A Simplified Approach to Multivariable Model Predictive Control
The benefits of applying the range of technologies generally known as Model Predictive Control (MPC) to the control of industrial processes have been well documented in recent years. One of the principal drawbacks to MPC schemes are the relatively high on-line computational burdens when used with...
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doaj-f74435d43b5145ddaea27e7df5daca802020-11-24T21:39:13ZengTaiwan Association of Engineering and Technology InnovationInternational Journal of Engineering and Technology Innovation2223-53292226-809X2015-01-01511932A Simplified Approach to Multivariable Model Predictive ControlMichael Short0Teesside UniversityThe benefits of applying the range of technologies generally known as Model Predictive Control (MPC) to the control of industrial processes have been well documented in recent years. One of the principal drawbacks to MPC schemes are the relatively high on-line computational burdens when used with adaptive, constrained and/or multivariable processes, which has warranted some researchers and practitioners to seek simplified approaches for its implementation. To date, several schemes have been proposed based around a simplified 1-norm formulation of multivariable MPC, which is solved online using the simplex algorithm in both the unconstrained and constrained cases. In this paper a 2-norm approach to simplified multivariable MPC is formulated, which is solved online using a vector-matrix product or a simple iterative coordinate descent algorithm for the unconstrained and constrained cases respectively. A CARIMA model is employed to ensure offset-free control, and a simple scheme to produce the optimal predictions is described. A small simulation study and further discussions help to illustrate that this quadratic formulation performs well and can be considered a useful adjunct to its linear counterpart, and still retains the beneficial features such as ease of computer-based implementation.http://sparc.nfu.edu.tw/~ijeti/download/V5-no1-19-32.pdf: Real-time and embedded controlpredictive controlmultivariable control |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Michael Short |
spellingShingle |
Michael Short A Simplified Approach to Multivariable Model Predictive Control International Journal of Engineering and Technology Innovation : Real-time and embedded control predictive control multivariable control |
author_facet |
Michael Short |
author_sort |
Michael Short |
title |
A Simplified Approach to Multivariable Model Predictive Control |
title_short |
A Simplified Approach to Multivariable Model Predictive Control |
title_full |
A Simplified Approach to Multivariable Model Predictive Control |
title_fullStr |
A Simplified Approach to Multivariable Model Predictive Control |
title_full_unstemmed |
A Simplified Approach to Multivariable Model Predictive Control |
title_sort |
simplified approach to multivariable model predictive control |
publisher |
Taiwan Association of Engineering and Technology Innovation |
series |
International Journal of Engineering and Technology Innovation |
issn |
2223-5329 2226-809X |
publishDate |
2015-01-01 |
description |
The benefits of applying the range of technologies generally known as Model Predictive Control (MPC) to the
control of industrial processes have been well documented in recent years. One of the principal drawbacks to MPC
schemes are the relatively high on-line computational burdens when used with adaptive, constrained and/or
multivariable processes, which has warranted some researchers and practitioners to seek simplified approaches for
its implementation. To date, several schemes have been proposed based around a simplified 1-norm formulation of
multivariable MPC, which is solved online using the simplex algorithm in both the unconstrained and constrained
cases. In this paper a 2-norm approach to simplified multivariable MPC is formulated, which is solved online using a
vector-matrix product or a simple iterative coordinate descent algorithm for the unconstrained and constrained cases
respectively. A CARIMA model is employed to ensure offset-free control, and a simple scheme to produce the
optimal predictions is described. A small simulation study and further discussions help to illustrate that this quadratic
formulation performs well and can be considered a useful adjunct to its linear counterpart, and still retains the
beneficial features such as ease of computer-based implementation. |
topic |
: Real-time and embedded control predictive control multivariable control |
url |
http://sparc.nfu.edu.tw/~ijeti/download/V5-no1-19-32.pdf |
work_keys_str_mv |
AT michaelshort asimplifiedapproachtomultivariablemodelpredictivecontrol AT michaelshort simplifiedapproachtomultivariablemodelpredictivecontrol |
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1725931899787935744 |