Risk-Aware Maximum Hands-Off Control Using Worst-Case Conditional Value-at-Risk
Risk-Aware Maximum Hands-Off Control Using Worst-Case Conditional Value-at-Risk
复制标题
DOI：
10.1109/tac.2023.3235246
复制
发表时间：
2023-10
影响因子：
6.8
通讯作者：
M. Kishida;M. Nagahara
中科院分区：
文献类型：
--
作者：
M. Kishida;M. Nagahara
本文从风险的角度出发，研究了以非零控制输入长度最小为目标的最大放手控制问题。更具体地说，我们考虑随机系统，并寻求稀疏的控制输入，使系统状态的球集中在原点，这样的期望值的状态，进一步从原点的给定阈值是小的，从而最大限度地减少风险，系统状态是球的外面。为了解决这个问题，我们采用了最坏情况下的条件风险值的假设下，前两个时刻的干扰分布是已知的。特别是，我们考虑两种风险意识的最大放手控制问题：一个提高了在给定的风险阈值的稀疏性，和其他最小化的风险受到稀疏性约束。我们还推导了一个风险约束稀疏模型预测控制，并提供了一个数值例子，表明所提出的方法在网络控制系统的有效性。
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.