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A Torque Estimation Scheme for Induction Motor Based on GRU and A Two-step Training Process

This paper presents a torque estimation scheme for induction motors based on gated recurrent unit (GRU) and a two-step training process. GRU uses recurrent neural network (RNN) structure to learn the relationship between the excitation and response sequences of the motor, so as to have the ability to adapt to the changes of parameters and working state. Sliding window mode is used to calculate GRU to avoid non convergence of parameters during training. Also, a two-step training process is proposed, that is, pretraining the model to obtain a suboptimal network in simulation, and then fine-tuning the network by the experiments of acceleration presented in this paper. In the end, the results of simulation and some experiments verify the robustness and practicability of the algorithm.