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...

Full description

Saved in:
Bibliographic Details
Main Author: Wu, Bing Fang
Other Authors: Ling Keck Voon
Format: Theses and Dissertations
Language:English
Published: 2009
Subjects:
Online Access:https://hdl.handle.net/10356/19371
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-19371
record_format dspace
spelling 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
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering
spellingShingle DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering
Wu, Bing Fang
Multiplexed model predictive control
description 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.
author2 Ling Keck Voon
author_facet 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
_version_ 1772829167158558720