Optimal Cost Function Parameter Design in Predictive Torque Control (PTC) Using Artificial Neural Networks (ANN)

The use of artificial neural networks (ANN) for the selection of the weighting factors in the cost function of the finite-set model predictive control (FS-MPC) algorithm can speed up selection without imposing additional computational burden to the algorithm and ensure that optimum weights are selected for the specific application. In this paper the design process of the weighting factors based on ANN is used for predictive torque control (PTC). In the design process the weighting factors in the cost function and the reference flux value are obtained for different fitness functions. The results show that different operating conditions of the drive will have new optimum parameters of the cost function, therefore sweeping parameters like load torque or reference speed can optimize the PTC for the whole operating conditions of the drive. A good match of the performance metrics predicted by the ANN and the simulation model is also observed. The experiments demonstrate that the selected cost function parameters can provide a fast drive start and good performance during different loading conditions and also in reversing of the drive.