Application of Intelligent Search Algorithms in Proportional-Integral-Derivative Control of Direct-Current Motor System

Abstract

Abstract Proportional-Integral-Derivative (PID) control is one of the famous controllers applied in industrial and robotics world. It is quite easy to understand and to be implemented. It is known that, with a proper combination of its gain parameters, a system with good performance can be obtained. This parameter tuning can be tricky since the number of the combination is almost infinite. Hence, methods to tune the parameter gains of proportional, integral and derivative is needed. Some optimization methods that can be used in tuning parameters are Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE). The paper will analyse the performance of those methods by applying them in PID Controller to control a Direct Current (DC) motor system by comparing the iteration time, the number of parameters, and augmented system performance. The comparison is done on MATLAB simulation by using the same computer. It results as DE as the best among them with the least number of parameters and best DC motor system performances.