抽象的

PARTICLE SWARM OPTIMIZATION AND NEURAL NETWORK FOR FREQUENCY DOMAIN IDENTIFICATION OF SERVO SYSTEM WITH FRICTION FORCE

Shaik Rafi Kiran, Dr.T.Sairama, Dr.S.Varadarajan

Generally, the mechanical devices come with undesirable nonlinearities. Due to these nonlinearities the frequency domain system identification process in servo system seems to be a tough task. In the paper, particle swarm optimization (PSO) algorithm based hybrid technique is proposed for the frequency domain identification of servo system. The proposed hybrid technique is the combination of artificial neural network (ANN) and PSO algorithm. Initially, the system parameters are generated as a data set at different mass level by the artificial network. From the dataset, the PSO algorithm is used to optimize the system parameters such as pole, constant, DC gain and friction force etc. Then, the optimized parameters are applied to the system and the friction of system is analyzed in terms of velocity. The proposed identification method is implemented in MATLAB working platform and the deviation performances are evaluated. The system parameters identified by proposed method (PSO-ANN) is compared with actual system, GA-ANN, and adaptive GA-ANN.