Fırçasız doğru akım motorların adaptif filtre tabanlı MRAS ile hız algılayıcısız doğrudan moment kontrolü

Year 2025, Volume: 14 Issue: 2, 680 - 687, 15.04.2025

Canberk Tuzcu , Engin Cemal Mengüç , Rıdvan Demir , Remzi İnan

https://doi.org/10.28948/ngumuh.1639562

Abstract

Bu çalışma, kalıcı mıknatıslı fırçasız doğru akım motoru (SMFDAM) sürücüsünde gereken rotor hızını tahmin etmek için en küçük ortalama kare (Least mean square, LMS), en küçük ortalama kurtosis (LMK) ve en küçük ortalama dördüncü (Least mean fourth, LMF) yaklaşımlarına dayalı model referanslı adaptif sistem (Model reference adaptive system, MRAS) tahmin edicilerini tanıtmaktadır. Önerilen MRAS tahmin edicileri, referans modeli olarak hizmet eden ölçülen stator akımları ile adaptif modelin çıkışında üretilen stator akımları arasındaki hata terimini minimize ederek rotor hızını doğrudan kestirmektedir. Ayrıca, rotor hızını kapsayan ağırlık vektörleri üç tahmin edicide de her örnekleme adımında adaptif olarak güncellendiğinden, geleneksel MRAS çerçevelerinde yaygın olarak kullanılan sabit kazançlı orantılı-integral bir denetleyiciye olan ihtiyacı ortadan kaldırmaktadır. Önerilen tahmin edicilerin başarımları, zorlu çalışma senaryoları altında doğrudan moment kontrolü (DMK) tabanlı bir SMFDAM sürücüsü aracılığıyla değerlendirilmiştir. Benzetim sonuçları, önerilen kestiricilerin başarımlarının birbirlerine alternatif olduğunu göstermiştir. Özellikle, hız kestiriminde LMF tabanlı MRAS yapısı diğer MRAS yapılarından bir miktar daha iyi başarım sağlarken, 3-faz stator akım kestiriminde LMS ve LMK tabanlı MRAS yapıları daha iyi başarımlar sağlamıştır.

Keywords

SMFDAM, DMK, En küçük ortalama kare, En küçük ortalama kurtosis, En küçük ortalama dördüncü, Model referans adaptif sistem.

Speed sensorless direct torque control of brushless direct current motors with adaptive filter based MRAS

Abstract

This study introduces model reference adaptive system (MRAS) estimators based on least mean square (LMS), least mean kurtosis (LMK), and least mean fourth (LMF) approaches for estimating the rotor speed required in a permanent magnet brushless direct current motor (SMFDAM) drive. The proposed MRAS estimators directly estimate the rotor speed by minimizing the error term between the measured stator currents, serving as the reference model, and the stator currents generated at the adaptive model’s output. Additionally, since the weight vectors, which encapsulate rotor speed, are updated adaptively at each sampling step in all three estimators, the necessity for a fixed-gain proportional-integral controller commonly employed in conventional MRAS frameworks is removed. The performances of the proposed estimators are assessed through a direct torque control (DTC)-based SMFDAM drive under demanding operating scenarios. Simulation results show that the performances of the proposed estimators are alternative to each other. In particular, while the LMF-based MRAS structure performs slightly better than the other MRAS structures in speed estimation, the LMS and LMK-based MRAS structures provide better performances in 3-phase stator current estimation.

Keywords

PMBLDC, DTC, Least mean square, Least mean kurtosis, Least mean fourth, Model reference adaptive system.

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