As the speed of permanent magnet motors increases during operation,the internal parameters gradually change due to magnetic circuit saturation,and inductance parameters significantly impact both steady-state and dynamic operational performance.Therefore,building upon the theoretical foundation of model reference adaptive control,this paper proposes an algorithm for identifying d-and q-axis inductances by integrating a neural network approach.The Mish function is employed as the activation function for the neural network algorithm.The identification results are then applied to motor vector control,with simulation results confirming the effectiveness of the activation function in improving convergence speed during parameter identification without disrupting normal motor system operation.
维普
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