Position Control Based on Add-on-Type Iterative Learning Control with Nonlinear Controller for Permanent-Magnet Stepper Motors

In this paper, a current-error-based iterative learning controller (ILC) with a nonlinear controller is proposed to improve the position-tracking performance in permanent-magnet (PM) stepper motors. Our proposed method comprises a current-error-based ILC for mechanical dynamics and a nonlinear controller for current dynamics. A nonlinear controller using a variable structure is designed to obtain the field-oriented control. This nonlinear controller can cause the PM stepper motor become a single-input single-output linear system after finite time. The add-on-type ILC with proportional–integral control is designed to improve the position-tracking performance as the systems repeatedly perform the same operation. To increase the rate of error convergence, the current-error-based ILC is designed using the plant inversion method. The condition that the error converges to zero is mathematically derived. Thus, the proposed method can reduce the position-tracking error as the systems repeatedly perform the same operation. Furthermore, the proposed method can be easily plugged into the pre-designed controller. The performance of our proposed method was evaluated via simulations. In simulations, it is observed that the proposed method reduces the position-tracking error compared to the previous methods.