dowagers-hump
发表于 2025-3-28 18:37:01
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
发表于 2025-3-28 18:45:17
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.
optic-nerve
发表于 2025-3-29 01:56:06
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平躺
发表于 2025-3-29 05:00:13
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.
有组织
发表于 2025-3-29 07:41:51
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.
CORD
发表于 2025-3-29 13:09:09
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asthma
发表于 2025-3-29 19:28:56
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食物
发表于 2025-3-29 21:05:58
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CALL
发表于 2025-3-30 03:33:22
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浮雕宝石
发表于 2025-3-30 04:35:24
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.