Fuzzy logic based field orientation in an indirect FOC strategy of an induction actuator
Mathematics and Computers in Simulation - Special issue on modelling and simulation of electrical machines
Stochastic and neural models of an induction motor
Mathematics and Computers in Simulation - Special issue on modelling and simulation of electrical machines
Statistical efficiency of adaptive algorithms
Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
Neural network flux optimization using a model of losses in induction motor drives
Mathematics and Computers in Simulation
LMS learning algorithms: misconceptions and new results on converence
IEEE Transactions on Neural Networks
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Two new methods to identify the mechanical parameters in induction motor based field oriented drives are presented in this paper. The identified parameters are: the moment of inertia and the viscous damping coefficient. The proposed methods are based on the adaptive linear neuron (ADALINE) networks. The two parameters are derived and optimized during the online training process. During the identification phase, the motor torque is controlled by the well-known field oriented control strategy. This torque is subjected to variations in order to obtain mechanical speed transients. The two proposed methods are simple to implement compared to the previous techniques. They require only the stator current and mechanical speed measurements. Finally, the effectiveness of the two methods and the accuracy of the derived parameters are proven experimentally by two direct starting tests. The originality of this work is the building of a model representation that it is suitable for implementation with ADALINE networks. This leads to a simple implementation and ease of mechanical parameters identification.