Titlebook: Linear and Nonlinear Iterative Learning Control; Jian-Xin Xu,Ying Tan Book 2003 Springer-Verlag Berlin Heidelberg 2003 Analysis.Iterative

linear

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Composite Energy Function Based Learning Control,en the local Lipschitz continuous nonlinear factors are involved, either in the system dynamics or in the control mechanism. In the presence of such nonlinearities, finite escape time phenomenon may occur and the contraction mapping method is no longer applicable. That is the reason why most iterati

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MILK

Learning Wavelet Control Using Constructive Wavelet Networks,x based approximations. In preceding two chapters we focused on systems with parametric uncertainties. In practice many systems are also characterized by non-parametric (lumped) nonlinear uncertainties. In such circumstance, black-box methods may be the best choice. The area of black-box methods is

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refraction

Robust Optimal Design for the First Order Linear-Type ILC Scheme,LC has been evidenced both theoretically and practically to be one of the most effective methodologies for repeatable control environment, which deals with repeated tracking control tasks for deterministic systems [10,121,61,69,131,23,85,32].

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Learning Wavelet Control Using Constructive Wavelet Networks,quite diverse, and covers topics from mathematical approximation theory, estimation theory and non-parametric regression, to algorithms and currently widely discussed concepts like neural network, fuzzy and wavelet models [117].