Improved Iterative Learning Control Strategy for Surface-Mounted Permanent Magnet Synchronous Motor Drives

In this article, an improved iterative learning control (ILC), which significantly enhances the speed tracking performance in the transient and steady state for a surface-mounted permanent magnet synchronous motor (SPMSM) drive, is proposed. The proposed ILC encapsulates two control terms: the feedback linearization control terms force the speed error to approach zero, and the iterative learning control terms (ILCTs) improve the performance of the control inputs based on the stored data such as previous speed error and previous control input. Unlike the conventional feedback linearization control (FLC), the proposed ILC does not require the exact information of the SPMSM parameters, significantly rejects the periodic and nonperiodic disturbances, and efficiently provides an enhanced speed tracking performance owing to the included ILCTs. Besides, its stability is proven by showing that the speed tracking error asymptotically goes to zero. In comparative studies, the proposed method offers the better transient performance (e.g., faster transient response and smaller overshoot) and steady-state performance (i.e., smaller steady-state error) than the conventional FLC under load and speed step changes. To prove the practicability of the proposed scheme, the proposed ILC is simulated and implemented on a MATLAB/Simulink software and a prototype SPMSM test-bed using TI TMS320F28335 digital signal processor, respectively.