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