Title :
Neural network based modeling of traveling wave ultrasonic motor using genetic algorithm
Author :
Jahani, Mohammad ; Mojallali, Hamed
Author_Institution :
Najaf Abad Branch, Dept. of Electr. Eng., Islamic Azad Univ., Isfahan, Iran
Abstract :
Ultrasonic motors (USM) have heavy nonlinear and load dependent characteristics such as dead-zone and saturation reverse effects, which vary with driving conditions. These features make serious problem in modeling of this kind of motors. In this paper, Modeling of Traveling Wave type Ultrasonic Motor (TWUSM) by means of genetic algorithm & neural network-based Hammerstein model is presented, in which the nonlinear static part is approximated by a genetic algorithm (GA) based radial basis function neural network (RBFNN) and the linear dynamic part is modeled by a transfer function model. GA is also adopted to optimize the hidden centers, the radial basis function widths and the weights of the RBFNN. The simulation results verify significantly improved matching between measured and simulated data.
Keywords :
actuators; genetic algorithms; radial basis function networks; transfer functions; ultrasonic motors; Hammerstein model; dead-zone effects; genetic algorithm; heavy nonlinear characteristics; load dependent characteristics; neural network-based modeling; nonlinear static part; radial basis function neural network; saturation reverse effects; transfer function model; traveling wave type ultrasonic motor; Actuators; Aerodynamics; Clocks; Gears; Genetic algorithms; Genetic engineering; Neural networks; Radial basis function networks; Torque; Transfer functions; Genetic Algorithm; Modeling; Neural Network; Ultrasonic Motor;
Conference_Titel :
Computer and Automation Engineering (ICCAE), 2010 The 2nd International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-5585-0
Electronic_ISBN :
978-1-4244-5586-7
DOI :
10.1109/ICCAE.2010.5451254
