Double-neuro-sliding mode position control for direct drive turning table servo system based on genetic algorithms

Author

Tang, Chuansheng ; Dai, Yuehong

Author_Institution

Sch. of Mechatron. Eng., Univ. of Electron. Sci. & Technol. of China, Chengdu, China

fYear

2011

fDate

15-17 July 2011

Firstpage

4912

Lastpage

4915

Abstract

Considering the parameters time variation, high nonlinear, disturbance and mechanical resonance of direct-drive turing table, a double neuro-sliding mode position control based on genetic algorithms is proposed. The turning table control system consists of sliding mode control and sliding swith control, the two control is used by two neural networks respectively, and use elastic BP algorithms and genetic algorithms(GA) to online optimize the weigh of the neural network. Simulation results demonstrate that the scheme can effectively achieve tracking performance and has strong robustness.

Keywords

backpropagation; genetic algorithms; neural nets; neurocontrollers; nonlinear control systems; position control; servomechanisms; time-varying systems; variable structure systems; direct drive turning table servo system; disturbance resonance; double-neuro-sliding mode position control; elastic BP algorithms; genetic algorithms; high nonlinear; mechanical resonance; neural networks; parameters time variation; sliding switch control; Artificial neural networks; Genetic algorithms; Observers; Sliding mode control; Sun; Turning; USA Councils; Direct-drive turning table; genetic algorithm; neural network; sliding mode control;

fLanguage

English

Publisher

ieee

Conference_Titel

Mechanic Automation and Control Engineering (MACE), 2011 Second International Conference on

Conference_Location

Hohhot

Print_ISBN

978-1-4244-9436-1

Type

conf

DOI

10.1109/MACE.2011.5988116

Filename

5988116