Scenario-Based Probabilistic Reachable Sets for Recursively Feasible Stochastic Model Predictive Control

被引:48
作者
Hewing, Lukas [1 ]
Zeilinger, Melanie N. [1 ]
机构
[1] Swiss Fed Inst Technol, Inst Dynam Syst & Control, CH-8092 Zurich, Switzerland
来源
IEEE CONTROL SYSTEMS LETTERS | 2020年 / 4卷 / 02期
基金
瑞士国家科学基金会;
关键词
Predictive control for linear systems; constrained control; stochastic optimal control;
D O I
10.1109/LCSYS.2019.2949194
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This letter presents a stochastic model predictive control approach (MPC) for linear discrete-time systems subject to unbounded and correlated additive disturbance sequences, which makes use of the scenario approach for offline computation of probabilistic reachable sets. These sets are used in a tube-based MPC formulation, resulting in low computational requirements. Using a recently proposed MPC initialization scheme and nonlinear tube controllers, we provide recursive feasibility and closed-loop chance constraint satisfaction, as well as hard input constraint guarantees, which are typically challenging in tube-based formulations with unbounded noise. The approach is demonstrated in simulation for the control of an overhead crane system.
引用
收藏
页码:450 / 455
页数:6
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