Titlebook: Discrete-Time Adaptive Iterative Learning Control; From Model-Based to Ronghu Chi,Na Lin,Ruikun Zhang Book 2022 The Editor(s) (if applicab

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Ronghu Chi,Na Lin,Ruikun ZhangFocuses on discrete-time adaptive iterative learning control (DAILC).Proposes systematic procedures for design and analysis of model-based DAILC for parametric systems.Proposes systematic procedures f

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Intelligent Control and Learning Systemshttp://image.papertrans.cn/e/image/281192.jpg

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Dimensions of Sustainability Appraisal,l performance by the use of both the operation errors and the prior control knowledge. Generally speaking, learning refers to an action of a system to adapt and change its behavior based on input/output observations.

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Science for Sustainable Societiesion. For a multi-agent system, the presented distributed DAILC method in Chap. . is suitable since it uses the consensus error in the learning control algorithm. Further, for a practical plant that is too complex to obtain the exact mechanistic model, the data-driven DAILC methods presented in Chaps. . and . are the suitable choices.

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2662-5458 AILC for parametric systems.Proposes systematic procedures fThis book belongs to the subject of control and systems theory. The discrete-time adaptive iterative learning control (DAILC) is discussed as a cutting-edge of ILC and can address random initial states, iteration-varying targets, and other

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Science for Sustainable Societiese been widely studied by introducing parametric adaptation law in the learning process. The DAILC method not only overcomes the iteration-varying reference trajectories but also removes the requirement of identical initial states to achieve a perfect tracking.

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Introduction,l performance by the use of both the operation errors and the prior control knowledge. Generally speaking, learning refers to an action of a system to adapt and change its behavior based on input/output observations.

Mechanics