A Comparison Among Random Search Algorithms for PID Controller Optimization

Abstract

Random search algorithms have been widely applied in practice to solve optimization problems. In the field of automatic control, there has been much research applying random search algorithms to optimize the parameter of Proportional – Integral - Derivative (PID) controller. The content of this research mainly stops at comparing the performance of the PID controller using the traditional tuning method with the optimization algorithm-based tuning method. Therefore, there is a need for research to compare the performance among random search algorithms for finding optimal parameters of the PID controller. In this paper, authors simulate five well-known and modern stochastic search algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Chemical Reaction Optimization (CRO), Teaching Learning Based Optimization (TLBO) to find optimal parameters of the PID controller for controlling the speed of the DC motor. The simulation results showed that the CRO algorithm is the most suitable to optimize PID controller parameters for the proposed problem.