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Robust Data-Enabled Predictive Control: Tractable Formulations and Performance Guarantees
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DOI：10.1109/TAC.2023.3241282.png)
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Abstract
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We introduce a general framework for robust data-enabled predictive control (DeePC) for linear time-invariant systems, which enables us to obtain robust and optimal control in a receding-horizon fashion based on inexact input and output data. Robust DeePC solves a min-max optimization problem to compute the optimal control sequence that is resilient to all possible realizations of the uncertainties in data within a prescribed uncertainty set. We present computationally tractable reformulations of the min-max problem with various uncertainty sets. Moreover, we show that even though an accurate prediction of the future behavior is unattainable due to inaccessibility of exact data, the obtained control sequence provides performance guarantees for the actually realized input and output cost in open loop. Finally, we demonstrate the performance of robust DeePC using high-fidelity simulations of a power converter system.
Keywords:
Uncertainty
Trajectory
Optimal control
Predictive control
Optimization
Behavioral sciences
Noise measurement
Data-driven control
predictive control
regularization
robust control
robust optimization
Journal
IF:
7
Papers:
1.3W
Citations:
6.7W
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