Recursively Feasible Data-Driven Distributionally Robust Model Predictive Control With Additive Disturbances

被引：7

作者：

Mark, Christoph ^{[1
]}

Liu, Steven ^{[1
]}

机构：

[1] Univ Kaiserslautern, Inst Control Syst, Dept Elect & Comp Engn, D-67663 Kaiserslautern, Germany

来源：

IEEE CONTROL SYSTEMS LETTERS | 2023年 / 7卷

关键词：

Random variables; Linear matrix inequalities; Additives; Symmetric matrices; Stochastic processes; Predictive control; Cost function; Predictive control for linear systems; distributionally robust optimization; stochastic optimal control;

D O I：

10.1109/LCSYS.2022.3199940

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this letter we propose a data-driven distributionally robust Model Predictive Control framework for constrained stochastic systems with unbounded additive disturbances. Recursive feasibility is ensured by optimizing over a linearly interpolated initial state constraint in combination with a simplified affine disturbance feedback policy. We consider a moment-based ambiguity set with data-driven radius for the second moment of the disturbance, where we derive a minimum number of samples in order to ensure user-given confidence bounds on the chance constraints and closed-loop performance. This letter closes with a numerical example, highlighting the performance gain and chance constraint satisfaction based on different sample sizes.

引用

页码：526 / 531

页数：6

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