Low-Speed Sensorless Control of Permanent-Magnet Synchronous Motors

Permanent-magnet synchronous motors provide, in conjunction with field-oriented control (FOC), a servo system with very high power density, efficiency and dynamic performance. The drawback of the classical FOC configuration is the requirement of a position or speed sensor for its reference frame transformations. Sensorless control schemes aim to eliminate this sensor from the FOC topology, which the back-EMF estimation methods have generally succeeded in for motor speeds above, typically, 15 % to 20 % of rated value. The focus of this thesis is on developing sensorless schemes that function reliably in the low-speed range, which is defined here as speeds at or below 10 RPM, including operation at standstill. In this thesis, in the framework of space vectors, high-frequency and voltage pulse injection methods are developed and tested by experiment. The high-frequency injection methods are generally sensitive to the voltage error introduced by the nonideal characteristics of voltage source inverter drives. Compensating for the inverter voltage error typically requires offline characterization of the inverter, which represents an impractical dependency. Instead of compensating for the inverter voltage error, the voltage pulse injection methods are instead developed to be robust to it. The INFORM method is modified to directly take into account the inverter voltage error, and measurements results show reasonable tracking performance of the rotor position, which, due to the effect of magnetic saturation, degrades significantly above the rated current of the motor tested. A new algorithm is developed based on the same fundamentals as the INFORM method, but which deliberately utilizes less information. This restricts the estimate of the rotor position to fixed 30◦ sectors, but in doing so, the algorithm is able to reliably estimate the rotor position to within ±20◦ electrical, regardless of the level of load current. For low levels of load current, the estimation error of the INFORM method is slightly lower.

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