- 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