Ultrasonic motor servo-drive with online trained neural-network model-following controller

被引:28
作者
Lin, FJ [1 ]
Hwang, WJ [1 ]
Wai, RJ [1 ]
机构
[1] Chung Yuan Christian Univ, Dept Elect Engn, Chungli 32023, Taiwan
来源
IEE PROCEEDINGS-ELECTRIC POWER APPLICATIONS | 1998年 / 145卷 / 02期
关键词
ultrasonic motor drive; neural-network control;
D O I
10.1049/ip-epa:19981727
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
An ultrasonic motor (USM) servo-drive with an online trained neural-network model-following controller is proposed. First, the driving circuit for the USM, which is a two-phase chopper-inverter combination, is introduced. Since the dynamic characteristics of the USM are difficult to obtain and the motor parameters are time varying, an online trained neural-network model-following controller is proposed to control the rotor position of the USM. An accurate tracking response can be obtained by random initialisation of the weights and biases of the network owing to the powerful online learning capability. Moreover, the influences of parameter variations and external disturbances of the USM servo-drive can be effectively reduced by the neural-network controller.
引用
收藏
页码:105 / 110
页数:6
相关论文
共 12 条
[1]  
[Anonymous], 1993, ENGINEERINGSCIENCE E
[2]   THEORY AND APPLICATIONS OF NEURAL NETWORKS FOR INDUSTRIAL CONTROL-SYSTEMS [J].
FUKUDA, T ;
SHIBATA, T .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 1992, 39 (06) :472-489
[3]  
FURUYA S, 1990, IEEE POWER ELECTRON, P17, DOI 10.1109/PESC.1990.131167
[4]  
IZUNO Y, 1994, IEEE PESC REC, P1237
[5]  
LIAW CM, 1994, IEEE T IND ELECTRON, V241, P308
[6]   Fuzzy adaptive model-following position control for ultrasonic motor [J].
Lin, FJ .
IEEE TRANSACTIONS ON POWER ELECTRONICS, 1997, 12 (02) :261-268
[7]   Driving circuit for ultrasonic motor servo drive with variable-structure adaptive model-following control [J].
Lin, FJ ;
Kuo, LC .
IEE PROCEEDINGS-ELECTRIC POWER APPLICATIONS, 1997, 144 (03) :199-206
[8]  
MAHAN N, 1989, POWER ELECT CONVERTE
[9]  
Narendra K S, 1990, IEEE Trans Neural Netw, V1, P4, DOI 10.1109/72.80202
[10]  
Psaltis D., 1988, IEEE Control Systems Magazine, V8, P17, DOI 10.1109/37.1868