论文标题
随机MPC具有分布强大的机会限制
Stochastic MPC with Distributionally Robust Chance Constraints
论文作者
论文摘要
在本文中，我们讨论了线性时间流体系统的随机模型预测控制（SMPC）的分布鲁棒性。我们在添加零均值I.I.D.下简单地得出了MPC问题的简单近似。噪音二次成本。由于缺乏分配信息，因此将机会限制定为分布稳健（DR）的机会约束，我们选择将其统一与概率可及的概念（PRS）统一。对于Wasserstein的歧义集，我们提出了一个简单的凸优化问题，以根据有限的许多干扰样本来计算DR-PRS。该论文以双集成符系统的数值示例关闭，突出了DR-PRS W.R.T.的可靠性。 Wasserstein集和由此产生的SMPC的性能。
In this paper we discuss distributional robustness in the context of stochastic model predictive control (SMPC) for linear time-invariant systems. We derive a simple approximation of the MPC problem under an additive zero-mean i.i.d. noise with quadratic cost. Due to the lack of distributional information, chance constraints are enforced as distributionally robust (DR) chance constraints, which we opt to unify with the concept of probabilistic reachable sets (PRS). For Wasserstein ambiguity sets, we propose a simple convex optimization problem to compute the DR-PRS based on finitely many disturbance samples. The paper closes with a numerical example of a double integrator system, highlighting the reliability of the DR-PRS w.r.t. the Wasserstein set and performance of the resulting SMPC.
