论文标题
降低有效启用数据的预测控制的尺寸
Dimension Reduction for Efficient Data-Enabled Predictive Control
论文作者
论文摘要
在这封信中，我们提出了一种简单而有效的奇异价值分解（SVD）策略，以减少支持数据支持的预测性控制（DEEPC）中的优化问题维度。具体而言，在线性时间流体系统的情况下，可以将过多的输入/输出测量值重新排列到较小的数据库中，以进行系统行为的非参数表示。基于此观察结果，我们制定了一种基于SVD的策略，以预先处理降低DIEPC的离线数据。数值实验证实，所提出的方法显着提高了计算效率，而无需牺牲控制性能。
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.
