Title :
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
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;
Conference_Titel :
Mechanic Automation and Control Engineering (MACE), 2011 Second International Conference on
Conference_Location :
Hohhot
Print_ISBN :
978-1-4244-9436-1
DOI :
10.1109/MACE.2011.5988116
