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Nonlinear offset-free model predictive control

delete2012-09-01
delete155
PRE
AI
M
Manfred Morari *
U
Urban Maeder
DOI:10.1016/j.automatica.2012.06.038delete
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Abstract

Abstract

En 中文
This paper addresses offset-free reference tracking of asymptotically constant reference signals using Model Predictive Control. Existing results for linear models are extended to general nonlinear models. The core of the proposed method employs a disturbance model and an observer to estimate its state. Typical disturbance models are shown and the implications of using them are discussed. Conditions are given for which this setup eliminates the tracking error asymptotically. Basically, we prove that error free output estimation and error free nominal tracking imply offset-free Model Predictive Control. (C) 2012 Elsevier Ltd. All rights reserved.
Keywords:
Control of constrained systems
Output feedback control
Disturbance rejection
Tracking
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Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Automatica cover
Automatica
IF:
5.9
Papers:
1.2W
Citations:
5.2W

Organization

S
swiss federal institutes of technology domain
Scholars:
9.0W
Papers: 8.0W
Citations: 163
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