RBF Neural Network-based PID controller design for the airborne satellite antenna servo system

Author(s):  
Xiaogang Gou ◽  
Yuzhan Wu
2014 ◽  
Vol 898 ◽  
pp. 755-758 ◽  
Author(s):  
Wei Li ◽  
Jian Fang

Establish the attitude model for self-designed mobile robot, According to the characteristics of nonlinear, unstable, using BP neural network method to achieve self-tuning PID parameters to make optimal parameters of the PID controller. Stabilization control of two-wheeled self-balanced robots at the same time, decrease the overshoot of the system and the number of shocks. Simulation experiments show that: Using BP neural network self-tuning PID controller improves system stability, effectiveness has been well controlled, with high practical value


2013 ◽  
Vol 756-759 ◽  
pp. 514-517
Author(s):  
Hao Xu ◽  
Jin Gang Lai ◽  
Zhen Hong Yu ◽  
Jiao Yu Liu

The technologic of PID control is very conventional. There is an extensive application in many fields at present. The PID controller is simple in structure, strong in robustness, and can be understood easily. Then neural networks have great capability in solving complex mathematical problems since they have been proven to approximate any continuous function as accurately as possible. Hence, it has received considerable attention in the field of process control. Due to the complication of modern industrial process and the increase of nonlinearity, time-varying and uncertainty of the practical production processes, the conventional PID controller can no longer meet our requirement. This paper introduces the theoretical foundation of the BP neural network and studying algorithm of the neural network briefly, and designs the PID temperature control system and simulation model based on BP neural network.


2012 ◽  
Vol 503-504 ◽  
pp. 1351-1356
Author(s):  
Deng Hua Li ◽  
Zhan Xian Chen ◽  
Shuang Zhai

For the problem of that the traditional brushless DC torque motor controller position servo system is difficult to track accurately the complex and varied location information, an improved double closed-loop controller is designed in this paper. A self-adaptive PID algorithm based on RBF neural network is employed to design position loop controller and a feed-forward control algorithm is used in the speed loop controller. The improved double closed-loop controller validated the feasibility of the new method for the design of brushless DC torque motor position servo system


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