Online Adaptive Current Vector Adjustment for Deep Flux-Weakening Control of IPMSM

Abstract

This article presents a novel online adaptive and accurate deep flux-weakening (FW) control for interior permanent magnet synchronous machine (IPMSM) drives. Based on the fundamental equations of IPMSM, the proposed online current vector adjustment in conjunction with the adaptive torque angle control loop contributes to improving model-based existing literature in two aspects. First, current vector control is constructed on the proposed flexible current vector recognition algorithm, which can be applied for motoring and generating modes. The optimum reference current vector for the FW region is analytically obtained using electrical speed as a feedback signal instead of using voltage feedback. Second, the proposed adaptive gain for the torque angle control loop is obtained using the small-signal approach to overcome the nonlinear effects in deep FW and improve dynamic performance. The effect of adaptive gain is compared using a bode diagram and an experimental test. Also, the stability of the proposed control scheme under parameter variations is maintained and shown by numerical and experimental analyses. The running of the IPMSM with the proposed control provides a smooth transition between regions, including maximum torque per ampere, FW, and deep FW applicable for EVs application. The experimental results confirm the performance of the proposed control strategy.