Optimization of pi controller for level control of water tank system
The liquid level control is one of the most important parameter in most of industrial process such as in water treatment plant. The general control objective in water treatment system is to maintained a desired water level of the water tank when there is an inflow and outflow at the respective tank....
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| Format: | Thesis |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/93002/1/SarahJewahidMSKE2020.pdf |
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| Summary: | The liquid level control is one of the most important parameter in most of industrial process such as in water treatment plant. The general control objective in water treatment system is to maintained a desired water level of the water tank when there is an inflow and outflow at the respective tank. Although the water tank system is considered as a simple plant from construction perspective, the control action faces with various challenges due to the complexity of the system which are influenced by nonlinear behavior and uncertainties. Inadequate control strategies to deal with the nonlinearities and uncertainties appearance will contribute to inaccurate levelling process. Proportional -Integral - Derivative (PID) controller is widely used as control algorithm in many process due to the simplicity and ease to use. However, conventional PID controller is not satisfactory to water level control system because it cannot perform better in nonlinear system as the controller parameters need to be tuned properly and continuously throughout the whole process. Therefore, this project proposes an optimized method to determine optimum PID control parameters for level control of water tank system using swarm optimization method. The solution is based on the idea that accurate selection of controller parameters in dealing with nonlinearities and uncertainties will contribute to high precision levelling process. The approach has several notable merits, namely rapid convergence, simplicity in determining algorithm parameters and finding the best optimal point. A comprehensive verification using simulation is carried out to determine the performance of the proposed method. From the simulation work, it is shows that swarm optimization method validates its ability to tune up controller parameters with high level of precision compared with manually adjustments of the controller parameters. The proposed method is shows it has better performance than manual tuning PID controller in terms of the time response. |
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