.png){kind=icon}
Return
Robust MPC with recursive model update
.png){kind=icon}
OA
AI
DOI：10.1016/j.automatica.2019.02.023.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
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.
Keywords:
Model predictive control
Adaptive model predictive control
Receding horizon control
Control of constrained systems
Adaptive control
Uncertain linear systems
System identification
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
5.9
Papers:
1.2W
Citations:
5.2W
Organization
Cited Papers
ShareSaveShareSaveDehydration of 2-Hydroxyimines with Dicyclohexylcarbodiimide: An Access to Cyclic Alkenylimines.png)
Synlett
SynlettIF0
PREAI
ShareSaveShareSave
View more