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Fast and stable composite learning via high‐order optimization
Author(s) -
Jiang Tao,
Han Hongwei
Publication year - 2020
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.5232
Subject(s) - backstepping , control theory (sociology) , computer science , process (computing) , nonlinear system , bounded function , lyapunov function , mathematical optimization , filter (signal processing) , tracking error , property (philosophy) , tracking (education) , adaptive control , mathematics , artificial intelligence , control (management) , psychology , mathematical analysis , pedagogy , philosophy , physics , epistemology , quantum mechanics , computer vision , operating system
Summary Fast and stable adaptation is necessary to achieve stringent tracking performance specifications in the face of large system uncertainties. This work develops a novel fast adaption architecture based on a high‐order optimization idea, where an approximated filter of weight is applied to smoothen and stabilize the estimation process. Larger learning rate can be selected to achieve fast adaption in that high‐frequency uncertainties are attenuated. Moreover, composite learning combined with filtering regression and experience replay technique is utilized to further smoothen and accelerate the parameter estimation process. Given a nonlinear plant with multi‐input multi‐output strict‐feedback structure, the proposed adaptive control is integrated into the backstepping framework. The uniformly bounded property of the tracking errors and the approximation errors is proven by Lyapunov theory. The superiority of the proposed method is demonstrated by comparative simulations.