DocumentCode :
1688678
Title :
Adaptive torque control using RBF neural networks for nonlinear DC chassis dynamometer drive
Author :
Duan, Qi-chang ; Zeng, Yong ; Duan, Pan ; Huang, Xiao-gang
Author_Institution :
Coll. of Autom., Chongqing Univ., Chongqing, China
fYear :
2010
Firstpage :
5093
Lastpage :
5098
Abstract :
For the DC chassis dynamometer, a nonlinear mathematical model was established based on the analysis of the transmission system of the DC dynamometer, and an adaptive controller based on RBF NN (radial basis function neural network) was proposed to control a dynamometer to load resistance intelligently to achieve stepless simulation of inertia. By using the Lyapunov synthesis approach, it was proved that the closed-loop system is uniformly ultimately bounded in the presence of bounded neural network approximation error and bounded disturbance force. Simulation results show that the developed controller can offer a good control performance.
Keywords :
DC motor drives; Lyapunov methods; adaptive control; closed loop systems; dynamometers; machine control; neurocontrollers; nonlinear control systems; radial basis function networks; torque control; RBF neural networks; adaptive torque control; bounded disturbance force; bounded neural network approximation error; closed-loop system; nonlinear DC chassis dynamometer drive; nonlinear mathematical model; radial basis function neural network; Artificial neural networks; Equations; Mathematical model; Motorcycles; Torque; Torque control; Trajectory; Adaptive control; DC chassis dynamometer; Nonlinear system; RBF Neural Networks; Resistance loading;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Control and Automation (WCICA), 2010 8th World Congress on
Conference_Location :
Jinan
Print_ISBN :
978-1-4244-6712-9
Type :
conf
DOI :
10.1109/WCICA.2010.5554490
Filename :
5554490
Link To Document :
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