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- W3183326909 abstract "Recent years have witnessed renewed interest in so-called data-enabled control, in which predictions of system behavior are made directly from measured data, in place of an explicit system model. In this paper, we consider systems with measurable disturbances, and present a closed-loop data enabled predictive control strategy that searches over sequences of feedback policies, thereby extending the open-loop strategy recently proposed in Huang et al. [1]. We make use of recent advances in polytopic containment to derive convex formulations of the minimax (i.e. worst-case disturbances) control problem, for a variety of cost functions, constraints, and sets of allowable disturbances. Advantages over open-loop and nonrobust alternatives are illustrated with a numerical example." @default.
- W3183326909 created "2021-08-02" @default.
- W3183326909 creator A5014393565 @default.
- W3183326909 date "2021-05-25" @default.
- W3183326909 modified "2023-09-26" @default.
- W3183326909 title "Closed-loop data-enabled predictive control" @default.
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- W3183326909 doi "https://doi.org/10.23919/acc50511.2021.9483211" @default.
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