Hybrid Stepper Motor Electrical Model Extensions for Use in Intelligent Drives

The use of microcontrollers and digital signal processors for the standalone control of stepper motors is nowadays widespread. At the same time, more complex and intelligent algorithms are being used and developed for real-time control and observation. These algorithms often require a precise model of the motor, but the standard electrical model used for hybrid stepper motors is frequently not capable of giving an accurate enough representation of the real system. In this paper, new model extensions are proposed in order to have a more precise model of the motor, while maintaining a low computational cost. It is shown that a significant improvement in the model fit is achieved with these extensions, and their computational performance is compared to study the cost of model improvement versus computation cost. The applicability of the proposed model extensions is demonstrated via their use in an extended Kalman filter running in real time for closed-loop current control and mechanical state estimation.

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