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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Muchaiteyi Togo,Heila Lotz-SisitkaIn Chap.?., the DAILC methods can achieve an almost perfect tracking performance over a finite time interval even though both the initial states and the target trajectories vary iteratively. However, all of them have to impose linear growth conditions on the nonlinearities to provide global stability.

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https://doi.org/10.1007/978-3-319-02375-5Chapter . shows some initial results of DAILC methods for nonlinear systems under linear growth conditions where the KTL-like technology is adopted for convergence analysis.

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Mohammadsajjad Sheikhmiri,Tomayess IssaDistributed control that aims for consensus tasks of multi-agent systems has progressed rapidly with a wide range of applications

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Discrete-Time Adaptive ILC for?Nonlinear Parametric SystemsIn control practice, many control tasks end in a finite interval and repeat. Examples are the track-following control of a hard disk drive, and the temperature or pressure control in a batch reactor. In such a circumstance, iterative learning control (ILC) methods, evolved over the past nearly four decades

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Data-Weighted Discrete-Time Adaptive ILCIn Chap.?., the DAILC methods can achieve an almost perfect tracking performance over a finite time interval even though both the initial states and the target trajectories vary iteratively. However, all of them have to impose linear growth conditions on the nonlinearities to provide global stability.

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Neural Network-Based Discrete-Time Adaptive ILCAs we all know that neural network (NN) has the property of universal approximation to nonlinear functions, it is therefore considered as a general tool for modeling a nonlinear function and has been applied to the adaptive control systems.

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https://doi.org/10.1007/978-981-19-0464-6Iterative Learning Control; Adaptive Iterative Learning Control; Terminal Iterative Learning Control; D