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An Iterative Learning Control of Nonlinear Systems Using Neural Network Design
Author(s) -
Chien ChiangJu,
Fu LiChen
Publication year - 2002
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1111/j.1934-6093.2002.tb00329.x
Subject(s) - iterative learning control , control theory (sociology) , artificial neural network , sigmoid function , computer science , nonlinear system , bounded function , feedforward neural network , tracking error , internal model , controller (irrigation) , projection (relational algebra) , feed forward , lyapunov function , mathematics , algorithm , artificial intelligence , control engineering , control (management) , engineering , mathematical analysis , physics , quantum mechanics , agronomy , biology
ABSTRACT In this paper, a feedforward neural network with sigmoid hidden units is used to design a neural network based iterative learning controller for nonlinear systems with state dependent input gains. No prior offline training phase is necessary, and only a single neural network is employed. All the weights of the neurons are tuned during the iteration process in order to achieve the desired learning performance. The adaptive laws for the weights of neurons and the analysis of learning performance are determined via Lyapunov‐like analysis. A projection learning algorithm is used to prevent drifting of weights. It is shown that the tracking error vector will asymptotically converges to zero as the iteration goes to infinity, and the all adjustable parameters as well as internal signals remain bounded.