Neural Generalized Predictive Controller for Induction Motor

Abstract

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