Multivariable Tracking Control of a Bioethanol Process under Uncertainties
Bioethanol is one of the most studied alternative fuels nowadays. Due to its production process complexity and the low quality of the mathematical models that describe it, a reliable controller is needed to maximize the fuel production and minimize its environmental impact, even in the presence of u...
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doaj-3dc03d1d2c4d4623853fee638c8989f02020-11-25T02:36:42ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472020-01-01202010.1155/2020/82636908263690Multivariable Tracking Control of a Bioethanol Process under UncertaintiesM. Cecilia Fernández0M. Nadia Pantano1Emanuel Serrano2Gustavo Scaglia3Instituto de Ingeniería Química, Universidad Nacional de San Juan (UNSJ), CONICET, Av. Lib. San Martín Oeste 1109, San Juan J5400ARL, ArgentinaInstituto de Ingeniería Química, Universidad Nacional de San Juan (UNSJ), CONICET, Av. Lib. San Martín Oeste 1109, San Juan J5400ARL, ArgentinaInstituto de Ingeniería Química, Universidad Nacional de San Juan (UNSJ), CONICET, Av. Lib. San Martín Oeste 1109, San Juan J5400ARL, ArgentinaInstituto de Ingeniería Química, Universidad Nacional de San Juan (UNSJ), CONICET, Av. Lib. San Martín Oeste 1109, San Juan J5400ARL, ArgentinaBioethanol is one of the most studied alternative fuels nowadays. Due to its production process complexity and the low quality of the mathematical models that describe it, a reliable controller is needed to maximize the fuel production and minimize its environmental impact, even in the presence of uncertainty. Here, a controller for tracking optimal profiles considering model errors and external perturbations is proposed. This work is an improvement of a previously presented technique. To reduce the earlier mentioned uncertainties’ effect during the fermentation, some tracking error integrators are added in the control action calculation. This simple modification ensures the tracking error convergence to zero, even in the presence of uncertainties (demonstration available). Different tests are carried out and a performance comparison with the original controller is shown to highlight improvements in the tracking error of up to 98% when integrators are incorporated. Furthermore, a classical PI controller is contrasted with the proposed techniques.http://dx.doi.org/10.1155/2020/8263690 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
M. Cecilia Fernández M. Nadia Pantano Emanuel Serrano Gustavo Scaglia |
spellingShingle |
M. Cecilia Fernández M. Nadia Pantano Emanuel Serrano Gustavo Scaglia Multivariable Tracking Control of a Bioethanol Process under Uncertainties Mathematical Problems in Engineering |
author_facet |
M. Cecilia Fernández M. Nadia Pantano Emanuel Serrano Gustavo Scaglia |
author_sort |
M. Cecilia Fernández |
title |
Multivariable Tracking Control of a Bioethanol Process under Uncertainties |
title_short |
Multivariable Tracking Control of a Bioethanol Process under Uncertainties |
title_full |
Multivariable Tracking Control of a Bioethanol Process under Uncertainties |
title_fullStr |
Multivariable Tracking Control of a Bioethanol Process under Uncertainties |
title_full_unstemmed |
Multivariable Tracking Control of a Bioethanol Process under Uncertainties |
title_sort |
multivariable tracking control of a bioethanol process under uncertainties |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2020-01-01 |
description |
Bioethanol is one of the most studied alternative fuels nowadays. Due to its production process complexity and the low quality of the mathematical models that describe it, a reliable controller is needed to maximize the fuel production and minimize its environmental impact, even in the presence of uncertainty. Here, a controller for tracking optimal profiles considering model errors and external perturbations is proposed. This work is an improvement of a previously presented technique. To reduce the earlier mentioned uncertainties’ effect during the fermentation, some tracking error integrators are added in the control action calculation. This simple modification ensures the tracking error convergence to zero, even in the presence of uncertainties (demonstration available). Different tests are carried out and a performance comparison with the original controller is shown to highlight improvements in the tracking error of up to 98% when integrators are incorporated. Furthermore, a classical PI controller is contrasted with the proposed techniques. |
url |
http://dx.doi.org/10.1155/2020/8263690 |
work_keys_str_mv |
AT mceciliafernandez multivariabletrackingcontrolofabioethanolprocessunderuncertainties AT mnadiapantano multivariabletrackingcontrolofabioethanolprocessunderuncertainties AT emanuelserrano multivariabletrackingcontrolofabioethanolprocessunderuncertainties AT gustavoscaglia multivariabletrackingcontrolofabioethanolprocessunderuncertainties |
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