Motor control method using single-sensor phase current reconstruction

Abstract

This work aims to address the current sensing issue in a three-phase bridge inverter circuit and discuss a motor control method based on single-sensor phase current reconstruction. By collecting the motor's current signals and utilizing signal processing techniques such as Fourier transform and wavelet transform, information about the three-phase currents is extracted from the data of a single sensor. Simultaneously, optimization algorithms like neural networks are employed to learn from historical data to predict and estimate the current values of the three phases. Software tools such as MATLAB and LabVIEW are used for data processing and analysis in the implementation process. An experimental platform is set up to verify the accuracy and real-time performance of the reconstruction method. The experimental results indicate that employing the Mixed Space Vector Pulse Width Modulation (MSVPWM) control strategy reduces the reconstruction error from the original e = 3.5 % to e = 3.1 %. The current transition is smooth throughout the vector plane, and even in unobservable regions, the phase current can be accurately reconstructed. The motor control method based on single-sensor phase current reconstruction exhibits high accuracy and real-time performance, meeting practical requirements for motor control. In conclusion, this work provides technical support and a theoretical basis for the precise control of motors.