Position Estimation of Stepping Motor Using Adaptive Gain Super Twisting Algorithm Sliding Mode Observer

This paper presents an Adaptive Gain Super Twisting Sliding Mode Observer (AGSTA-SMO) for a permanent magnet stepping motor as position. Since the proposed algorithm has a different structure with the Super Twisting Algorithm Sliding Mode Observer (STA-SMO), the AGSTA-SMO ensures a global, finite-time convergence even with the unknown, bounded perturbations/uncertainties. With the experimental validation, we show that the position estimation performance of AGSTA-SMO outperforms comparing to the position estimation result of STA-SMO.