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Y. Long,S. Liu,L. Xie,K. H. Johansson.[en_title][J].Control Theory and Technology,2016,14(1):11~20.[Copy]
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Distributed non-cooperative robust MPC based on reduced-order models
Y.Long,S.Liu,L.Xie,K.H.Johansson
0
(School of Electrical and Electronic Engineering, Nanyang Technological University)
摘要:
In this paper, a non-cooperative distributed MPC algorithm based on reduced order model is proposed to stabilize large-scale systems. The large-scale system consists of a group of interconnected subsystems. Each subsystem can be partitioned into two parts: measurable part, whose states can be directly measured by sensors, and the unmeasurable part. In the online computation phase, only the measurable dynamics of the corresponding subsystem and neighbour-to-neighbour communication are necessary for the local controller design. Satisfaction of the state constraints and the practical stability are guaranteed while the complexity of the optimization problem is reduced. Numerical examples are given to show the effectiveness of this algorithm.
关键词:  Model predictive control, distributed control, building energy efficiency
DOI:
Received:December 01, 2015Revised:December 20, 2015
基金项目:
Distributed non-cooperative robust MPC based on reduced-order models
Y. Long,S. Liu,L. Xie,K. H. Johansson
(School of Electrical and Electronic Engineering, Nanyang Technological University;ACCESS Linnaeus Center, School of Electrical Engineering, Royal Institute of Technology)
Abstract:
In this paper, a non-cooperative distributed MPC algorithm based on reduced order model is proposed to stabilize large-scale systems. The large-scale system consists of a group of interconnected subsystems. Each subsystem can be partitioned into two parts: measurable part, whose states can be directly measured by sensors, and the unmeasurable part. In the online computation phase, only the measurable dynamics of the corresponding subsystem and neighbour-to-neighbour communication are necessary for the local controller design. Satisfaction of the state constraints and the practical stability are guaranteed while the complexity of the optimization problem is reduced. Numerical examples are given to show the effectiveness of this algorithm.
Key words:  Model predictive control, distributed control, building energy efficiency