Sensorless Control of Compressor Motor Considering Inverter Nonlinearities and Parameter Estimation
Energies, cilt.19, sa.10, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 19 Sayı: 10
- Basım Tarihi: 2026
- Doi Numarası: 10.3390/en19102374
- Dergi Adı: Energies
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Directory of Open Access Journals, Academic Search Ultimate (EBSCO), Engineering Source (EBSCO)
- Anahtar Kelimeler: adaptive-gain sliding mode observer, Back-EMF estimation, dead-time compensation, extended Kalman filter, frequency-adaptive complex-coefficient filter, parameter estimation, permanent magnet synchronous motor, phase-locked loop, recursive least squares, sensorless control
- Yıldız Teknik Üniversitesi Adresli: Evet
Özet
In this study, parameter estimation-assisted sensorless control methods are proposed for compressor motors. As sensorless control strategies, rotating high-frequency injection (RHFI), pulsating high-frequency injection (RHFI), and an adaptive-gain sliding mode observer (AG-SMO) are employed. During startup, HFI-based methods are utilized, whereas AG-SMO is activated under steady-state operating conditions. To mitigate parameter variations and inverter nonlinearities, Adaline Neural Network (ANN), Recursive Least Squares (RLS), and Extended Kalman Filter (EKF) algorithms are integrated for the real-time estimation of stator resistance and dead-time voltage. The proposed framework is validated through both simulation and experimental studies on a 30 W, 20 V interior permanent magnet motor commonly used in compressor applications. The results demonstrate that sensorless control algorithms alone provide robust operation, while the incorporation of parameter estimation effectively eliminates stability issues and ensures reliable transitions from low to high speeds. Comparative analysis reveals that ANN has a simple structure, RLS achieves faster convergence, and EKF provides smoother estimates under noisy conditions. Overall, the integration of sensorless control algorithms with ANN/RLS/EKF-based parameter estimation and dead-time compensation offers a cost-effective and reliable solution for high-performance compressor applications.