控制理论(社会学)
稳健性(进化)
模型预测控制
指数稳定性
有界函数
鲁棒控制
线性系统
数学
上下界
噪音(视频)
LTI系统理论
计算机科学
控制系统
控制(管理)
工程类
人工智能
非线性系统
数学分析
生物化学
化学
物理
量子力学
电气工程
基因
图像(数学)
作者
Julian Berberich,Johannes Köhler,Matthias A. Müller,Frank Allgöwer
出处
期刊:IEEE Transactions on Automatic Control
[Institute of Electrical and Electronics Engineers]
日期:2021-04-01
卷期号:66 (4): 1702-1717
被引量:304
标识
DOI:10.1109/tac.2020.3000182
摘要
We propose a robust data-driven model predictive control (MPC) scheme to control linear time-invariant (LTI) systems. The scheme uses an implicit model description based on behavioral systems theory and past measured trajectories. In particular, it does not require any prior identification step, but only an initially measured input-output trajectory as well as an upper bound on the order of the unknown system. First, we prove exponential stability of a nominal data-driven MPC scheme with terminal equality constraints in the case of no measurement noise. For bounded additive output measurement noise, we propose a robust modification of the scheme, including a slack variable with regularization in the cost. We prove that the application of this robust MPC scheme in a multi-step fashion leads to practical exponential stability of the closed loop w.r.t. the noise level. The presented results provide the first (theoretical) analysis of closed-loop properties, resulting from a simple, purely data-driven MPC scheme.
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