Modified Euler integration based control of a five-phase induction motor drive

In principle, control methods for multi-phase machines are the same as for three-phase machines. Variable speed induction motor drives without mechanical speed sensors at the motor shaft have the attractions of low cost and high reliability. To replace the speed sensor, information of the rotor speed is extracted from measured stator currents and voltages at motor terminals. Vector-controlled drives require estimating the magnitude and spatial orientation of the fundamental magnetic flux in the stator or in the rotor. Open-loop estimators or closed-loop observers are used for this purpose. They differ with respect to accuracy, robustness, and sensitivity against model parameter variations. This paper analyses operation of a modified Euler integration-
based sensorless control of vector controlled five-phase induction machine with current control in the stationary reference frame. A linear neural network has been then designed and trained online by means of back propagation network (BPN) algorithm, differently from that in the literature which employs a nonlinear back propagation network (BPN) algorithm. The Artificial Neural Network (ANN)-Model Reference Adaptive System (MRAS) based sensorless operation of a three-phase induction machine is well established and the same principle is extended in this paper for a five-phase induction machine. Performance, obtainable with hysteresis current control, is illustrated for a number of operating conditions on the basis of simulation results. Full decoupling of rotor flux control and torque control is realised. Dynamics, achievable with a five-phase vector controlled induction machine, are shown to be essentially identical to those obtainable with a three-phase induction machine.