Title :
Tension identification of two-motor system based on neural network left-inverse
Author :
Zhennan Cai ; Guohai Liu ; Wenxiang Zhao ; Hao Zhang ; Yan Jiang ; Yaojie Mi
Author_Institution :
Sch. of Electr. & Inf. Eng., Jiangsu Univ., Zhenjiang, China
Abstract :
Tension detection is a key to improve performance of two-motor system under sensorless operation. This paper presents a new identification method for two-motor system based on artificial neural network and the left-inverse theory. Considering that the system parameters are time-variant and the mathematic model of left-inverse identification is complex, BP neural network is used to build the left-inverse model in this method, which is easy to implement. A simulation model of a two-motor system is developed. The simulated results verify the proposed method. By using this control strategy, the tension can be identified quickly and accurately, in which satisfactory robustness is offered.
Keywords :
MIMO systems; backpropagation; neurocontrollers; nonlinear control systems; sensorless machine control; synchronous motors; BP neural network; artificial neural network; left-inverse identification mathematic model; multiinput multioutput system; multimotor synchronous system; neural network left-inverse; nonlinear system; sensorless operation; strong coupling control; tension identification; time-variant system parameter; two-motor system; Artificial neural networks; Induction motors; Mathematical model; Rotors; Synchronous motors; Torque;
Conference_Titel :
Neural Networks (IJCNN), 2014 International Joint Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-6627-1
DOI :
10.1109/IJCNN.2014.6889688