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Risk-Aware Maximum Hands-Off Control Using Worst-Case Conditional Value-at-Risk
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DOI：10.1109/TAC.2023.3235246.png)
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Abstract
En 中文
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.
Keywords:
Conditional value-at-risk (CVaR)
maximum hands-off control
model predictive control (MPC)
networked control systems
stochastic systems
Journal
IF:
7
Papers:
1.3W
Citations:
6.7W