Composite learning adaptive backstepping control using neural networks with compact supports

Pan, Yongping; Yang, Chenguang; Pratama, Mahardhika; Yu, Haoyong

Abstract

© 2019 John Wiley & Sons, Ltd. The ability to learn is crucial for neural network (NN) control as it is able to enhance the overall stability and robustness of control systems. In this study, a composite learning control strategy is proposed for a class of strict-feedback nonlinear systems with mismatched uncertainties, where raised-cosine radial basis function NNs with compact supports are applied to approximate system uncertainties. Both online historical data and instantaneous data are utilized to update NN weights. Practical exponential stability of the closed-loop system is established under a weak excitation condition termed interval excitation. The proposed approach ensures fast parameter convergence, implying an exact estimation of plant uncertainties, without the trajectory of NN inputs being recurrent and the time derivation of plant states. The raised-cosine radial basis function NNs applied not only reduces computational cost but also facilitates the exact determination of a subregressor activated along any trajectory of NN inputs so that the interval excitation condition is verifiable. Numerical results have verified validity and superiority of the proposed approach.

• Journal Article Type: Article
• Acceptance Date: Apr 15, 2019
• Online Publication Date: May 17, 2019
• Publication Date: 2019-12
• Deposit Date: May 21, 2019
• Publicly Available Date: May 18, 2020
• Journal: International Journal of Adaptive Control and Signal Processing
• Print ISSN: 0890-6327
• Electronic ISSN: 1099-1115
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Volume: 33
• Issue: 12
• Pages: 1726-1738
• DOI: https://doi.org/10.1002/acs.3002
• Public URL: https://uwe-repository.worktribe.com/output/846854
• Publisher URL: http://doi.org/10.1002/acs.3002
• Additional Information: Additional Information : This is the peer reviewed version of the following article: Pan, Y., Yang, C., Pratama, M. and Yu, H. (2019) Composite learning adaptive backstepping control using neural networks with compact supports. International Journal of Adaptive Control and Signal Processing. ISSN 0890-6327, which has been published in final form at http://doi.org/10.1002/acs.3002. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
• Contract Date: May 21, 2019