Year: 2023
Type: article
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.
Source: IEEE Control Systems Letters
Authors: Kaixiang Zhang, Yang Zheng, Chao Shang, Zhaojian Li
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Funder: National Science Foundation
Awards: Matrix Decomposition for Scalable Conic Optimization with Applications to Distributed Control and Machine Learning, Collaborative Research: Scalable Data-Enabled Predictive Control for Heterogeneous Mixed Traffic Systems, Collaborative Research: Scalable Data-Enabled Predictive Control for Heterogeneous Mixed Traffic Systems
OpenAlex ID: W4387448461