Genetic Algorithm Optimization of PID Controller for Brushed DC Motor

Abstract

PID controller is one of the most common usable controller in controlling the velocity of brushed DC motor due to its reliability and simplicity. Classical design formulas to tune the PID parameter such as Ziegler-Nichols and Skogestad IMC produce less desirable results in terms of performance. In order to improve the performance, PID parameter is optimized by using Genetic Algorithm (GA) method. Integral of Time Multiplied by Absolute Error (ITAE) objective function is modified based on design requirements. The results obtained show that tuning method by using GA produce the best outcome. Rise time, settling time, percentage of overshoot yields from modified fitness function of GA is the fastest compare with Ziegler-Nichols, Skogestad IMC, and unmodified GA fitness function. The modification of ITAE fitness function improve the rise time and settling time by 76.63% and 78.29% respectively. All of the methods produce minimal steady-state error and is acceptable for velocity control application. Embedding the PID optimized by GA into the microcontroller and the comparison between the simulation and real-time application should be done in the future to evaluate the performance in real life application.