Hybrid fuzzy-sliding mode observer design for estimation and advanced control of an ethylene polymerization process / Jarinah Mohd Ali

Observers are computational algorithms designed to estimate unmeasured state variables due to the lack of appropriate estimating devices or to replace the high-priced sensors in a plant. It is always important to determine those unknown variables before developing state feedback laws for control,...

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Main Author: Jarinah , Mohd Ali
Format: Thesis
Published: 2017
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Online Access:http://studentsrepo.um.edu.my/7186/4/All.pdf
http://studentsrepo.um.edu.my/7186/6/jarinah.pdf
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spelling my.um.stud.71862020-06-09T19:51:36Z Hybrid fuzzy-sliding mode observer design for estimation and advanced control of an ethylene polymerization process / Jarinah Mohd Ali Jarinah , Mohd Ali T Technology (General) TA Engineering (General). Civil engineering (General) Observers are computational algorithms designed to estimate unmeasured state variables due to the lack of appropriate estimating devices or to replace the high-priced sensors in a plant. It is always important to determine those unknown variables before developing state feedback laws for control, preventing process disruptions and plant shutdowns. Due to high-nonlinearities of the chemical process systems, a single observer may not be sufficient to estimate the variables resulting in offsets and slow estimation rates. Therefore, a hybrid approach will be the best solution. In this research, a hybrid observer is designed using the combination of artificial intelligence (AI) algorithm and conventional observer. The conventional observer chosen is the sliding mode observer (SMO) and it is merged with fuzzy logic to become the hybrid fuzzy-sliding mode observer or fuzzy-SMO. The fuzzy-SMO is designed in such a way that it can be adjusted to estimate several parameters without re-designing the overall structure of the observer. This feature is unique and different from the observers available in the literature. The estimated parameters are then used as the measured parameters to develop a model predictive control (MPC) for overall control of the process system. The MPC is embedded with an integrator to avoid offsets and is designed in three cases to imitate ideal and practical conditions. The first case is the known initial state without constraint, which is the ideal case for study or more likely for programming validation purposes. The second case is the unknown initial state without constraint, which also include the proposed hybrid fuzzy-SMO. The third case is the unknown initial state with input and output constraints incorporated in the system. Both the second and third cases are behaving like practical cases. Polymerization reactor for producing polyethylene plant is chosen as the case study to observe the performances of both the fuzzy-SMO and the embedded integrator MPC. In addition, the estimator is also validated using the experimental data from the polymerization pilot plant to observe the precision of the simulated data towards the real plant. 2017-02 Thesis NonPeerReviewed application/pdf http://studentsrepo.um.edu.my/7186/4/All.pdf application/pdf http://studentsrepo.um.edu.my/7186/6/jarinah.pdf Jarinah , Mohd Ali (2017) Hybrid fuzzy-sliding mode observer design for estimation and advanced control of an ethylene polymerization process / Jarinah Mohd Ali. PhD thesis, University of Malaya. http://studentsrepo.um.edu.my/7186/
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Student Repository
url_provider http://studentsrepo.um.edu.my/
topic T Technology (General)
TA Engineering (General). Civil engineering (General)
spellingShingle T Technology (General)
TA Engineering (General). Civil engineering (General)
Jarinah , Mohd Ali
Hybrid fuzzy-sliding mode observer design for estimation and advanced control of an ethylene polymerization process / Jarinah Mohd Ali
description Observers are computational algorithms designed to estimate unmeasured state variables due to the lack of appropriate estimating devices or to replace the high-priced sensors in a plant. It is always important to determine those unknown variables before developing state feedback laws for control, preventing process disruptions and plant shutdowns. Due to high-nonlinearities of the chemical process systems, a single observer may not be sufficient to estimate the variables resulting in offsets and slow estimation rates. Therefore, a hybrid approach will be the best solution. In this research, a hybrid observer is designed using the combination of artificial intelligence (AI) algorithm and conventional observer. The conventional observer chosen is the sliding mode observer (SMO) and it is merged with fuzzy logic to become the hybrid fuzzy-sliding mode observer or fuzzy-SMO. The fuzzy-SMO is designed in such a way that it can be adjusted to estimate several parameters without re-designing the overall structure of the observer. This feature is unique and different from the observers available in the literature. The estimated parameters are then used as the measured parameters to develop a model predictive control (MPC) for overall control of the process system. The MPC is embedded with an integrator to avoid offsets and is designed in three cases to imitate ideal and practical conditions. The first case is the known initial state without constraint, which is the ideal case for study or more likely for programming validation purposes. The second case is the unknown initial state without constraint, which also include the proposed hybrid fuzzy-SMO. The third case is the unknown initial state with input and output constraints incorporated in the system. Both the second and third cases are behaving like practical cases. Polymerization reactor for producing polyethylene plant is chosen as the case study to observe the performances of both the fuzzy-SMO and the embedded integrator MPC. In addition, the estimator is also validated using the experimental data from the polymerization pilot plant to observe the precision of the simulated data towards the real plant.
format Thesis
author Jarinah , Mohd Ali
author_facet Jarinah , Mohd Ali
author_sort Jarinah , Mohd Ali
title Hybrid fuzzy-sliding mode observer design for estimation and advanced control of an ethylene polymerization process / Jarinah Mohd Ali
title_short Hybrid fuzzy-sliding mode observer design for estimation and advanced control of an ethylene polymerization process / Jarinah Mohd Ali
title_full Hybrid fuzzy-sliding mode observer design for estimation and advanced control of an ethylene polymerization process / Jarinah Mohd Ali
title_fullStr Hybrid fuzzy-sliding mode observer design for estimation and advanced control of an ethylene polymerization process / Jarinah Mohd Ali
title_full_unstemmed Hybrid fuzzy-sliding mode observer design for estimation and advanced control of an ethylene polymerization process / Jarinah Mohd Ali
title_sort hybrid fuzzy-sliding mode observer design for estimation and advanced control of an ethylene polymerization process / jarinah mohd ali
publishDate 2017
url http://studentsrepo.um.edu.my/7186/4/All.pdf
http://studentsrepo.um.edu.my/7186/6/jarinah.pdf
http://studentsrepo.um.edu.my/7186/
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score 13.211869