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- W4386271712 abstract "For linear systems, many data-driven control methods rely on the behavioral framework, using historical data of the system to predict the future trajectories. However, measurement noise introduces errors in predictions. When the noise is bounded, we propose a method for designing historical experiments that enable the computation of an upper bound on the prediction error. This approach allows us to formulate a minimax control problem where robust constraint satisfaction is enforced. We derive an upper bound on the suboptimality gap of the resulting control input sequence compared to optimal control utilizing accurate measurements. As demonstrated in numerical experiments, the solution derived by our method can achieve constraint satisfaction and a small suboptimality gap despite the measurement noise." @default.
- W4386271712 created "2023-08-31" @default.
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- W4386271712 date "2023-08-27" @default.
- W4386271712 modified "2023-10-16" @default.
- W4386271712 title "Data-Driven Robust Control Using Prediction Error Bounds Based on Perturbation Analysis" @default.
- W4386271712 doi "https://doi.org/10.48550/arxiv.2308.14178" @default.
- W4386271712 hasPublicationYear "2023" @default.
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