Publication: Integration of pi and sliding mode control in two-phase stepper motors with adaptive weight adjustment.
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Date
2025-05
Authors
Song, Lixian
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Abstract
Hybrid stepper motors control displacement and speed by adjusting the number and frequency of input electrical pulses, offering benefits such as easy controllability, high positioning accuracy, and operational reliability. Additionally, compared to other servo motor types, they are advantageous in terms of lower cost. However, achieving enhanced control accuracy requires advanced drivers and controllers. Traditional open-loop control strategies often suffer from step losses and resonance phenomena. Closed-loop vector control methods are therefore adopted in scenarios demanding higher precision, and intelligent algorithms have also been increasingly integrated into the position and speed control loops of stepper motors. In this study, a compound control strategy is developed for two-phase hybrid stepper motors, integrating proportional-integral (PI) control and sliding mode control (SMC). The PI component primarily ensures precise tracking and reduces steady-state error, while the SMC component improves system robustness under disturbances. An adaptive weighting mechanism based on fuzzy logic is introduced, automatically adjusting the contributions of PI and SMC according to real-time operating conditions. The weight adjustment follows a smooth S-curve profile, which helps maintain stable transitions. The proposed method was evaluated using MATLAB/Simulink simulations for both speed and position control scenarios, comparing its performance with standalone PI and SMC methods. Simulation results indicate that the proposed compound controller achieves reduced overshoot and comparable steady-state error performance to PI control, while exhibiting response speeds similar to those of SMC control. Additionally, it demonstrates superior robustness, characterized by faster recovery and smaller deviations under load disturbances. Overall, the compound control provides a balanced trade-off between response speed, steady-state accuracy, and robustness, significantly improving performance without incurring the computational burden associated with fully intelligent control algorithms.