COMPOSITE NEURAL NETWORK LEARNING FROM FRACTIONAL BACKSTEPPING

Heng Liu, Hongling Qiu (), Xiaoyan Zhang (), Shenggang Li () and Jinde Cao
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Heng Liu: School of Mathematics, Southeast University, Nanjing 211189, P. R. China2School of Mathematics and Physics, Guangxi Minzu University, Nanning 530006, P. R. China
Hongling Qiu: School of Mathematics and Physics, Guangxi Minzu University, Nanning 530006, P. R. China
Xiaoyan Zhang: College of Mathematics and Information Science, Shaanxi Normal University, Xi’an 710119, P. R. China
Shenggang Li: College of Mathematics and Information Science, Shaanxi Normal University, Xi’an 710119, P. R. China
Jinde Cao: School of Mathematics, Southeast University, Nanjing 210096, China5Yonsei Frontier Lab, Yonsei University, Seoul 03722, South Korea

FRACTALS (fractals), 2022, vol. 30, issue 10, 1-12

Abstract: This paper focuses on parameter convergence and precise modeling for fractional-order nonlinear systems with functional uncertainties via using adaptive backstepping neural network control (ABNNC) and composite learning adaptive backstepping neural network control (CLABNNC). In the ABNNC design, a command filter is proposed, and the neural network approximation system is considered to deal with the unknown function, where an adaptation law is designed to ensure tracking errors converge to an arbitrarily small region near the origin under a strict persistent excitation condition that is too strict for the convergence of adaptive parameters. In order to relax this condition, a composite learning adaptation law is established by taking advantage of the tracking error and the prediction error to update the free parameter of the neural network system. The proposed CLABNNC method can not only ensure the convergence of tracking errors, but also achieve the accurate approximation of functional uncertainties under a weaker interval excitation condition. Finally, a numerical simulation example is put forward to demonstrate the effectiveness of our method.

Keywords: Adaptive Neural Network Control; Composite Learning; Fractional-Order Nonlinear System; Backstepping; Functional Uncertainty (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1142/S0218348X22401909

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