Smooth Speed Control of Permanent Magnet Synchronous Machine Using Back Propagation Neural Network

Abstract

Torque ripple is one of the most critical problems in PMSM system. In this paper, a neural network (NN) torque compensator is combined with a conventional extended state observer (ESO)-based active disturbance rejection controller (ADRC) system to suppress the torque ripple at wide machine operation speed range by generating the optimal current reference. The ESO is able to estimate and reject the low-frequency component in the torque ripple, while the remaining disturbances can be learned and compensated by the neural network. Compared with commonly used schemes, the proposed method does not need to analyze the influence of various sources of the torque ripple, such as the cogging torque, non-sinusoidal back-EMF, parameter variations, and unmodeled disturbances. In addition, the simple structure of the neural network helps reduce the computation time and save computer memory. The effectiveness of the proposed neural network compensator with both the rotor position and mechanical angular velocity as inputs is verified in the experiment under different operation speeds.