scholarly journals Speed Control of Ultrasonic Motor using a Variable Gain Type PID Control Based on Neural Networks

Author(s):  
Shenglin Mu ◽  
Satoru Shibata ◽  
Tomonori Yamamoto ◽  
Shota Nakashima ◽  
Kanya Tanaka
2004 ◽  
Vol 01 (03) ◽  
pp. 457-470
Author(s):  
X. H. SHI ◽  
Y. C. LIANG ◽  
X. XU

An ultrasonic motor speed control scheme is presented in this paper based on neural networks and iterative controller. Suitable ranges of the adaptive learning rates of neural network controller are presented through the theoretical analysis on the proposed model, which could guarantee its stability. The convergence of iterative controller is also discussed. Numerical results show that the control scheme is effective for various kinds of reference speeds of ultrasonic motors. Comparisons with the existing method show that the precision of control could be increased using the proposed method. Simulations also show that the proposed scheme is fairly robust against random disturbance to the control variables.


2019 ◽  
Vol 7 (1) ◽  
pp. 25-31 ◽  
Author(s):  
Shota Nakashima ◽  
Shingo Aramaki ◽  
Shenglin Mu ◽  
Huimin Lu ◽  
Kanya Tanaka

Author(s):  
Lijie Yang ◽  
Guimei Wang ◽  
Huadong Zhang ◽  
Jiehui Liu ◽  
Yachun Zhang

A special ceramic roller bearing press (SCRBP) is developed to press two bearings efficiently and precisely at the same time. A speed control mathematical model of the bearing press is built to obtain stability bearing pressing speed. The fuzzy adaptive PID controller of the bearing pressing speed of SCRBP is designed. The simulation model is also built. Fuzzy adaptive PID control is compared with conventional PID control. By simulation analysis, the simulation results show that adjusting time of fuzzy adaptive PID control is short, and its overshoot is very small, almost coincides with the set pressing speed. Moreover, fuzzy adaptive PID is suitable for the pressing speed control of the bearing pressing speed system with step interference signal. The pressing stability speed is obtained by fuzzy adaptive PID control.


2012 ◽  
Author(s):  
R. E. Samin ◽  
N. A. Azmi ◽  
M. A. Ahmad ◽  
M. R. Ghazali ◽  
M. A. Zawawi

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