Adaptive linear neuron-based encoder measurement error compensation in vector control of two-phase stepping motors

This paper proposes an encoder measurement error compensation method using an adaptive linear neuron, a type of artificial neural network, for use in the vector control of two-phase stepping motors. Stepping motors can have an asymmetric structure due to an eccentricity that occurs in the manufacturing and assembly process. When performing vector control with a flux angle measured by an incremental encoder attached to stepping motors with eccentricity, the following two kinds of encoder measurement errors occur. First, an offset position error occurs during the forced excitation process for the initial rotor position alignment. Second, sinusoidal position and speed errors with a frequency equal to the mechanical speed occur. In this study, how the eccentricity causes offset and sinusoidal measurement errors in addition to the phenomena caused by these encoder measurement errors are analyzed. From these analyses, an encoder error compensation method based on an adaptive linear neuron is proposed. The validity of the proposed encoder errors compensation methods is verified through experiments on a two-phase stepping motor drive system.