Evolutionary Soft Computing Tools Based Tuning Of PID Controller for EMA-AFC

Abstract

This paper proposes the tuning of PID controller of electromagnetic actuator (EMA) system for aero fin control (AFC) using Particle swarm optimization (PSO) and Bacterial foraging optimization (BFO). The EMA is realized with permanent magnet brush DC motor which is driven by a constant current driver. Using the non-linear model of EMAAFC system which includes the non-linearities of DC motor, a PID position controller is designed using different soft computing techniques like PSO and BFO in SIMULINK so that the system satisfies all the design requirements. We propose PSO and BFO based PID controller which is tuned by using PSO and BFO algorithms respectively. The design parameters which are to be optimized are rise time, peak time and percentage overshoot. Presented results show that the transient response and closed loop response of EMA-AFC system using PSO is better when compared that of BFO.