期刊
2020 59TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC)
卷 -, 期 -, 页码 6204-6209出版社
IEEE
关键词
-
资金
- National Physical Science Consortium
- National Institute of Standards and Technology [ONR-N00014-18-1-2833]
We propose a control design method for linear time-invariant systems that iteratively learns to satisfy unknown polyhedral state constraints. At each iteration of a repetitive task, the method constructs an estimate of the unknown environment constraints using collected closed-loop trajectory data. This estimated constraint set is improved iteratively upon collection of additional data. An MPC controller is then designed to robustly satisfy the estimated constraint set. This paper presents the details of the proposed approach, and provides robust and probabilistic guarantees of constraint satisfaction as a function of the number of executed task iterations. We demonstrate the safety of the proposed framework and explore the safety vs. performance trade-off in a numerical example.
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