Optimized Robust Observer-Based Encoderless Control for SPMSM

Gradient-based flux observers are promising for encoderless control of surface-permanent magnet synchronous motors (SPMSMs) for their computational efficiency and adaptability, yet suffer from noise sensitivity and gain tuning. To this end, this article proposes a robust optimized flux observer combining the huber loss function (HLF) and root mean square propagation (RMSProp) algorithm to overcome these limitations. First, a robust nonlinear flux observer is developed with the HLF to enhance the steady state performance of the encoderless control by leveraging its smoothness and noise suppression capabilities. Second, the RMSProp algorithm is employed to optimize the gradient search process, dynamically adjusting the step size in real-time based on historical gradient information to ensure improved dynamic response. Additionally, the Lyapunov stability criterion is utilized to analyze the stability of the proposed controller and prove the bounds of its observation error. Finally, experimental results confirm that the proposed control strategy achieves high-precision position estimation. Angular estimation errors under no-load and load conditions are reduced to below −0.09 rad and −0.08 rad, respectively, compared to −0.22 rad and −0.15 rad with conventional controller, due to flexible gain tuning and strong noise robustness.