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A direct NN‐approximation control for uncertain nonaffine systems with unknown control direction
Author(s) -
Zhang Yao,
Chen Shengyong,
Diao Chen,
Xian Bin
Publication year - 2019
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.4796
Subject(s) - control theory (sociology) , controller (irrigation) , tracking error , artificial neural network , chaotic , computer science , stability (learning theory) , approximation error , term (time) , adaptive control , control (management) , mathematics , algorithm , artificial intelligence , physics , quantum mechanics , machine learning , agronomy , biology
Summary In this paper, we present an adaptive neural network (NN) controller for uncertain nonaffine systems with unknown control direction. Most of the previous NN‐based controllers included a damping term in the adaptive law of NN weights to ensure the closed‐loop stability. The estimated error of the NN weights as well as the tracking error were therefore increased, relying not only on the size of the NN approximation error but also on the ideal NN weights. Compared with those, the proposed controller evades using the damping term through combining a novel adaptive algorithm and a switching mechanism to update the weights. The NN thus can directly approach a target controller with satisfactory accuracy even if the control direction is unknown. Stability analysis shows that the tracking error and the estimated error of NN weights both converge to small neighbors of 0 which solely depend on the NN approximation error. At last, simulations on a Duffing‐Holmes chaotic model show the effectiveness of the proposed controller in comparison to another NN‐based method.