Date of Award
2-1-2017
Thesis Type
phd
Document Type
Thesis (Restricted Access)
Divisions
eng
Department
Faculty of Engineering
Institution
University of Malaya
Abstract
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.
Note
Thesis (PhD) - Faculty of Engineering, University of Malaya, 2017.
Recommended Citation
Jarinah, Mohd Ali, "Hybrid fuzzy-sliding mode observer design for estimation and advanced control of an ethylene polymerization process / Jarinah Mohd Ali" (2017). Student Works (2010-2019). 4321.
https://knova.um.edu.my/student_works_2010s/4321