Some Permanent Magnet Synchronous Motor (PMSM) Sensorless Control Methods based on Operation Speed Area

This paper compares some sensorless Permanent Magnet Synchronous Motor (PMSM) controls for driving an electric vehicle in terms of operating speed. Sensorless control is a type of control method in which sensors, such as speed and position sensors, are not used to measure controlled variables.  The controlled variable value is estimated from the stator current measurement. Sensorless control performance is not as good as a sensor-based system. This paper aims are to recommend a control method for the PMSM sensorless controls that would be used to drive an electric vehicle. The methods that we will discuss are divided into four categories based on the operation speed area.  They are a startup, low speed, high speed, and low and high-speed areas. The low and high-speed area will be divided into with and without switching.  If PMSM more work at high speed, the most speed area that is used, we prefer to choose the method that works at high speed, that is, the modification or combination of two or more conventional methods

Sensorless control application of PMSM with a novel adaptation mechanism

KALE, MURAT/0000-0002-3814-9009; Dursun, Mustafa/0000-0001-9952-9358; Boz, Ali Fuat/0000-0001-6575-7678; Karabacak, Murat/0000-0001-6468-8471WOS: 000422933800009This paper proposes a novel adaptation design to replace the classical proportional-integral controller used in model reference adaptive system (MRAS) speed estimation. The proposed adaptation scheme is an association of two fuzzy units. The rules of each module were obtained by user experience and numerical data. In the traditional fuzzy logic controllers, the computational complication enhances with the attributes of the system variable quantities; the number of rules increases incrementally while the number of control variables increases. This negatively affects the response time of the system. This novel design was deduced to reduce the number of rules for a linear function of system variables. By this way, the response of the system became faster. Detailed simulation and experimental results were obtained for comparison of this novel method with traditional MRAS techniques. The results showed that the proposed method was faster in speed tracking and exhibited higher prediction accuracy and less oscillation than the traditional method. Thus, the proposed MRAS method was clearly seen to be applicable and reliable.Duzce University Scientific Research Projects Coordination Unit [2015.07.03.364]This work was supported by the number of Duzce University Scientific Research Projects Coordination Unit with number of 2015.07.03.364