Online training of parallel neural network estimators for control of induction motors

This paper presents an adaptive parallel control architecture, using an artificial neural network (ANN) which is trained while the controller is operating online. The proposed control structure incorporates five-multilayer feedforward ANNs, which are online trained using the Marquardt-Levenberg least-squares learning algorithm. The five networks are used exclusively for system estimation. The estimation mechanism uses online training to learn the unknown model dynamics and estimate the rotor fluxes of an inverter-fed induction motor. Subsequently, the estimated stator currents are fed into an adaptive controller to track the desired stator current trajectories. The adaptive controller is constructed as a feedback signal (a nonlinear control law), depending on estimated stator currents supplied by the neural estimators and the desired reference trajectories to be tracked by the output. The control of the direct and quadrature components of the stator current successfully tracked a wide variety of reference trajectories after relatively short online training periods.

[1]  T. G. Habetler,et al.  Identification and control of induction motor stator currents using fast on-line random training of a neural network , 1995, IAS '95. Conference Record of the 1995 IEEE Industry Applications Conference Thirtieth IAS Annual Meeting.

[2]  Stefen Hui,et al.  Application of feedforward neural networks to dynamical system identification and control , 1993, IEEE Trans. Control. Syst. Technol..

[3]  Bernard Widrow,et al.  Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[4]  Chun-Chieh Wang,et al.  A rotor-flux-observer-based composite adaptive speed controller for an induction motor , 1997 .

[5]  J. Theocharis,et al.  Neural network observer for induction motor control , 1994, IEEE Control Systems.

[6]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[7]  B. Burton,et al.  Linear speed-up parallel implementation of continually online trained neural networks for identification and control of fast processes [induction motor control] , 1996, IAS '96. Conference Record of the 1996 IEEE Industry Applications Conference Thirty-First IAS Annual Meeting.

[8]  Ahmed Rubaai,et al.  A continually online-trained neural network controller for brushless DC motor drives , 2000 .

[9]  B. Burton,et al.  Reducing the computational demands of continually online trained artificial neural networks for system identification and control of fast processes , 1994, Proceedings of 1994 IEEE Industry Applications Society Annual Meeting.

[10]  I. J. Leontaritis,et al.  Input-output parametric models for non-linear systems Part II: stochastic non-linear systems , 1985 .

[11]  Stefanos Kollias,et al.  An adaptive least squares algorithm for the efficient training of artificial neural networks , 1989 .

[12]  Ronald G. Harley,et al.  Identification and control of induction machines using artificial neural networks , 1993 .

[13]  M. Agarwal.  A systematic classification of neural-network-based control , 1997 .

[14]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.