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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| 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 http://studentsrepo.um.edu.my/7186/ |
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| Summary: | 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. |
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