Dimension Reduction for Efficient Data-Enabled Predictive Control
Kaixiang Zhang, Yang Zheng, Chao Shang, Zhaojian Li
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.
Topics & Concepts
Singular value decompositionModel predictive controlDimension (graph theory)Dimensionality reductionComputationReduction (mathematics)Computer scienceParametric statisticsInvariant (physics)AlgorithmData reductionLTI system theoryRepresentation (politics)Process (computing)Artificial intelligenceMathematical optimizationControl (management)Data miningLinear systemMathematicsStatisticsPure mathematicsOperating systemMathematical analysisMathematical physicsPoliticsGeometryLawPolitical scienceControl Systems and IdentificationAdvanced Control Systems OptimizationFault Detection and Control Systems