论文标题
在线支持数据的预测控制
Online Data-Enabled Predictive Control
论文作者
论文摘要
我们开发了一个在线数据的预测（ODEEPC）控制方法，用于基于最近提出的DEEPC [1]的最佳控制系统。我们提出的ODEEPC方法利用具有实时测量反馈的原始二算法算法，以迭代地计算相应的实时最佳控制策略，因为系统条件发生了变化。拟议的ODEEPC概念性概念类似于标准自适应系统识别和模型预测控制（MPC），但它为标准方法提供了一种新的替代方案。 ODEEPC通过计算有效的方法来启用，该方法在DEEPC的背景下以快速的傅立叶变换（FFT）和原始偶尔算法来利用Hankel矩阵的特殊结构。我们提供有关ODEEPC渐近行为的理论保证，并通过数值示例证明了其性能。
We develop an online data-enabled predictive (ODeePC) control method for optimal control of unknown systems, building on the recently proposed DeePC [1]. Our proposed ODeePC method leverages a primal-dual algorithm with real-time measurement feedback to iteratively compute the corresponding real-time optimal control policy as system conditions change. The proposed ODeePC conceptual-wise resembles standard adaptive system identification and model predictive control (MPC), but it provides a new alternative for the standard methods. ODeePC is enabled by computationally efficient methods that exploit the special structure of the Hankel matrices in the context of DeePC with Fast Fourier Transform (FFT) and primal-dual algorithm. We provide theoretical guarantees regarding the asymptotic behavior of ODeePC, and we demonstrate its performance through numerical examples.
