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Robust MPC with recursive model update
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DOI：10.1016/j.automatica.2019.02.023.png)
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摘要
En 中文
Robust constrained control of linear systems with parametric uncertainty and additive disturbance is addressed. The main contribution is the introduction of a mathematically rigorous and computationally tractable framework for stabilizing model predictive control with online parameter estimation to improve performance and reduce conservatism. Requirements for closed-loop stability and provable constraint satisfaction are considered separately, resulting in the use of online set-membership system identification combined with homothetic prediction tubes for robust constraint satisfaction, and an i optimal point estimate of the unknown parameters to achieve a finite closed-loop gain from the disturbance to the state. Extensions to time-varying parameters and persistently exciting inputs to guarantee parameter convergence are presented. A numerical example illustrates the proven properties and efficacy of the approach. (C) 2019 Elsevier Ltd. All rights reserved.
Keyword:
Model predictive control
Adaptive model predictive control
Receding horizon control
Control of constrained systems
Adaptive control
Uncertain linear systems
System identification
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期刊
IF:
5.9
论文数:
1.2W
被引数:
5.2W
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