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Wyświetlanie 1-2 z 2
Tytuł:
Efficiency analysis of parallel computing applied to auto-tuning of state feedback speed controller for PMSM drive
Autorzy:
Szczepański, Rafał
Tarczewski, Tomasz
Grzesiak, Lech M.
Powiązania:
https://bibliotekanauki.pl/articles/376463.pdf
Data publikacji:
2019
Wydawca:
Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej
Tematy:
parallel computing
Artificial Bee Colony
PMSM
state feedback
controller
MATLAB/Simulink
Opis:
Nowadays the simulation is inseparable part of researcher's work. Its computation time may significantly exceed the experiment time. On the other hand, multi-core processors are common in personal computers. These processors can be used to reduce computation time by using parallel computing on multiple cores. The most popular software applied to simulate behavior of the plant is MATLAB/Simulink. A single simulation of Simulink model cannot be computed by multiple cores, but there are many engineering problems, that require a multiple simulation of the same model with different parameters. In these problems, the parallel computing can be employed to decrease the overall simulation time. In this paper the parallel computing is used to speed-up the auto-tuning process of state feedback speed controller for PMSM drive. In order to obtain the optimal coefficients of the controller, an Artificial Bee Colony optimization algorithm is employed.
Źródło:
Poznan University of Technology Academic Journals. Electrical Engineering; 2019, 100; 145-156
1897-0737
Pojawia się w:
Poznan University of Technology Academic Journals. Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Artificial Neural Network-Based Gain-Scheduled State Feedback Speed Controller for Synchronous Reluctance Motor
Autorzy:
Tarczewski, Tomasz
Niewiara, Łukasz J.
Grzesiak, Lech M.
Powiązania:
https://bibliotekanauki.pl/articles/1956002.pdf
Data publikacji:
2021
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
synchronous reluctance motor
state feedback controller
gain scheduling
artificial neural network
robustness analysis
Opis:
This paper focuses on designing a gain-scheduled (G-S) state feedback controller (SFC) for synchronous reluctance motor (SynRM) speed control with non-linear inductance characteristics. The augmented model of the drive with additional state variables is introduced to assure precise control of selected state variables (i.e. angular speed and d-axis current). Optimal, non-constant coefficients of the controller are calculated using a linear-quadratic optimisation method. Non-constant coefficients are approximated using an artificial neural network (ANN) to assure superior accuracy and relatively low usage of resources during implementation. To the best of our knowledge, this is the first time when ANN-based gain-scheduled state feedback controller (G-S SFC) is applied for speed control of SynRM. Based on numerous simulation tests, including a comparison with a signum-based SFC, it is shown that the proposed solution assures good dynamical behaviour of SynRM drive and robustness against q-axis inductance, the moment of inertia and viscous friction fluctuations.
Źródło:
Power Electronics and Drives; 2021, 6, 41; 276-288
2451-0262
2543-4292
Pojawia się w:
Power Electronics and Drives
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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