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Reference governors for chance-constrained systems

delete2019-11-01
delete15
PRE
AI
U
Uroš Kalabić *
李楠 封面图
李楠 (Nan I. Li)
C
Christopher Vermillion
I
Ilya Kolmanovsky
DOI:10.1016/j.automatica.2019.108500delete
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摘要

摘要

En 中文
This paper presents a reference governor formulation that is applicable to systems with stochastic disturbances and achieves constraint satisfaction properties that are analogous to those of conventional reference governors. In particular, the reference governor proposed herein is shown to enforce chance constraints, guarantee a form of eventual recursive feasibility, and guarantee almost-sure convergence to constant, constraint-admissible reference inputs. It can also be applied to enforce constraints for closed-loop systems with state observers. This stands in contrast with traditional reference governor techniques, which must be heuristically tuned in order to achieve a balance between constraint satisfaction and the size of achievable steady-state references in the presence of stochastic disturbances. A numerical example is reported which illustrates the operation of the reference governor for chance-constrained systems and is compared to the conventional, robust approach. (C) 2019 Published by Elsevier Ltd.
Keyword:
Reference governors
Constrained systems
Predictive control
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期刊

Automatica 封面图
Automatica
IF:
5.9
论文数:
1.2W
被引数:
5.2W

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U
University of Michigan
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6.4W
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被引数: 124
U
university of michigan system
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论文数: 8.6W
被引数: 133
N
North Carolina State University
学者数:
2.6W
论文数: 2.3W
被引数: 3.7W
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引用论文

引用论文

Chance-constrained model predictive control
err2004-04-16
err234
PREAI
errSchwarm, AT; Nikolaou, M
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