Guest columnist Anders Norlin Fredriksen, marketing manager at Analog Devices, believes that improved DSP capability can support higher levels of system modelling for sensorless motor control
The recent development and easier deployment of very highly exposed permanent magnet materials has driven increased use of permanent magnet synchronous motors (PMSMs) in high-performance variable-speed motors for many industrial applications.
Advantages of using a PMSM drive include a high ratio of torque to weight, high power factor, faster response, rugged construction, easy maintenance, ease of control and high efficiency.
The high performance speed and position control requires an accurate knowledge of rotor shaft position and velocity to synchronise the phase excitation pulses to the rotor position. This implies the need for speed and position sensors, such as absolute encoders and magnetic resolvers attached to the shaft of the motor.
But in most applications, these sensors have several disadvantages, such as reduced reliability, susceptibility to noise, additional cost and weight, and increased complexity of the drive system. Sensorless vector control eliminates the need for speed/position sensors, overcoming these challenges.
Three basic techniques have been developed for sensorless rotor position estimation of PMSM drives:
* Techniques based on back-electromotive-force (back-EMF) estimation.
* Techniques based on state observers and extended Kalman filters (EKF).
* Or other techniques based on spatial saliency tracking – initial positioning.
Back-EMF techniques
Position estimation based on back-EMF techniques estimates the flux and velocity from voltage and current, which is especially sensitive to the stator resistance at low speed range. The actual voltage information on the machine terminal can hardly be detected because of the machine’s small back-EMF and the system noise caused by the non-linear characteristics of the switching devices. Back-EMF methods yield good position estimation in middle and high speed, but fail in the low speed region.
The magnitude of back-EMF voltage is proportional to the rotor speed, so at standstill it is impossible to estimate the initial position. Therefore, starting from unknown rotor position may be accompanied by a temporary reverse rotation or may cause a starting failure. Because of its ability to perform state estimation for non-linear systems that involve random noise environment, EMF appears a viable and computationally efficient candidate for the online estimation of speed and rotor position of a PMSM.
EKF techniques
EKF is one of the most widely used methods for tracking and estimation for non-linear systems because of its simplicity, optimality, tractability and robustness. To achieve sensorless control of the salient-pole IPMSM, EKF is used to estimate the speed and rotor position. The line voltages of the motor and load torque are the vector input variable of the system.
The speed and the rotor position are the two magnitudes to be estimated, and, with the motor current, they constitute the state vector. The motor currents will be the only observable magnitude that constitutes the output vector.
For the implementation of an EKF for sensorless IPMSM drives, it is essential to choose the two-axis reference frame. The ideal case is to use the d- and q rotating reference frame attached to the rotor.
This approach is not compatible for IPMSM sensorless speed control because the input vector (currents and voltages) of the estimator depend on the rotor position. It has been observed in implementation that an error of estimation in the initial position of the rotor can have serious repercussions by inducing error in the progress of the EKF with regard to the real system.
The enhancement of multiple observer models running real-time, model-based estimators will help to enhance performance of the drive, the system efficiency and topology and the deployment method of the design.
Graphical systems such as Matlab/Simulink are capable of easing the design flow and enhancing the development of new algorithms. With these tools – and the link to the executing processors – a more complex deployment can be achieved.
Motor system designers can now use high-performance digital signal processors (DSPs) to achieve greater system functionality and precision by using more advanced algorithms, which can be applied to accurately determine rotor shaft position and speed, eliminating the need for position/speed sensors in the system.
New technologies are pushing a paradigm shift in motor system capability, balancing design topologies and processor attributes to achieve greater overall system performance and efficiency.
High-performing DSPs enable modern control theory to be used in advanced system modelling, ensuring optimal power and control efficiency for any real-time motor system.
The broad-based application of sensorless vector control is now within reach and promises to accelerate the global movement toward more energy-efficient and performing industrial equipment.