volume | PIER Journals

Abstract

To overcome bandwidth-accuracy trade-offs in sensorless surface-mounted permanent magnet synchronous motor (SPMSM) drives, this study addresses the limitations of traditional observers. Conventional first-order sliding-mode observer (SMO) inherently exhibits high-frequency chattering due to discontinuous switching functions. To smooth the estimated back-EMF, a low-pass filter (LPF) is typically appended as a remedy; however, this filtering induces speed-dependent phase lag during medium- and high-speed operations, whereas its impact at low speeds is negligible. Although conventional higher-order model-based SMO (HOSMO) can better attenuate chattering, a lower cutoff frequency inherently compromises dynamic tracking precision due to increased phase lag. To overcome these challenges, the proposed scheme substitutes filter-dependent laws with a second-order super-twisting algorithm (STA), ensuring continuity of the control signal and zero phase lag. Furthermore, a state-dependent adaptive tuning mechanism based on a continuous nonlinear barrier function was developed. This adaptive law dynamically boosts observer gains to suppress transient perturbations and contracts them at a steady state to minimize chattering. Comprehensive simulations and experimental verifications demonstrated that the proposed gain-adaptive super-twisting higher-order SMO (GAST-HOTSMO) significantly reduced harmonic distortion, restricted estimation errors, and accelerated dynamic recovery compared to conventional methods.

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Dr. Wenshuai Du

Professor Zhonggen Wang

Professor Wenyan Nie