Multiplexed model predictive control
During the last two decades, Model Predictive Control (MPC) has established itself as an important form of advanced control due to its ability to deal with constraints. This results in demanding on-line optimization, hence computing resource can become an issue when applying MPC to complex systems w...
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sg-ntu-dr.10356-193712023-07-04T16:08:59Z Multiplexed model predictive control Wu, Bing Fang Ling Keck Voon School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering During the last two decades, Model Predictive Control (MPC) has established itself as an important form of advanced control due to its ability to deal with constraints. This results in demanding on-line optimization, hence computing resource can become an issue when applying MPC to complex systems with many inputs or with fast response times. In this thesis, a novel algorithm, called the Multiplexed MPC is proposed. The Multiplexed MPC scheme divides the MPC problem into a sequence of smaller optimizations, solves each subsystem sequentially and updates subsystem controls as soon as the solution is available, thus distributing the control moves over a complete update cycle while the total number of control moves in a given period remains the same as that of the original MPC problem. This results in reduced computational complexity and thus shorter computational time. This reduction in computational complexity allows Multiplexed MPC to be executed at higher sampling rate, which in turn reacts faster to disturbances and thus can lead to improved performance in some cases, despite finding sub-optimal solutions to the original problem. Stability of the Multiplexed MPC is established in this thesis. DOCTOR OF PHILOSOPHY (EEE) 2009-12-11T08:49:37Z 2009-12-11T08:49:37Z 2009 2009 Thesis Wu, B. F. (2009). Multiplexed model predictive control. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/19371 10.32657/10356/19371 en 112 p. application/pdf |
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DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering Wu, Bing Fang Multiplexed model predictive control |
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During the last two decades, Model Predictive Control (MPC) has established itself as an important form of advanced control due to its ability to deal with constraints. This results in demanding on-line optimization, hence computing resource can become an issue when applying MPC to complex systems with many inputs or with fast response times. In this thesis, a novel algorithm, called the Multiplexed MPC is proposed. The Multiplexed MPC scheme divides the MPC problem into a sequence of smaller optimizations, solves each subsystem sequentially
and updates subsystem controls as soon as the solution is available, thus distributing the control moves over a complete update cycle while the total number of control moves in a given period remains the same as that of the original MPC problem. This results in reduced computational complexity and thus shorter computational time. This reduction in computational complexity allows Multiplexed MPC to be executed at higher sampling rate, which in turn reacts faster to disturbances
and thus can lead to improved performance in some cases, despite finding sub-optimal solutions to the original problem. Stability of the Multiplexed MPC is established in this thesis. |
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Ling Keck Voon |
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Ling Keck Voon Wu, Bing Fang |
format |
Theses and Dissertations |
author |
Wu, Bing Fang |
author_sort |
Wu, Bing Fang |
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 |
2009 |
url |
https://hdl.handle.net/10356/19371 |
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1772829167158558720 |