Abstract
This paper aims at exploring the use of designing a Fuzzy Logic Controller (FLC) tuned using a Genetic Algorithm (GA) which is proposed for a spherical tank to control its level. System identification of this nonlinear process is done using a black box model, which is approximated to be a First Order Plus Dead Time (FOPDT) model. Then, the controller tuning strategy has been applied using Skogestad's PI tuning method. A FLC performance depends on membership functions and its rule base. Genetic algorithms are powerful stochastic search algorithms used for optimization and general problem solving. In this paper, genetic algorithms are used to tune the rule base of the FLC designed for the spherical tank system. The comparison is made with the Skogestad's PI method for the nonlinear process using cost effective data acquisition ADAM's module in real time. The controller design based on GA tuned FLC shows soft computing methods perform better than a conventional controller on performance indices like Integral Squared Error (ISE) and Integral Absolute Error (IAE).
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