Inverted Pendulum RBF Neural Network PID Controller Design

Author

Yu Jing ; Fang Jian

Author_Institution

Inst. of Electr. Eng., Jilin Teachers´ Inst. of Eng. & Technol., Changchun, China

fYear

2014

fDate

10-12 June 2014

Firstpage

560

Lastpage

562

Abstract

In view of the first-order inverted pendulum problem such as poor stability, large amount of overshoot, using RBF neural network to PID parameters self-tuning control system. This paper analyzes the RBF neural network tuning PID principles, and for an inverted pendulum system simulation. Simulation results show that the use of RBF neural network tuning PID controller adaptive ability, good quickness, good quality control.

Keywords

control system synthesis; neurocontrollers; nonlinear control systems; pendulums; radial basis function networks; self-adjusting systems; three-term control; PID parameters self-tuning control system; RBF neural network PID controller design; RBF neural network tuning; first-order inverted pendulum problem; inverted pendulum system simulation; Biological neural networks; Mathematical model; PD control; Radial basis function networks; Tuning; Inverted pendulum; Neural network;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer, Consumer and Control (IS3C), 2014 International Symposium on

Conference_Location

Taichung

Type

conf

DOI

10.1109/IS3C.2014.152

Filename

6845944

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=168089