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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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 |
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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. |
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School of Electrical and Electronic Engineering Ling, Keck Voon Maciejowski, Jan Richards, Arthur Wu, Bing Fang |
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Ling, Keck Voon Maciejowski, Jan Richards, Arthur Wu, Bing Fang |
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Ling, Keck Voon Maciejowski, Jan Richards, Arthur Wu, Bing Fang Multiplexed model predictive control |
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Ling, Keck Voon |
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Multiplexed model predictive control |
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Multiplexed model predictive control |
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Multiplexed model predictive control |
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Multiplexed model predictive control |
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Multiplexed model predictive control |
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multiplexed model predictive control |
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2013 |
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https://hdl.handle.net/10356/98897 http://hdl.handle.net/10220/12675 |
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