The recent data-enabled predictive control (DeePC) paradigm directly utilizes
offline input/output data from an unknown system to predict its future
trajectory and compute optimal control inputs online. In this scheme, the
pre-collected input/output data needs to be sufficiently rich to represent the
system behavior. This generally leads to an excessive amount of offline data,
which consequently results in a high-dimension optimization problem in online
predictive control. In this paper, we propose a simple yet effective singular
value decomposition (SVD) based strategy to reduce the optimization problem
dimension in 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 use 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
compromising the control performance.Comment: 9 pages, 4 figure