Adaptive tracking control of nonlinear systems with dynamic uncertainties using neural network

ABSTRACT

In this paper, an adaptive neural tracking control approach is proposed for a class of nonlinear systems with dynamic uncertainties. The radial basis function neural networks (RBFNNs) are used to estimate the unknown nonlinear uncertainties, and then a novel adaptive neural scheme is developed, via backstepping technique. In the controller design, instead of using RBFNN to approximate each unknown function, we lump all unknown functions into a suitable unknown function that is approximated by only a RBFNN in each step of the backstepping. It is shown that the designed controller can guarantee that all signals in the closed-loop system are semi-globally bounded and the tracking error finally converges to a small domain around the origin. Two examples are given to demonstrate the effectiveness of the proposed control scheme.

KEYWORDS:

• Adaptive tracking control
• nonlinear systems
• neural network
• dynamic uncertainties

Acknowledgments

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

Disclosure statement

No potential conflict of interest was reported by the author.

Additional information

Notes on contributors

Yu-Qun Han

Yuqun Han received the B.Sc. degree in mathematics and applied mathematics and the M.Sc. degree in applied mathematics from Qingdao University of Science and Technology, Qingdao, China, in 2010 and 2013, respectively, and he is currently pursuing the Ph.D. degree in control theory and control engineering form Southeast University. His current research interests include nonlinear system control, stochastic nonlinear system control, adaptive control and neural networks.