Metaheuristic Tuning and Practical Implementation of a PID Controller Employing Genetic Algorithms

Abstract

PID controllers tuning is a complex task from the optimization perspective because it is a multiobjective optimization problem, which must ensure the accomplishment of a set of desired operating conditions of the closed-loop system as the overshoot, the settling time, and the steady state error. Employing metaheuristic optimization techniques is possible to find optimal solutions for the PID tuning multiobjective optimization problem with less computational cost. This paper presents the using of genetic algorithms as metaheuristic optimization technique for the tuning of a PID controller employed for the speed control of a motor-generator system. The genetic algorithm is designed to find the PID controller proportional, integral, and derivate terms that ensure the desired overshoot and settling time of the motor-generator system. The practical implementation of the PID controller is performed with a data acquisition card and the Matlab Stateflow toolbox. The proposed controller is contrasted with a PID controller tuned by the Internal Model Control technique. A robustness analysis is performed to evaluate the system response in the presence of the external disturbances. Obtained results shown that the PID controller tuned by genetic algorithm has a better response in the presence of external disturbances.