scholarly journals Neural Network Composite Control of a Hydraulic Servo Motor System by Means of Smoothed Selection of Velocity and Position Compensators

2006 ◽  
Vol 72 (4) ◽  
pp. 475-480
Author(s):  
Hiroshi KATOH ◽  
Takao NISHIUMI ◽  
Takashi HARAGUCHI
Keyword(s):  
Neural Network ◽  
Motor System ◽  
Servo Motor ◽  
Composite Control ◽  
Hydraulic Servo ◽  
Selection Of

Research on the Adaptive Zero Phase Error Tracking and Proportion Composite Controller of Continuous Rotary Electro-Hydraulic Servo Motor

2014 ◽  
Vol 620 ◽  
pp. 443-448
Author(s):  
Xiao Jing Wang ◽  
Gui Tao Sun ◽  
Chang Fu Xian ◽  
Song Li ◽  
Jian Cao ◽  
...  
Keyword(s):  
Phase Error ◽  
Mathematic Model ◽  
Servo Motor ◽  
Tracking Accuracy ◽  
Composite Control ◽  
Hydraulic Servo ◽  
Working Principle ◽  
Periodic Signals ◽  
Rotary Motor ◽  
Zero Phase

According to the poor tracking accuracy of periodic signals for continuous rotary electro-hydraulic servo motor, the mathematic model of the electro-hydraulic position servo system of the continuous rotary motor was established, a composite control strategy combining with proportion controller and adaptive zero phase error tracking controller was designed. The working principle of composite strategy was given, and the composite strategy was verified on Co-simulation of MATLAB and AMESim platform. The simulation result shows that the amplitude attenuation and phase delay of the tracking curve which excitated by the sinusoidal signal 0.5degree and 10Hz for continuous rotary motor is less than 1.2% and 1.08°respectively, and the efficiency of the composite controller is verified through simulation.

DC Servo Motor Control Research Based on BP Neural Network PID

2014 ◽  
Vol 926-930 ◽  
pp. 1230-1234
Author(s):  
Yuan Xie
Keyword(s):  
Neural Network ◽  
Motor Control ◽  
Steady State ◽  
Bp Neural Network ◽  
Pid Control ◽  
Pid Controller ◽  
Servo Motor ◽  
Composite Control ◽  
Control Research ◽  
Neural Network Pid

This paper introduced the structure and principle of DC servo motor, , derived its transfer function; combined BP neural network with PID control to achieve a composite control arithmetic, that fully embodies advantages of strong self-adaptive, robustness of neural network and high steady-state accuracy of PID controller, which improved the performance of DC motor servo system.

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