Titlebook: Iterative Learning Control; An Optimization Para David H. Owens Book 2016 Springer-Verlag London 2016 Control Applications.Control Engineer

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https://doi.org/10.1007/978-1-4471-6772-3Control Applications; Control Engineering; Control Theory; Iterative Learning Control; Parameter Optimiz

临时抱佛脚

Iterative Learning Control: A Formulation,ant dynamics and norm-based requirements of convergence and monotonicity. The necessary properties of the operators are described in terms of spectral radius and positivity conditions. The effect of using relaxed algorithms is included as is an introduction to the ideas of robustness and multiplicative modelling errors.

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Monotonicity and Gradient Algorithms,tified with adjoint operators which take a simple form for state space systems. The monotonicity and robustness of the algorithms for MIMO, discrete, state space systems is characterized by frequency domain conditions. The applicability of the results is extended using .-weighted norms.

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Norm Optimal Iterative Learning Control,Convergence conditions are established and frequency attenuation and eigenstructure interpretations presented. Robustness conditions are put forward and written in frequency domain terms for discrete state space systems. The effect of non-minimum-phase zeros on performance is described in terms of the “flat-lining” phenomenon.

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State Space Models,The chapter provides the relevant language and techniques needed for the use of linear, continuous and discrete time state space models in later chapters. It includes both time and frequency domain modelling tools, some properties of inverse systems and the elements of linear quadratic optimal tracking control.