论文标题
分布稳健的模型预测控制，总变异距离
Distributionally Robust Model Predictive Control with Total Variation Distance
论文作者
论文摘要
本文使用总变化距离歧义集研究了分布鲁棒模型预测控制（MPC）的问题。对于具有加性干扰的离散时间线性系统，我们为MPC优化问题提供了有条件的价值重新印度，该重新设置在预期的成本和机会限制下在分配上具有稳定性。分布强大的机会限制因更简单，收紧的机会约束而被过度添加，从而减轻了计算负担。数值实验支持我们的概率保证和计算效率的结果。
This paper studies the problem of distributionally robust model predictive control (MPC) using total variation distance ambiguity sets. For a discrete-time linear system with additive disturbances, we provide a conditional value-at-risk reformulation of the MPC optimization problem that is distributionally robust in the expected cost and chance constraints. The distributionally robust chance constraint is over-approximated as a simpler, tightened chance constraint that reduces the computational burden. Numerical experiments support our results on probabilistic guarantees and computational efficiency.
