Inverted Pendulum RBF Neural Network PID Controller Design

2014 ◽  
Author(s):  
Yu Jing ◽  
Fang Jian
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Inverted Pendulum ◽  
Controller Design ◽  
Rbf Neural Network ◽  
Pid Controller Design ◽  
Neural Network Pid

scholarly journals Based on Neural Network PID Controller Design and Simulation

2013 ◽  
Vol 756-759 ◽  
pp. 514-517
Author(s):  
Hao Xu ◽  
Jin Gang Lai ◽  
Zhen Hong Yu ◽  
Jiao Yu Liu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Pid Controller ◽  
Controller Design ◽  
Industrial Process ◽  
Temperature Control System ◽  
The Neural Network ◽  
Mathematical Problems ◽  
Pid Controller Design ◽  
Neural Network Pid

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.

BP Neural Network for Mobile Robot Self-Tuning PID Controller Design

2014 ◽  
Vol 898 ◽  
pp. 755-758 ◽  
Author(s):  
Wei Li ◽  
Jian Fang
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Bp Neural Network ◽  
Pid Controller ◽  
Controller Design ◽  
System Stability ◽  
Stabilization Control ◽  
Pid Controller Design ◽  
Self Tuning ◽  
Attitude Model

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

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