Optimization-Method-Inspired Update Scheme for Neural Network Assisted Control Tuning of PMSMs
Zhenxiao Yin, Hang Zhao, Pei Lu, Yang Shen · 2023
The derivative of the control increment concerning the output of the neural network (NN) stands as a pivotal factor within the NN-assisted control tuning approach for permanent magnet synchronous motors (PMSMs). However, the significance of selecting an appropriate formula lacks explanation. Through an analysis of gradient-based optimization methods, the underlying principle behind the update of control increments becomes evident. Consequently, the design of the control tuning scheme incorporates gradient descent with and without momentum, as well as accelerated momentum. These techniques are applied to formulate the update law for the backpropagation neural network (BPNN) assisted control. Subsequently, these devised methodologies are integrated into the BPNN framework for the purpose of comparison. Finally, the results are validated in experiments.