PARTICLE SWARM OPTIMIZATION ANDNEURAL NETWORK FOR FREQUENCY DOMAINIDENTIFICATION OF SERVO SYSTEM WITHFRICTION FORCE

International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering

article

Shaik Rafi Kiran¹  T.Sairama²  S.Varadarajan³ 

[1] Dept of EEE, JNTUACE;Dept of EEE, Vardhaman College of Engineering;Dept of ECE, S.V.U. College of Engineering, S.V. University

关键词: servo system;    nonlinearity;    identification;    frequency domain;    ANN;    PSO.;   

来源: Research & Reviews

【 摘 要 】

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.

【 授权许可】

Unknown