Stability in data-driven MPC: an inherent robustness perspective

被引:8
作者
Berberich, Julian [1 ]
Koehler, Johannes [2 ]
Mueller, Matthias A. [3 ]
Allgower, Frank [1 ]
机构
[1] Univ Stuttgart, Inst Syst Theory & Automat Control, D-70550 Stuttgart, Germany
[2] Swiss Fed Inst Technol, Inst Dynam Syst & Control, ZH-8092 Zurich, Switzerland
[3] Leibniz Univ Hannover, Inst Automat Control, D-30167 Hannover, Germany
来源
2022 IEEE 61ST CONFERENCE ON DECISION AND CONTROL (CDC) | 2022年
基金
欧洲研究理事会;
关键词
MODEL-PREDICTIVE CONTROL; TO-STATE STABILITY; SYSTEMS;
D O I
10.1109/CDC51059.2022.9993361
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Data-driven model predictive control (DD-MPC) based on Willems' Fundamental Lemma has received much attention in recent years, allowing to control systems directly based on an implicit data-dependent system description. The literature contains many successful practical applications as well as theoretical results on closed-loop stability and robustness. In this paper, we provide a tutorial introduction to DD-MPC for unknown linear time-invariant (LTI) systems with focus on (robust) closed-loop stability. We first address the scenario of noise-free data, for which we present a DD-MPC scheme with terminal equality constraints and derive closed-loop properties. In case of noisy data, we introduce a simple yet powerful approach to analyze robust stability of DD-MPC by combining continuity of DD-MPC w.r.t. noise with inherent robustness of model-based MPC, i.e., robustness of nominal MPC w.r.t. small disturbances. Moreover, we discuss how the presented proof technique allows to show closed-loop stability of a variety of DD-MPC schemes with noisy data, as long as the corresponding model-based MPC is inherently robust.
引用
收藏
页码:1105 / 1110
页数:6
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