A Novel PMSM Hybrid Sensorless Control Strategy for EV Applications Based on PLL and HFI

In this paper, a novel hybrid sensorless control strategy for Permanent Magnet Synchronous Machine (PMSM) drives applied to Electric Vehicles (EV) is presented. This sensorless strategy covers the EV full speed range and also has speed reversal capability. It combines a High Frequency Injection (HFI) technique for low and zero speeds, and a Phase-Locked Loop (PLL) for the medium and high speed regions. A solution to achieve smooth transitions between the PLL and the HFI strategies is also proposed, allowing to correctly detect the rotor position polarity when HFI takes part. Wide speed and torque four-quadrant simulation results are provided, which validate the proposed sensorless strategy for being further implemented in EV.Peer ReviewedPostprint (author's final draft

On extended Kalman filters with augmented state vectors for the stator flux estimation in SPMSMs

The demand for highly dynamic electrical drives, characterized by high quality torque control, in a wide variety of applications has grown tremendously during the past decades. Direct torque control (DTC) for permanent magnet synchronous motors (PMSM) can provide this accurate and fast torque control. When applying DTC the change of the stator flux linkage vector is controlled, based on torque and flux errors. As such the estimation of the stator flux linkage is essential. In the literature several possible solutions for the estimation of the stator flux linkage are proposed. In order to overcome problems associated with the integration of the back-emf, the use of state observers has been advocated in the literature. Several types of state observers have been conceived and implemented for PMSMs, especially the Extended Kalman Filter (EKF) has received much attention. In most reported applications however the EKF is only used to estimate the speed and rotor position of the PMSM in order to realize field oriented current control in a rotor reference frame. Far fewer publications mention the use of an EKF to estimate the stator flux linkage vector in order to apply DTC. Still the performance of the EKF in the estimation of the stator flux linkage vector has not yet been thoroughly investigated. In this paper the performance of the EKF for stator flux linkage is studied and simulated. The possibilities to improve the estimation by augmenting the state vector and the consequences of these alterations are explored. Important practical aspects for FPGA implementation are discussed

Development of FPGA based control architecture for PMSM drives

University of Technology Sydney. Faculty of Engineering and Information Technology.The rapid advancement of the very large scale integration (VLSI) technology and electronic design automation techniques in recent years has made a significant impact on the development of complex and compact high performance control architecture for industrial motion systems. Specific hardware with the field programmable gate array (FPGA) technology is now considered as a promising solution in order to make use of the reliability and versatility of controllers. Indeed, FPGAs have been successfully used in many control applications such as power converter control and electrical machines control. This is because such an FPGA-based implementation can offer an effective reprogrammable capability and overcome disadvantages of microprocessor-based or digital signal processor-based embedded systems. This thesis aims to provide a proof-of-concept for the control-system-on-chip and a prototype for a fully-implemented FPGA control architecture for permanent magnet synchronous motor (PMSM) drives. In this thesis, a special focus is given on analytical effects, design procedure, and control performance enhancement for PMSM drives under sensor/sensorless vector control using a number of control techniques. The control schemes include FPGA-based intelligent control and robust cascade control for single axis and multiple axis tracking with PMSMs. An important contribution of this thesis rests with a convincing demonstration of high performance estimation schemes, using sliding mode observers and extended Kalman filters, in terms of accuracy and robustness against noisy and/or perturbed currents for sensorless PMSM control based on the FPGA technology. In addition, a sequential finite state machine is developed in this work to result in less logic gate resources, leading to a faster processing time. Significance of this thesis contribution includes in providing a feasible and effective solution for the implementation of complex control strategies to fully exploit the FPGA advantages in power electronics and drive applications

Industrial applications of the Kalman filter:a review

International audienc

On the stator flux linkage estimation of an PMSM with extended Kalman filters

The demand for drives with high quality torque control has grown tremendously in a wide variety of applications. Direct torque control (DTC) for permanent magnet synchronous motors can provide this accurate and fast torque control. When applying DTC the change of the stator flux linkage vector is controlled. As such the estimation of the stator flux linkage is essential. In this paper the performance of the Extended Kalman Filter (EKF) for stator flux linkage estimation is studied. Starting from a formulation of the EKF for isotropic motors, the influence of rotor anisotropy and saturation is evaluated. Subsequently it is expanded to highly isotropic motors as well. In both cases the possibilities to add parameter estimations are evaluated

High Frequency Injection Sensorless Control for a Permanent Magnet Synchronous Machine Driven by an FPGA Controlled SiC Inverter

