Regular Article

Online intelligent parameter and speed estimation of permanent magnet synchronous motors using bacterial foraging optimization

¹ Department of Electrical Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
² University of Gabes, National Engineering School of Gabes, 6072, Tunisia
³ Centre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, 140401, Punjab, India
⁴ Applied Science Research Center, Applied Science Private University, Amman, 11931, Jordan
⁵ Jadara University Research Center, Jadara University, Irbid, Jordan
⁶ College of Engineering and Technology, University of Doha for Science and Technology, Doha, Qatar

^* Corresponding author: aymen.flah@enig.u-gabes.tn

Received: 31 January 2025
Accepted: 27 February 2025

Abstract

Accurate estimation of the parameters and speed of Permanent Magnet Synchronous Motors (PMSMs) is crucial for achieving optimal performance in control applications. Traditional methods, such as the Model Reference Adaptive System (MRAS) rely on manually tuned Proportional-Integral (PI) controllers, leading to suboptimal results due to fixed tuning parameters that do not adapt to varying operating conditions. This limitation affects the precision of parameter identification, leading to potential inefficiencies in motor control. This paper proposes an intelligent online estimation method that leverages Popov hyperstability theory and the Bacterial Foraging Optimization (BFO) algorithm to address this issue. The proposed approach simultaneously estimates three key PMSM parameters – stator resistance, inductance, and permanent magnet flux – along with the actual motor speed. Unlike conventional methods, an online BFO-based tuning algorithm is integrated into the MRAS framework, allowing adaptive and optimal adjustment of controller parameters in real time. Extensive practical evaluations demonstrate that the proposed method significantly improves estimation accuracy and adaptability compared to traditional approaches. The results confirm its effectiveness in enhancing PMSM control performance, making it a promising solution for high-precision motor applications. Experimental results demonstrate a 12% improvement in estimation precision compared to traditional manual tuning methods.