Neural Generalized Predictive Controller for Induction Motor

In this paper the authors present a new advanced control algorithm for speed and flux tracking of an induction motor. This algorithm called: Neural Networks Generalized Predictive Control (NNGPC) uses a combination of Artificial Neural Networks (ANN) and Generalized Predictive Control technique (GPC). This later is traditionally used for systems characterised by a slow dynamic as in chemical process control. The NNGPC algorithm is based on the use of ANN as a nonlinear prediction model of the motor. This modeling technique is done by using the data from the system inputs/outputs information without requiring the knowledge about machine parameters. The outputs of the neural predictor are the future values of the controlled variables needed by the optimization procedure, which is achieved by minimizing a cost function with the reference control model using the Newton-Raphson optimization algorithm. The reference control model is carried out from an open loop control strategy of the induction motor. Simulation results show the effectiveness of the proposed control method.

[1]  Didier Dumur,et al.  Two cascaded nonlinear predictive controls of induction motor , 2003, Proceedings of 2003 IEEE Conference on Control Applications, 2003. CCA 2003..

[2]  Matthew W. Dunnigan,et al.  A new non‐linear sliding‐mode torque and flux control method for an induction machine incorporating a sliding‐mode flux observer , 2004 .

[3]  Didier Dumur,et al.  A new control strategy for induction motor based on non-linear predictive control and feedback linearization , 2000 .

[4]  Peter Vas,et al.  Artificial-Intelligence-Based Electrical Machines and Drives: Application of Fuzzy, Neural, Fuzzy-neural, and Genetic-Algorithm-based Techniques , 1999 .

[5]  Manfred Morari,et al.  Model predictive control: Theory and practice - A survey , 1989, Autom..

[6]  Ole Ravn,et al.  Implementation of neural network based non-linear predictive control , 1999, Neurocomputing.

[7]  E. Mendes,et al.  A new nonlinear multivariable control strategy of induction motors , 2000 .

[8]  D I Soloway,et al.  Neural Generalized Predictive Control: A Newton-Raphson Implementation , 1997 .

[9]  Ralph Kennel,et al.  Generalized predictive control (GPC)-ready for use in drive applications? , 2001, 2001 IEEE 32nd Annual Power Electronics Specialists Conference (IEEE Cat. No.01CH37230).

[10]  M. H. Shwehdi,et al.  A global ANN algorithm for induction motor based on optimal preview control theory , 2003, 2003 IEEE Bologna Power Tech Conference Proceedings,.

[11]  D. Naunin,et al.  Stochastic and neural models of an induction motor , 1998 .

[12]  J. A. Dente,et al.  AUTOMATIC INPUT/OUTPUT MODELING OF A SQUIRREL-CAGE INDUCTION MOTOR DRIVE SYSTEM USING NEURAL NETWORK , 1997 .

[13]  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.

[14]  John Chiasson,et al.  Dynamic feedback linearization of the induction motor , 1993, IEEE Trans. Autom. Control..

[15]  D. Neumerkel,et al.  Real-time application of neural model predictive control for an induction servo drive , 1994, 1994 Proceedings of IEEE International Conference on Control and Applications.

[16]  Wang Zhenlei,et al.  Identification and control of induction motor using artificial neural networks , 2001, ICEMS'2001. Proceedings of the Fifth International Conference on Electrical Machines and Systems (IEEE Cat. No.01EX501).

[17]  Dong-Hoon Lee,et al.  Neural Network-Based System Identification and Controller Synthesis for an Industrial Sewing Machine , 2004 .

[18]  Manfred Morari,et al.  Model predictive control: Theory and practice , 1988 .

[19]  Maurizio Cirrincione,et al.  An MRAS-based sensorless high-performance induction motor drive with a predictive adaptive model , 2005, IEEE Transactions on Industrial Electronics.