It is well known that the process of tuning a fuzzy logic controller is almost always a very complex task, which is time consuming, very laborious and often requires expert knowledge of the controlled system. Mapping of fuzzy logic controller's parameters (rule base and membership functions of input(s) and output(s)) into a performance measure in a closed analytical form is near impossible to get, and thus the use of any classical optimization method is automatically ruled out. Knowing this, genetic algorithms with a fitness function in a form of cumulative response error represent a good choice of the optimization method. This approach enables the use of offline optimization of membership functions' parameters (which are being coded into chromosomes). Sugeno-Takagi fuzzy logic controllers with a proportional and a derivative component, and also with a fixed rule base are used in this approach. Experimental results of both simulations and validations on real systems are given in this paper and they show the good performance of this approach.

The paper deals with one of frequently encountered tasks in process industry - water level control. Proportional Integral Derivative (PID) control is often used for this purpose. Since control parameters of PID controller are fixed and tank system is inherently nonlinear, PID controller should not be used on wider level range. Therefore, this paper analyzes the effectiveness of water level control using fuzzy controller. The fuzzy controller is implemented based on mathematical model of tank and using MATLAB. The controller is implemented on Friendly ARM - embedded computer. Arduino board is used as an acquisition board for collecting sensor data from tank system Festo Didactic DD 3100 and as a PWM signal generator for water pump control. Experimental results confirm that the fuzzy control system has good adaptability in comparison with PID and provided satisfying results.