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Title: Composite learning adaptive backstepping control using neural networks with compact supports
Authors: Pan, Yongping
Yang, Chenguang
Pratama, Mahardhika
Yu, Haoyong
Keywords: Engineering::Computer science and engineering
Issue Date: 2019
Source: Pan, Y., Yang, C., Pratama, M. & Yu, H. (2019). Composite learning adaptive backstepping control using neural networks with compact supports. International Journal of Adaptive Control and Signal Processing, 33(12), 1726-1738.
Project: 15/12124019
Journal: International Journal of Adaptive Control and Signal Processing
Abstract: 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.
ISSN: 0890-6327
DOI: 10.1002/acs.3002
Rights: © 2019 John Wiley & Sons, Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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