Adaptive composite control combining ONB-LSSVM inverse model with PID control

2011 ◽  
Vol 25 (3) ◽  
pp. 233-239
Author(s):  
Xinran Zhou ◽  
Zhaosheng Teng ◽  
Xingjun Jiang
Keyword(s):  
Pid Control ◽  
Inverse Model ◽  
Composite Control ◽  
Adaptive Composite

Backlash Robotic Systems Compensation by Inverse Model-Based PID Control

2019 ◽  
Author(s):  
Fernando da Fonseca Schneider ◽  
Cesar Bastos da Silva ◽  
Paulo Jefferson Dias de Oliveira Evald ◽  
Rodrigo Sousa e Silva ◽  
Rodrigo Zelir Azzolin
Keyword(s):  
Pid Control ◽  
Inverse Model ◽  
Robotic Systems ◽  
Model Based

Study on Fuzzy-PID Control Simulation of Diesel Engines Electronic Governor

2013 ◽  
Vol 331 ◽  
pp. 263-266
Author(s):  
Yan Qiang Li ◽  
Zhi Gang Yao ◽  
You Lin Xu
Keyword(s):  
Pid Control ◽  
Engine Speed ◽  
Control Methods ◽  
Fuzzy Pid ◽  
Control Effect ◽  
Fuzzy Pid Control ◽  
Parallel Connection ◽  
Composite Control ◽  
Speed Regulation ◽  
Better Than

In this paper, two types Fuzzy-PID composite control methods were studied. The Fuzzy-PID alternately control and parallel connection control were simulated by MATLAB SIMULINK. The simulation conclusions were that Fuzzy-PID composite control effect was better than FUZZY control or PID control, and Fuzzy-PID parallel connection control effect was better than alternately control on diesel engine speed regulation.

Adaptive Composite Control for Jitter Rejection of Satellite in Optical Communications

2014 ◽  
Vol 51 (4) ◽  
pp. 040606
Author(s):  
覃浩 Qin Hao ◽  
付承毓 Fu Chengyu ◽  
黄永梅 Huang Yongmei
Keyword(s):  
Optical Communications ◽  
Composite Control ◽  
Adaptive Composite

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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