An Optimized Extended Kalman Filter Algorithm for Hybrid Stepper Motors

In this article a high performance Extended Kalman filter algorithm is used to estimate rotor speed, rotor position and load torque in a Hybrid Stepper Motor (HSM). Together with a PI speed controller, the Kalman filter forms a very robust sensorless closed loop system. During acceleration the motor is capable of running in closed loop from 0-100% of the nominal speed. The controller can handle charge steps of over 50% of the maximum load torque, and acceleration up to 13 [krad/s 2]. To achieve theses good dynamic capabilities, the Kalman filter is optimized by an off-line identification process. This gives a very good match between the model and the real system. The standstill position at start-up is estimated by a current injection method. Due to the optimized Kalman filter the experimental results show very good precision and closed loop robustness for a 10.5 [W] HSM. The algorithm is implemented on a floating point DSP. The driver consists of two H-bridges with MOSFET transistors. The currents are measured with one shunt resistor for each phase.

Innovative standstill position detection combined with sensorless control of synchronous motors

Jan Persson

Sensorless control of PMSM's (Permanent Magnet Synchronous Motors) has occupied scientists for a long time. The result of this research is becoming widely accepted by the industry due to its low cost and reliability. However, the majority of today's motor drives are still equipped with some kind of position sensor. The reason is that sensorless control still have several limitations and is usually more complex than a traditional motor control. A new method to estimate the standstill position of PMSM's is presented. The method is based on the anisotropic properties of permanent magnets and is therefore referred to as MAM (Magnetic Anisotropy Method). The proposed method is independent of any ferromagnetic material and can therefore be used in many types of ironless PMSM's where standard methods fail. The MAM method is tested on a linear ironless motor, and measurement results from a "stripped" rotative motor without any supporting ferromagnetic material are also presented. Furthermore, it is shown in this thesis how Extended Kalman filters can be used to estimate the rotor position, and especially how the robustness and dynamic response of the overall control algorithm can be improved by using identification experiments and optimization algorithms. Some basic modelling of PMSM's are also presented. The modelling process is focused on techniques which are suitable for observers such as Extended Kalman filters. To show the versatility of the method, experimental results from two different motor types are presented. The first is a linear ironless motor while the second is a Hybrid Stepper Motor (HSM). Amongst other things it will be shown how the HSM can be transformed into a highly dynamic brushless DC motor without the drawbacks that is usually associated with HSM's. The linear ironless motor is used to demonstrate that it is possible to achieve good and robust position control without using a direct position sensor. Many different simulation and experimental results are presented for both the HSM and the linear ironless motor. The experiments are deliberately chosen to show both steady-state and dynamic operation of the proposed algorithm. The robustness of the overall algorithm is also analysed, considering unknown external load torque and motor parameters.

EPFL2005

Innovative standstill position detection combined with sensorless control of synchronous motors

Jan Persson

EPFL2005