Simplified Sensorless Current Predictive Control of Synchronous Reluctance Motor Using Online Parameter Estimation

Ahmed Farhan, Mohamed Abdelrahem, Amr Saleh, Adel Shaltout and Ralph Kennel
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Ahmed Farhan: Institute for Electrical Drive Systems and Power Electronics, Technical University of Munich (TUM), 80333 München, Germany
Mohamed Abdelrahem: Institute for Electrical Drive Systems and Power Electronics, Technical University of Munich (TUM), 80333 München, Germany
Amr Saleh: Electrical Engineering Department, Faculty of Engineering, Fayoum University, Fayoum 63514, Egypt
Adel Shaltout: Electrical Engineering Department, Faculty of Engineering, Cairo University, Cairo 12613, Egypt
Ralph Kennel: Institute for Electrical Drive Systems and Power Electronics, Technical University of Munich (TUM), 80333 München, Germany

Energies, 2020, vol. 13, issue 2, 1-18

Abstract: In this paper, a simplified efficient method for sensorless finite set current predictive control (FSCPC) for synchronous reluctance motor (SynRM) based on extended Kalman filter (EKF) is proposed. The proposed FSCPC is based on reducing the computation burden of the conventional FSCPC by using the commanded reference currents to directly calculate the reference voltage vector (RVV). Therefore, the cost function is calculated for only three times and the necessity to test all possible voltage vectors will be avoided. For sensorless control, EKF is composed to estimate the position and speed of the rotor. Whereas the performance of the proposed FSCPC essentially necessitates the full knowledge of SynRM parameters and provides an insufficient response under the parameter mismatch between the controller and the motor, online parameter estimation based on EKF is combined in the proposed control strategy to estimate all parameters of the machine. Furthermore, for simplicity, the parameters of PI speed controller and initial values of EKF covariance matrices are tuned offline using Particle Swarm Optimization (PSO). To demonstrate the feasibility of the proposed control, it is implemented in MATLAB/Simulink and tested under different operating conditions. Simulation results show high robustness and reliability of the proposed drive.

Keywords: synchronous reluctance motor; predictive current control; extended kalman filter; particle swarm optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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