Neural network based estimation of feedback signals for a vector controlled induction motor drive

Neural networks are is recently showing good promise for application in power electronics and motion control systems. So far, they have been applied to a few cases, mainly in the control of power converters and drives, but their application in estimation is practically new. This paper explores the application of neural networks for estimation of feedback signals in induction motor drive systems. A feedforward neural network receives the machine terminal signals at the input and calculates flux, torque and unit vectors (cos θ_e and sin θ_e) at the output which are then used in the control of a direct vector-controlled drive system. The three-layer network has been trained extensively by the NeuralWorks Professional II/Plus program to emulate the DSP-based computational characteristics. The performance of the estimator is good and is comparable to that of DSP-based estimation. The drive system has been operated in wide torque and speed regions independently with a DSP-based estimator and a neural network-based estimator, and are shown to have comparable performance. The neural network estimator has the advantages of faster execution speed, harmonic ripple immunity and fault tolerance characteristics compared to the DSP-based estimator