As motor drive inverters continue to employ Silicon Carbide (SiC) and Gallium Nitride (GaN) devices for power density improvements, sensorless motor control strategies can be developed with field-programmable gate arrays (FPGA) to take advantage of high inverter switching frequencies. Through the FPGA’s parallel processing capabilities, a high control bandwidth sensorless control algorithm can be employed. Sensorless motor control offers cost reductions through the elimination of mechanical position sensors or more reliable electric drive systems by providing additional position and speed information of the electric motor. Back electromotive force (EMF) estimation or model-based methods used for motor control provide precise sensorless control at high speeds; however, they are unreliable at low speeds. High frequency injection (HFI) sensorless control demonstrates an improvement at low speeds through magnetic saliency tracking. In this work, a sinusoidal and square-wave high frequency injection sensorless control method is utilized to examine the impact an interior permanent magnet synchronous machine’s (IPMSM) fundamental frequency, injection frequency, and switching frequency have on the audible noise spectrum and electrical angle estimation. The audible noise and electrical angle estimation are evaluated at different injection voltages, injection frequencies, switching frequencies, and rotor speeds. Furthermore, a proposed strategy for selecting the proper injection frequency, injection voltage, and switching frequency is given to minimize the electrical angle estimation error

A comparison of stator flux linkage estimators for a direct torque controlled PMSM drive

In an increasing number of applications highly dynamic electrical drives, characterized by high quality torque control, are demanded. Direct torque control (DTC) for AC machines, permanent magnet synchronous motors (PMSM) or induction machines, can provide this accurate and fast torque control. When applying DTC the change of the stator flux linkage vector is controlled, based on torque and flux errors. As such the estimation of the stator flux linkage is essential for a DTC drive. Furthermore the quality of the estimation directly determines the capability of the drive. In the literature several possible solutions for the estimation of the stator flux linkage are proposed. However, a comprehensive comparison between these solutions is not present. This paper gives an overview of several techniques for the estimation of the stator flux linkage for DTC in PMSMs. The theoretical advantages and disadvantages of the methods are outlined. After a short discussion on the effects of erroneous estimations the results from simulations for the different methods are reviewed. It is shown that, despite their simplicity stabilized voltage model methods can offer good performance. Still they can not reach the performance of an extended Kalman filter implementation of a current model. Aspects of the practical implementation on FPGA are discussed

Sensorless Control with Switching Frequency Square Wave Voltage Injection for SPMSM with Low Rotor Magnetic Anisotropy

High-frequency signal injection sensorless algorithms are widely studied and used for rotor angle estimation in PMSM at low speed or standstill. One of the main drawbacks of such methods is the acoustic noise connected to the voltage injection. In order to minimize this problem, it is advisable to increase the frequency of the injected signal. Thus, many studies focus on square-wave injection at the switching frequency, which is the maximum theoretical frequency. Since these methods exploit the rotor magnetic anisotropy, it is relatively easy to use them in interior PMSMs, where the rotor anisotropy is high. On the contrary, it is hard to exploit them in surface PMSMs, which have an almost symmetric rotor, although a low rotor magnetic anisotropy is still present. In this paper, a sensorless algorithm with switching frequency squarewave injection is developed for surface PMSMs. To increase the signal-to-noise ratio, current oversampling is exploited. The benefits of such a technique are demonstrated with experimental results on a 2 Nm SPMSM

FPGAs in Industrial Control Applications

The aim of this paper is to review the state-of-the-art of Field Programmable Gate Array (FPGA) technologies and their contribution to industrial control applications. Authors start by addressing various research fields which can exploit the advantages of FPGAs. The features of these devices are then presented, followed by their corresponding design tools. To illustrate the benefits of using FPGAs in the case of complex control applications, a sensorless motor controller has been treated. This controller is based on the Extended Kalman Filter. Its development has been made according to a dedicated design methodology, which is also discussed. The use of FPGAs to implement artificial intelligence-based industrial controllers is then briefly reviewed. The final section presents two short case studies of Neural Network control systems designs targeting FPGAs

Adaptive Two-Stage Extended Kalman Filter Theory in Application of Sensorless Control for Permanent Magnet Synchronous Motor

Extended Kalman filters (EKF) have been widely used for sensorless field oriented control (FOC) in permanent magnet synchronous motor (PMSM). The first key problem associated with EKF is that the estimator requires all the plant dynamics and noise processes are exactly known. To compensate inaccurate model information and improve tracking ability, adaptive fading extended Kalman filtering algorithms have been proposed for the nonlinear system. The second key problem is that the EKF suffers from computational burden and numerical problems when state dimension is large. The two-stage extended Kalman filter (TSEKF) with respect to this problem has been extensively studied in the past. Combining the advantages of both AFEKF and TSEKF, this paper presents an adaptive two-stage extended Kalman filter (ATEKF) for closed-loop position and speed estimation of a PMSM to achieve sensorless operation. Experimental results demonstrate that the proposed ATEKF algorithm for PMSMs has strong robustness against model uncertainties and very good real-time state tracking ability