Multiplexed model predictive control

This paper proposes a form of MPC in which the control variables are moved asynchronously. This contrasts with most MIMO control schemes, which assume that all variables are updated simultaneously. MPC outperforms other control strategies through its ability to deal with constraints. This requires o...

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Main Authors: Ling, Keck Voon, Maciejowski, Jan, Richards, Arthur, Wu, Bing Fang
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2013
Online Access:https://hdl.handle.net/10356/98897
http://hdl.handle.net/10220/12675
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-988972020-03-07T14:00:28Z Multiplexed model predictive control Ling, Keck Voon Maciejowski, Jan Richards, Arthur Wu, Bing Fang School of Electrical and Electronic Engineering This paper proposes a form of MPC in which the control variables are moved asynchronously. This contrasts with most MIMO control schemes, which assume that all variables are updated simultaneously. MPC outperforms other control strategies through its ability to deal with constraints. This requires on-line optimization, hence computational complexity can become an issue when applying MPC to complex systems with fast response times. The Multiplexed MPC (MMPC) scheme described in this paper solves the MPC problem for each subsystem sequentially, and updates subsystem controls as soon as the solution is available, thus distributing the control moves over a complete update cycle. The resulting computational speed-up allows faster response to disturbances, which may result in improved performance, despite finding sub-optimal solutions to the original problem. This paper describes nominal and robust MMPC, states some stability results, and demonstrates the effectiveness of MMPC through two examples. 2013-07-31T09:07:11Z 2019-12-06T20:00:54Z 2013-07-31T09:07:11Z 2019-12-06T20:00:54Z 2012 2012 Journal Article Ling, K. V., Maciejowski, J., Richards, A.,& Wu, B. F. (2012). Multiplexed model predictive control. Automatica, 48(2), 396-401. 0005-1098 https://hdl.handle.net/10356/98897 http://hdl.handle.net/10220/12675 10.1016/j.automatica.2011.11.001 en Automatica
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
description This paper proposes a form of MPC in which the control variables are moved asynchronously. This contrasts with most MIMO control schemes, which assume that all variables are updated simultaneously. MPC outperforms other control strategies through its ability to deal with constraints. This requires on-line optimization, hence computational complexity can become an issue when applying MPC to complex systems with fast response times. The Multiplexed MPC (MMPC) scheme described in this paper solves the MPC problem for each subsystem sequentially, and updates subsystem controls as soon as the solution is available, thus distributing the control moves over a complete update cycle. The resulting computational speed-up allows faster response to disturbances, which may result in improved performance, despite finding sub-optimal solutions to the original problem. This paper describes nominal and robust MMPC, states some stability results, and demonstrates the effectiveness of MMPC through two examples.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Ling, Keck Voon
Maciejowski, Jan
Richards, Arthur
Wu, Bing Fang
format Article
author Ling, Keck Voon
Maciejowski, Jan
Richards, Arthur
Wu, Bing Fang
spellingShingle Ling, Keck Voon
Maciejowski, Jan
Richards, Arthur
Wu, Bing Fang
Multiplexed model predictive control
author_sort Ling, Keck Voon
title Multiplexed model predictive control
title_short Multiplexed model predictive control
title_full Multiplexed model predictive control
title_fullStr Multiplexed model predictive control
title_full_unstemmed Multiplexed model predictive control
title_sort multiplexed model predictive control
publishDate 2013
url https://hdl.handle.net/10356/98897
http://hdl.handle.net/10220/12675
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