A Dual-Level Adaptive Law Design for Super-Twisting Algorithm in Sensorless IPMSM Drives
Qilian Lin, Ling Liu, Deliang Liang, Dongsong Jin, Shaofeng Jia, Han Song
Abstract
This paper proposes a dual-level adaptive law for third order super-twisting extended state observer (STESO) in a hybrid sensorless IPMSM drive. According to Lyapunov stability analysis, the coefficients of a super-twisting sliding-mode observer are often constrained by a lower limit to ensure robustness. However, when working in the wide-speed region, this constraint is too ambiguous to serve as a practical tuning guideline. A dual-level adaptive STESO is proposed using equivalent control and Lyapunov stability analysis to extend the operating range while simultaneously suppressing sliding-mode ripples. A rigorous stability analysis is presented, and a tuning guideline for guaranteeing the convergence of the whole algorithm is included. Besides, at low speeds, improvements have been made in torque ripple suppression and cross-coupling effect compensation, and their effectiveness has been validated both theoretically and practically. Several effective experimental results are presented to verify the feasibility of the proposed observer on the interior PMSM drive platform.