Modern Optimization Techniques for PID Parameters of Electrohydraulic Servo Control System

Abstract

Electrohydraulic servo system has been used in industry in a wide number of applications. Its dynamics are highly nonlinear and also have large extent of model uncertainties and external disturbances. In order to in-crease the reliability, controllability and utilizing the superior speed of response achievable from electrohydraulic systems, further research is required to develop a control software has the ability of overcoming the problems of system nonlinearities. In This paper, a Proportional Integral Derivative (PID) controller is designed and attached to electrohydraulic servo actuator system to control its stability. The PID parameters are optimized by using four techniques: Particle Swarm Optimization (PSO), Bacteria Foraging Algorithm (BFA), Genetic Algorithm (GA), and Ant colony optimization (ACO). The simulation results show that the steady-state error of system is eliminated; the rapidity is enhanced by PSO applied on Proportional Integral Derivative (PPID), Bacteria Foraging Algorithm applied on Proportional Integral Derivative (BPID), GA applied on Proportional Integral Derivative (GPID), and ACO Algorithm applied on Proportional Integral Derivative (ACO-PID) controllers when the system parameter variation was happened, and has good performances using in real applications. A comparative study between used modern optimization techniques are described in the paper and the tradeoff between them.