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arXiv · 2309.13755

Efficient Recursive Data-enabled Predictive Control (Extended Version)

Jicheng Shi·Yingzhao Lian·Colin N. Jones
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Abstract

In the field of model predictive control, Data-enabled Predictive Control (DeePC) offers direct predictive control, bypassing traditional modeling. However, challenges emerge with increased computational demand due to recursive data updates. This paper introduces a novel recursive updating algorithm for DeePC. It emphasizes the use of Singular Value Decomposition (SVD) for efficient low-dimensional transformations of DeePC in its general form, as well as a fast SVD update scheme. Importantly, our proposed algorithm is highly flexible due to its reliance on the general form of DeePC, which is demonstrated to encompass various data-driven methods that utilize Pseudoinverse and Hankel matrices. This is exemplified through a comparison to Subspace Predictive Control, where the algorithm achieves asymptotically consistent prediction for stochastic linear time-invariant systems. Our proposed methodologies' efficacy is validated through simulation studies.

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Jicheng Shi, Yingzhao Lian, Colin N. Jones. 2023-09-24. Efficient Recursive Data-enabled Predictive Control (Extended Version). https://arxiv.org/abs/2309.13755

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