Distributed non-cooperative robust MPC based on reduced-order models
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 direct...
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sg-ntu-dr.10356-806182020-03-07T13:57:22Z Distributed non-cooperative robust MPC based on reduced-order models Long, Yushen Liu, Shuai Xie, Lihua Johansson, Karl Henrik School of Electrical and Electronic Engineering Model predictive control Distributed control Building energy efficiency 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. Accepted version 2016-05-20T03:38:21Z 2019-12-06T13:53:19Z 2016-05-20T03:38:21Z 2019-12-06T13:53:19Z 2016 Journal Article Long, Y., Liu, S., Xie, L., & Johansson, K. H. (2016). Distributed non-cooperative robust MPC based on reduced-order models. Control Theory and Technology, 14(1), 11-20. 2095-6983 https://hdl.handle.net/10356/80618 http://hdl.handle.net/10220/40553 10.1007/s11768-016-5125-7 en Control Theory and Technology © 2016 Springer International Publishing AG. This is the author created version of a work that has been peer reviewed and accepted for publication by Control Theory and Technology, Springer International Publishing AG. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1007/s11768-016-5125-7]. 9 p. application/pdf |
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Model predictive control Distributed control Building energy efficiency Long, Yushen Liu, Shuai Xie, Lihua Johansson, Karl Henrik Distributed non-cooperative robust MPC based on reduced-order models |
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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. |
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School of Electrical and Electronic Engineering |
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School of Electrical and Electronic Engineering Long, Yushen Liu, Shuai Xie, Lihua Johansson, Karl Henrik |
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Article |
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Long, Yushen Liu, Shuai Xie, Lihua Johansson, Karl Henrik |
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Long, Yushen |
title |
Distributed non-cooperative robust MPC based on reduced-order models |
title_short |
Distributed non-cooperative robust MPC based on reduced-order models |
title_full |
Distributed non-cooperative robust MPC based on reduced-order models |
title_fullStr |
Distributed non-cooperative robust MPC based on reduced-order models |
title_full_unstemmed |
Distributed non-cooperative robust MPC based on reduced-order models |
title_sort |
distributed non-cooperative robust mpc based on reduced-order models |
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2016 |
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https://hdl.handle.net/10356/80618 http://hdl.handle.net/10220/40553 |
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1681036699996520448 |