Efficient Recursive Data-enabled Predictive Control (Extended Version)

TL;DR

This paper presents a recursive updating algorithm for Data-enabled Predictive Control that uses SVD for efficient computation, enabling scalable and consistent data-driven control in stochastic linear systems.

Contribution

It introduces a flexible recursive SVD-based algorithm for DeePC, improving computational efficiency and encompassing various data-driven control methods.

Findings

The algorithm achieves asymptotically consistent predictions for stochastic LTI systems.

Simulation results validate the efficiency and effectiveness of the proposed method.

The approach generalizes and unifies existing data-driven control techniques.

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.