The Decoupling Control of Induction Motor Based on Artificial Neural Network Inverse System Method

The induction motor is multi-variable, nonlinear and strong-coupled system. Due to parameters' variation during operation of induction motor, the decoupling and linearization implemented by field oriented control(FOC) and analytical inverse control(ANIC) is destroyed. For that, a novel linearization and decoupling method named as artificial neural network(ANN) inverse for induction motor control is proposed. It is characterized by that the construction of the ANN inverse is independent of the motor model and parameters. Cascading the ANN inverse which consists of a static ANN and four integrators with the motor, the multivariable, nonlinear and strongly coupled system is decoupled into two independent second-order linear subsystems, or rotor speed subsystem and rotor flux one, so as to be easy to design the closed-loop linear regulator to control each of the subsystems. Simulation and primary experiment results show that the good static and dynamic decoupling performance and the strong robustness to both variation of parameters and load torque disturbance can be achieved by using the proposed method.

[1]  Bimal K. Bose,et al.  Neural Network Applications in Power Electronics and Motor Drives—An Introduction and Perspective , 2007, IEEE Transactions on Industrial Electronics.

[2]  P.F.A. MacConnell,et al.  Nonlinear adaptive state-feedback speed control of a voltage-fed induction motor with varying parameters , 2006, IEEE Transactions on Industry Applications.

[3]  Romeo Ortega,et al.  Comparison of two nonlinear controllers for current-fed induction motors , 1997, 1997 European Control Conference (ECC).

[4]  P. Sicard,et al.  Field-oriented control of induction motors using neural-network decouplers , 1997 .

[5]  A. Chu,et al.  A novel neural network controller and its efficient DSP implementation for vector controlled induction motor drives , 2002 .

[6]  T.G. Habetler,et al.  High-performance induction motor speed control using exact feedback linearization with state and state derivative feedback , 2003, IEEE Transactions on Power Electronics.

[7]  Riccardo Marino,et al.  Global adaptive output feedback control of induction motors with uncertain rotor resistance , 1996, Proceedings of 35th IEEE Conference on Decision and Control.

[8]  Frank L. Lewis,et al.  Robust backstepping control of induction motors using neural networks , 2000, IEEE Trans. Neural Networks Learn. Syst..

[9]  T. G. Habetler,et al.  High-performance induction motor speed control using exact feedback linearization with state and state derivative feedback , 2004 .

[10]  Zhang Hao,et al.  Dynamic decoupling control of bearingless switched reluctance motors based on neural network inverse system , 2005, 2005 International Conference on Electrical Machines and Systems.

[11]  Frank L. Lewis,et al.  Neural net robot controller with guaranteed tracking performance , 1993, Proceedings of 8th IEEE International Symposium on Intelligent Control.