Adaptive neural output feedback tracking control for a class of nonlinear systems

Abstract

In this paper, an adaptive neural output feedback control scheme based on backstepping technique and dynamic surface control (DSC) approach is developed to solve the tracking control problem for a class of nonlinear systems with unmeasurable states. Firstly, a nonlinear state observer is designed to estimate the unmeasurable states. Secondly, in the controller design process, radial basis function neural networks (RBFNNs) are utilised to approximate the unknown nonlinear functions, and then a novel adaptive neural output feedback tracking control scheme is developed via backstepping technique and DSC approach. It is shown that the proposed controller ensures that all signals of the closed-loop system remain bounded and the tracking error converges to a small neighbourhood around the origin. Finally, two numerical examples and one realistic example are given to illustrate the effectiveness of the proposed design approach.

KEYWORDS:

• Neural network
• nonlinear system
• unmeasurable states
• adaptive tracking control
• dynamic surface control

Acknowledgments

The author would like to thank the editors and anonymous reviewers for their valuable suggestions about this paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Yu-Qun Han http://orcid.org/0000-0002-9055-2954

Shan-Liang Zhu http://orcid.org/0000-0002-6194-3614

Shu-Guo Yang http://orcid.org/0000-0003-3363-0044

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant number 11671235].

Notes on contributors

Yu-Qun Han

Yu-Qun Han received the B.S. degree in mathematics and applied mathematics and the M.S. degree in applied mathematics from Qingdao University of Science and Technology, Qingdao, China, in 2010 and 2013, respectively, and the Ph.D degree in control theory and control engineering form Southeast University, Nanjing, China, in 2018. He has been with the School of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, China, since December 2018. His current research interests include nonlinear system control, stochastic nonlinear system control, adaptive control and neural networks.

Shan-Liang Zhu

Shan-Liang Zhu received the B.S. degree in school of mathematics from the Qilu Normal University, Jinan, China in 2001 and the M.S. degree in school of mathematical sciences from the Ocean University of China in 2004. He is currently an Associate Professor with Qingdao Science and Technology University, where he is currently pursuing the Ph.D. degree. His research interests include differential dynamic system, Data driven control, machine learning, and their applications.

De-Yu Duan

De-Yu Duan received the B.S. degree in computational mathematics and its application software from the department of mathematics of the Northwest University, Xi'an, China in 2000 and the M.S. degree in control engineering from the Qingdao University of Science and Technology, Qingdao, China, in 2011. He has been with the School of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, China, since 2000. His current research interests include functional analysis, system stability, signal processing, computer vision modeling and algorithm analysis.

Shu-Guo Yang

Shu-Guo Yang received the Ph.D. degree in guidance navigation and control from the Harbin Engineering University, China, in 2003. From 2006 to 2008, he was an associate professor with Qingdao Science and Technology University, China. From 2009 to 2010, he was a Guest Professor with the Georgia Institute of Technology, USA. From 2011 to 2018, he was a Professor with the School of Mathematics & Physics, Qingdao Science and Technology University. His major research interests include model-based fault diagnosis, image digital watermarking, and their applications.