Springer and published over 100 international journal and conference papers. He is a senior member of IEEE. He serves as an editor of Journal of Intelligent & Robotic Systems and IEEE/CAA Journal of Automatica Sinic. His current research interests include robotics, distributed parameter systems and intelligent control systems.

Chenguang Yang

Chenguang Yang received his B.Eng. degree in measurement and control from Northwestern Polytechnical University, Xi'an, China, in 2005, and the Ph.D. degree in control engineering from the National University of Singapore, Singapore, in 2010. He received postdoctoral training at Imperial College London, UK. He is with Zienkiewicz Centre for Computational Engineering, Swansea University, UK as a senior lecturer. His research interests lie in robotics, automation and computational intelligence.