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Anton A. Pyrkin
Researcher at Saint Petersburg State University of Information Technologies, Mechanics and Optics
Publications - 213
Citations - 2424
Anton A. Pyrkin is an academic researcher from Saint Petersburg State University of Information Technologies, Mechanics and Optics. The author has contributed to research in topics: Nonlinear system & Adaptive control. The author has an hindex of 24, co-authored 190 publications receiving 1882 citations. Previous affiliations of Anton A. Pyrkin include Saint Petersburg State University & Hangzhou Dianzi University.
Papers
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Journal ArticleDOI
Performance Enhancement of Parameter Estimators via Dynamic Regressor Extension and Mixing
TL;DR: A new procedure to design parameter estimators with enhanced performance is proposed, which yields a new parameter estimator whose convergence is established without the usual requirement of regressor persistency of excitation.
Journal ArticleDOI
A robust globally convergent position observer for the permanent magnet synchronous motor
Alexey A. Bobtsov,Anton A. Pyrkin,Romeo Ortega,Slobodan N. Vukosavic,Aleksandar M. Stankovic,Elena Panteley +5 more
TL;DR: A new robust, nonlinear, globally convergent position observer for surface-mount permanent magnet synchronous motors is proposed and outperforms other existing designs from the point of view of robustness and convergence rate.
Proceedings ArticleDOI
Rejection of sinusoidal disturbance of unknown frequency for linear system with input delay
TL;DR: In this paper, a new approach for rejection of a sinusoidal disturbance of unknown frequency, bias, amplitude, and phase for a linear unstable plant with a delay in the control channel is presented.
Journal ArticleDOI
A parameter estimation approach to state observation of nonlinear systems
Romeo Ortega,Alexey A. Bobtsov,Alexey A. Bobtsov,Anton A. Pyrkin,Anton A. Pyrkin,Stanislav Aranovskiy,Stanislav Aranovskiy +6 more
TL;DR: The proposed approach is shown to be applicable to position estimation of a class of electromechanical systems and to two assumptions related to solvability of a partial differential equation and the ability to estimate the unknown parameters.
Journal ArticleDOI
Robust Adaptive Sensorless Control for Permanent-Magnet Synchronous Motors
TL;DR: This method has a robust property against dc bias errors, i.e., it cures the inherent weakness of the pure integrator (flux observer) to dc offsets that frequently occur in current measurements and voltage estimates.