Fuzzy modelling and control for a nonlinear reboiler system of a distillation column
Most process control systems are complex and nonlinear in nature. To design a good controller for these systems, accurate models are needed. Most of the available conventional plant modelling techniques cater linear plants and therefore in most cases, inaccurate nonlinear plant models are obtained w...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2006
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/1567/1/MohdFaisalIbrahimMFKE2006.pdf http://eprints.utm.my/id/eprint/1567/ |
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| Summary: | Most process control systems are complex and nonlinear in nature. To design a good controller for these systems, accurate models are needed. Most of the available conventional plant modelling techniques cater linear plants and therefore in most cases, inaccurate nonlinear plant models are obtained which in turn resulted in poor control performance. In view of this, the thesis explored the Fuzzy modelling technique based on Takagi-Sugeno (TS) method for modelling a reboiler system of a distillation column. Model parameters are tuned using Genetic algorithm (GA) and Recursive least square (RLS). The Unbiasedness criterion (UC) is applied for structure identification. Data from simulation system that represents the actual plant is used in model development. Fuzzy model obtained using the proposed technique is compared with various other modelling techniques such as the conventional Fuzzy model and linear model. The result shows that the technique proposed gives a more accurate model as compared to the other methods. The optimized Fuzzy model obtained is used to design a Fuzzy controller for the temperature control of the vessel of the distillation column. Manipulated variables for the Fuzzy controller are heat from electrical heater and flowrate of silicon oil in the reboiler system. Genetic algorithm (GA) is used to tune the parameters of the Fuzzy controller. The performance of the Fuzzy controller using the optimized Fuzzy model is better than using the linear model |
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