Risk-Aware Maximum Hands-Off Control Using Worst-Case Conditional Value-at-Risk

• 成果类型: Article
• 署名作者: Kishida, Masako; Nagahara, Masaaki
• 署名单位: Research Organization of Information & Systems (ROIS); National Institute of Informatics (NII) - Japan; University of Kitakyushu
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2023.3235246
• 发表日期: 2023
• 页码: 6353-6360
• 关键词: Conditional value-at-risk (CVaR); maximum hands-off control; model predictive control (MPC); networked control systems; Stochastic systems

摘要

With the view of risks, this article deals with the problems of maximum hands-off control that aims at minimizing the length of nonzero control input. More specifically, we consider stochastic systems and seek sparse control inputs that bring the system state to a ball centered at the origin, such that the expected value of the states that are further than a given threshold from the origin is small, thus minimizing the risk that the system state is outside of the ball. To deal with this problem, we employ the worst-case conditional value-at-risk under the assumption that the first two moments of the disturbance distribution are known. In particular, we consider two kinds of risk-aware maximum hands-off control problems: one enhances the sparsity within a given risk threshold, and the other minimizes the risk subject to a sparsity constraint. We also derive a risk-constrained sparse model predictive control and provide a numerical example that shows the effectiveness of the proposed approach in networked control systems.