Dimension Reduction for Efficient Data-Enabled Predictive Control

TL;DR

This paper introduces an SVD-based dimension reduction method for data-enabled predictive control, significantly improving computational efficiency while maintaining control performance in linear systems.

Contribution

It presents a novel SVD-based pre-processing approach to reduce data dimension in DeePC, enhancing efficiency without performance loss.

Findings

Significant reduction in computation time

Maintained control performance with reduced data size

Effective for linear time-invariant systems

Abstract

In this letter, we propose a simple yet effective singular value decomposition (SVD) based strategy to reduce the optimization problem dimension in data-enabled predictive control (DeePC). Specifically, in the case of linear time-invariant systems, the excessive input/output measurements can be rearranged into a smaller data library for the non-parametric representation of system behavior. Based on this observation, we develop an SVD-based strategy to pre-process the offline data that achieves dimension reduction in DeePC. Numerical experiments confirm that the proposed method significantly enhances the computation efficiency without sacrificing the control performance.