– Nowadays, in order to improve current applications, industry incorporates to their solution approaches artificial intelligence techniques and methodologies like Fuzzy Logic, neural networks and/or genetic algorithms (GA). Artificial intelligence techniques complement classical methodologies and include concepts that simulate the way humans solve problems or how processes work in nature. In this work, the Fuzzy Logic system cancels the effects of load perturbances in an energy plant, by implementing a secondary controller which complements the main controller. The purpose of this paper is to use GA to tune this new secondary controller. The authors particularize the proposal for three specific applications: control the angular speed and position of a Direct Current (DC) motor and control the output voltage of a DC/DC buck converter.
– The authors use GA for tuning a Proportional-Integral Fuzzy Controller (PI-Fuzzy). The proposal defines a new objective function in comparison with literature approaches. The main key in the new objective function is combining the best features of Integral Square Error (ISE) function and taking out the overshoot response.
– In order to demonstrate the proposed methodology based on GA tuning a PI-Fuzzy, the authors apply the literature benchmark to the solution. The results are compared with the following techniques: Robust control, continuous PID control, discrete PID control, Optimal Control, Fuzzy Control and Artificial Neural Network based control. Comparisons are presented in terms of setting time and overshot.
– Results demonstrate that ISE or integral of absolute value of error function do not provide the desired response. Achieved results demonstrate the usefulness of the proposal to eliminate the overshoot of the traditional behaviour without lost any of the main features of the literature methodologies.
