The paper presents an improved adaptive iterative learning current control approach for interior permanent magnet synchronous motor (IPMSM) drives, which greatly improves performance in both dynamic and steady-state scenarios. The proposed method includes three control terms: feedback control terms, which stabilize state errors and get them closer to zero; iterative learning control terms, which enhance transient performance by updating the control command signals according to the recorded data (i.e., the preceding input and state errors) so they get closer to zero; and adaptive-terms, which compensate for parameter variations. The proposed method offers a robust dynamic/steady-state response due to the above three terms, when compared to the conventional non-adaptive ILC, i.e., it is sensitive to parameter variations. Simulation and experimental analysis confirm the efficacy of the proposed method using PSIM software tools and an IPMSM test-bed. The proposed method demonstrates improvements in terms of its transient response (i.e., fast settling time with a smaller overshoot) and steady-state response (i.e., less THD along with reduced current ripples) when compared with conventional methods.
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. 20182410105160 Demonstration and Development of ESS Solution Connected with Renewable Energy against with the weather condition of Middle East Region, No. 20206910100160).
