A particle-swarm-based approach for optimum design of BELBIC controller in AVR system

In this paper, Particle Swarm Optimization algorithm is applied to design an optimum intelligent controller based on brain emotional learning. BELBIC controller is tuned to improve the time domain parameters such as percent overshoot, steady state error, settling time and rise time of the step response of an Automatic Voltage Regulator. Also the convergence characteristic of fitness function averaged over the whole particles in each generation is investigated. PSO-BELBIC performance is compared with the classic PSO-PID controller.