PUBLICATIONS

 The purpose of this paper is to present an improved adaptive iterative learning current control approach for interior permanent magnet synchronous motor (IPMSM) drives, which greatly upgrade the performance in both dynamic and steady state scenarios. In the proposed approach, there are three  types of control terms: feedback control terms, which stabilize state errors and get them closer to zero, as well as iterative learning control terms, which enhance transient performance by updating the control commend signals according to the recorded data (i.e., preceding input along with current speed and errors) so that they get closer to zero, and adaptive-terms, which compensate for parameter variations. The proposed method demonstrates a robust dynamic and steady-state response owing to the incorporation of the three terms stated above, unlike the conventional non-adaptive ILC, which is sensitive to parameter variations. The effectiveness of the proposed approach is verified via simulations and an experimental analysis conducted, respectively, using a PSIM software tool and an IPMSM test-bed, which employs a TMS320F28335-DSP. The proposed method demonstrates more improved transient response (i.e., fast settling time with smaller overshoot) and steady-state resoponse (i.e., less THD along with reduced current ripples) compared to conventional methods.