论文标题
学会满足迭代MPC中未知的约束
Learning to Satisfy Unknown Constraints in Iterative MPC
论文作者
论文摘要
我们为线性时间不变的系统提出了一种控制设计方法，它迭代地学习以满足未知的多面体约束。在重复任务的每次迭代中，该方法使用收集的闭环轨迹数据构建了未知环境约束的估计。收集其他数据后，该估计的约束集可迭代地改进。然后，将MPC控制器设计为可靠地满足估计的约束集。本文介绍了所提出的方法的详细信息，并提供了约束满意度的强大和概率保证，这是执行任务迭代次数的函数。我们在一个详细的数字示例中证明了拟议框架的安全性，并探讨了安全性与性能权衡。
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 detailed numerical example.
