This research work introduces an enhanced sensorless-based iterative learning control (ILC) method to elevate the steady-state and dynamic performance of permanent magnet synchronous motors (PMSMs). Unlike traditional sensorless control methods, the proposed method combines feedback control terms and ILC terms to minimize state errors and improve dynamic performance by utilizing recorded data from previous iterations. Precise knowledge of the PMSM parameters is not required, making the control approach less sensitive to system perturbation. Comparative simulation analyses using the PSIM simulation tool are carried out to validate the practicability of the proposed control approach both in transient and steady-state conditions.
This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20225500000110).
