Development of genetic algorithm-based fuzzy logic controller for conical tank process

R. Arivalahan, P. Subbaraj and D. Devaraj

International Journal of Industrial and Systems Engineering, 2013, vol. 13, issue 4, 442-461

Abstract: The proportional integral derivative controllers are widely used in industries for controlling the different process variables due to its simplicity, flexibility and efficiency. Recently, the control of non-linear processes in the industries have turned the attention towards the intelligent controllers such as neural networks, fuzzy logic controller (FLC), genetic algorithm-(GA) tuned controllers, adaptive controller, predictive controller, robust controller, etc. This work focuses on developing a GA-based FLC for conical tank. A conical tank is a highly non-linear process due to the variation in the area of cross section of the level system with change in shape. Conventionally, a parameter adaptive proportional integral (PI) controller has been designed for non-linear process. Alternatively, in this work, an intelligent controller (GA-based FLC) is designed for the control of non-linear process to ensure the exact level control. The experimental results are obtained for servo and regulatory response of the process. The GA-based FLC is compared with adaptive PI controller.

Keywords: nonlinear processes; conical tanks; adaptive PI controllers; proportional integral control; genetic algorithms; fuzzy logic controllers; FLC; fuzzy control; integral square error; intelligent control; exact level control. (search for similar items in EconPapers)
Date: 2013
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