Decoupled stator-flux-oriented induction motor drive with fuzzy neural network uncertainty observer

A stator-flux-oriented induction motor drive using online rotor time-constant estimation with a robust speed controller is introduced in this paper. The estimation of the rotor time constant is made on the basis of the model reference adaptive system using an energy function. The estimated rotor time-constant is used in the current-decoupled controller, which is designed to decouple the torque and flux in the stator-flux-field-oriented control. Moreover, a robust speed controller, which is comprised of an integral-proportional speed controller and a fuzzy neural network uncertainty observer, is designed to increase the robustness of the speed control loop. The effectiveness of the proposed control scheme is demonstrated by simulation and experimental results.

[1]  P. Vas Vector control of AC machines , 1990 .

[2]  Myung Joong Youn,et al.  Robust digital position control of brushless DC motor with adaptive load torque observer , 1994 .

[3]  Yie-Chien Chen,et al.  A model reference control structure using a fuzzy neural network , 1995 .

[4]  A. Morris,et al.  Fuzzy neural networks for nonlinear systems modelling , 1995 .

[5]  Faa-Jeng Lin,et al.  Robust speed-controlled induction-motor drive using EKF and RLS estimators , 1996 .

[6]  Teresa Orlowska-Kowalska Application of extended Luenberger observer for flux and rotor time-constant estimation in induction motor drives , 1989 .

[7]  David J. Atkinson,et al.  Observers for induction motor state and parameter estimation , 1991 .

[8]  Yoshiki Uchikawa,et al.  On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm , 1992, IEEE Trans. Neural Networks.

[9]  C. Pan,et al.  Reduced-Order Parameter Estimation for Continuous Systems From Sampled Data , 1990 .

[10]  X. Xu,et al.  A stator flux oriented induction machine drive , 1988, PESC '88 Record., 19th Annual IEEE Power Electronics Specialists Conference.

[11]  A. S. Bharadwaj,et al.  A review of parameter sensitivity and adaptation in indirect vector controlled induction motor drive systems , 1990, 21st Annual IEEE Conference on Power Electronics Specialists.

[12]  Kouhei Ohnishi,et al.  Estimation, identification, and sensorless control in motion control system , 1994 .

[13]  Nobuyuki Matsui,et al.  Robust speed control of IM with torque feedforward control , 1991, Proceedings IECON '91: 1991 International Conference on Industrial Electronics, Control and Instrumentation.

[14]  Bimal K. Bose,et al.  Power electronics and motion control-technology status and recent trends , 1992, PESC '92 Record. 23rd Annual IEEE Power Electronics Specialists Conference.

[15]  Ching-Cheng Teng,et al.  Implementation of a fuzzy inference system using a normalized fuzzy neural network , 1995, Fuzzy Sets Syst..

[16]  Tadashi Fukao,et al.  Robust vector control of induction motor without using stator and rotor circuit time constants , 1993, Conference Record of the 1993 IEEE Industry Applications Conference Twenty-Eighth IAS Annual Meeting.

[17]  A. Ismael,et al.  Fuzzy neural network implementation of self tuning PID control systems , 1994, Proceedings of 1994 9th IEEE International Symposium on Intelligent Control.

[18]  C. C. Chan,et al.  An effective method for rotor resistance identification for high-performance induction motor vector control , 1990 .

[19]  Makoto Iwasaki,et al.  Robust speed control of IM with torque feedforward control , 1993, IEEE Trans. Ind. Electron..

[20]  Chuen-Tsai Sun,et al.  Neuro-fuzzy modeling and control , 1995, Proc. IEEE.

[21]  Chin-Teng Lin,et al.  Neural-Network-Based Fuzzy Logic Control and Decision System , 1991, IEEE Trans. Computers.

[22]  Thomas A. Lipo,et al.  An extended Kalman filter approach to rotor time constant measurement in PWM induction motor drives , 1992 .