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- W1916062419 abstract "Discrete-time repetitive controllers have traditionally been motivated with a purely deterministic problem formulation. Trade-offs for robustness, settling time and stochastic performance were at the cost of asymptotic regulation against a purely periodic disturbance. In this paper, we establish a connection between optimal prediction-based and repetitive control. We show that the simple modified repetitive control algorithm may be a good approximation of minimum variance control. Indeed, for stable plants, this control law is minimum-variance control for a particular stochastic disturbance model. This minimum-variance interpretation of repetitive control leads to a parameter optimization. Control law parameters are optimized to minimize, subject to stability and robustness constraints, the mean-squared output of a system driven by inputs with both deterministic and stochastic components. The result is a simple design procedure which is experimentally verified on a track curvature correction servo for a digital video production recorder." @default.
- W1916062419 created "2016-06-24" @default.
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- W1916062419 date "1993-06-01" @default.
- W1916062419 modified "2023-09-27" @default.
- W1916062419 title "A Parameter Optimization for the Internal-Model Repetitive Controller based on Minimum-Variance Properties" @default.
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- W1916062419 doi "https://doi.org/10.23919/acc.1993.4793173" @default.
- W1916062419 hasPublicationYear "1993" @default.
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