Current commutation and control of brushless direct current drives using back electromotive force samples

Brushless DC machines (BLDC) are widely used in home, automotive, aerospace and military applications. The reason of this interest in different industries in this type of machine is due to their significant advantages. Brushless DC machines have a high power density, simple construction and higher efficiency compared to conventional AC and DC machines and lower cost comparing to permanent magnet AC synchronous machines. The phase currents of a BLDC machine have to commutate properly which is realised by using power semiconductors. For a proper commutation the rotor position is often obtained by an auxiliary instrument, mostly an arrangement of three Hall-effect sensors with 120 spatial displacement. In modern and cost-effective BLDC drives the focus is on replacing the noise sensitive and less reliable mechanical sensors by numerical algorithms, often referred to as sensorless or self-sensing methods. The advantage of these methods is the use of current or voltage measurements which are usually available as these are required for the control of the drive or the protection of the semiconductor switches. Avoiding the mechanical position sensor yields remarkable savings in production, installation and maintenance costs. It also implies a higher power to volume ratio and improves the reliability of the drive system. Different self-sensing techniques have been developed for BLDC machines. Two algorithms are proposed in this thesis for self-sensing commutation of BLDC machines using the back-EMF samples of the BLDC machine. Simulations and experimental tests as well as mathematical analysis verify the improved performance of the proposed techniques compared to the conventional back-EMF based self-sensing commutation techniques. For a robust BLDC drive control algorithm with a wide variety of applications, load torque is as a disturbance within the control-loop. Coupling the load to the motor shaft may cause variations of the inertia and viscous friction coefficient besides the load variation. Even for a drive with known load torque characteristics there are always some unmodelled components that can affect the performance of the drive system. In self-sensing controlled drives, these disturbances are more critical due to the limitations of the self-sensing algorithms compared to drives equipped with position sensors. To compensate or reject torque disturbances, control algorithms need the information of those disturbances. Direct measurement of the load torque on the machine shaft would require another expensive and sensitive mechanical sensor to the drive system as well as introducing all of the sensor related problems to the drive. An estimation algorithm can be a good alternative. The estimated load torque information is introduced to the self-sensing BLDC drive control loop to increase the disturbance rejection properties of the speed controller. This technique is verified by running different experimental tests within different operation conditions. The electromagnetic torque in an electrical machine is determined by the stator current. When considering the dynamical behaviour, the response time of this torque on a stator voltage variation depends on the electric time constant, while the time response of the mechanical system depends on the mechanical time constant. In most cases, the time delays in the electric subsystem are negligible compared to the response time of the mechanical subsystem. For such a system a cascaded PI speed and current control loop is sufficient to have a high performance control. However, for a low inertia machine when the electrical and mechanical time constants are close to each other the cascaded control strategies fail to provide a high performance in the dynamic behavior. When two cascade controllers are used changes in the speed set-point should be applied slowly in order to avoid stability problems. To solve this, a model based predictive control algorithm is proposed in this thesis which is able to control the speed of a low inertia brushless DC machine with a high bandwidth and good disturbance rejection properties. The performance of the proposed algorithm is evaluated by simulation and verified by experimental results as well. Additionally, the improvement on the disturbance rejection properties of the proposed algorithm during the load torque variations is studied. In chapters 1 and 2 the basic operation principles of the BLDC machine drives will be introduced. A short introduction is also given about the state of the art in control of BLDC drives and self-sensing control techniques. In chapter 3, a model for BLDC machines is derived, which allows to test control algorithms and estimators using simulations. A further use of the model is in Model Based Predictive Control (MBPC) of BLDC machines where a discretised model of the BLDC machine is implemented on a computation platform such as Field Programmable Gate Arrays (FPGA) in order to predict the future states of the machine. Chapter 4 covers the theory behind the proposed self-sensing commutation methods where new methodologies to estimate the rotor speed and position from back-EMF measurements are explained. The results of the simulation and experimental tests verifies the performance of the proposed position and speed estimators. It will also be proved that using the proposed techniques improve the detection accuracy of the commutation instants. In chapter 5, the focus is on the estimation of load torque, in order to use it to improve the dynamic performance of the self-sensing BLDC machine drives. The load torque information is used within the control loop to improve the disturbance rejection properties of the speed control for the disturbances resulting from the applied load torque of the machine. Some of the machine parameters are used within speed and load torque estimators such as back-EMF constant Ke and rotor inertia J. The accuracy with which machine parameters are known is limited. Some of the machine parameters can change during operation. Therefore, the influence of parameter errors on the position, speed and load torque is examined in chapter 5. In Chapter 6 the fundamentals of Model based Predictive Control for a BLDC drive is explained, which are then applied to a BLDC drive to control the rotor speed. As the MPC algorithm is computationally demanding, some enhancements on the FPGA program is also introduced in order to reduce the required resources within the FPGA implementation. To keep the current bounded and a high speed response a specific cost function is designed to meet the requirements. later on, the proposed MPC method is combined with the proposed self-sensing algorithm and the advantages of the combined algorithms is also investigated. The effects of the MPC parameters on the speed and current control performance is also examined by simulations and experiments. Finally, in chapter 7 the main results of the research is summarized . In addition, the original contributions that is give by this work in the area of self-sensing control is highlighted. It is also shown how the presented work could be continued and expanded

Improving the torque generation in self-sensing BLDC drives by shaping the current waveform

Brushless DC drives are widely used in different fields of application because of their high efficiency and power density. Torque ripple can be considered one of the drawbacks of these drives. This paper proposes a method to reduce the torque ripple in BLDC drives. For this reason, the current amplitude is adapted to the rotor position rather than to be kept constant as done in a conventional commutation method. This is done by computing an optimum reference current based on the phase back-EMF waveform. The proposed approach is implemented in a self-sensing drive so its applicability to self-sensing BLDC motor drives is verified. Simulation and experimental results are given and discussed to show that the proposed method actually is able to improve torque production

Implementing SVPWM Technique to an Axial Flux Permanent Magnet Synchronous Motor Drive with Internal Model Current Controller

This paper presents a study of axial flux permanent magnet synchronous motor (AFPMSM) drive system. An internal model control (IMC) strategy is introduced to control the AFPMSM drive through currents, leading to an extension of PI control with integrators added in the off-diagonal elements to remove the cross-coupling effects between the applied voltages and stator currents in a feed-forward manner. The reference voltage is applied through a space vector pulse width modulation (SVPWM) unit. A diverse set of test scenarios has been realized to comparatively evaluate the state estimation of the sensor-less AFPMSM drive performances under the implemented IMCbased control regime using a SVPWM inverter. The resulting MATLAB simulation outcomes in the face of no-load, nominal load and speed reversal clearly illustrate the well-behaved performances of IMC controller and SVPWM technique to an Axial Flux PM Motor Drive system

EKF and UKF-based estimation of a sensor-less axial flux PM machine under an internal-model control scheme using a SVPWM inverter

This paper presents a comparative estimation study of rotor speed and position of a sensor-less axial flux permanent magnet synchronous motor (AFPMSM) drive system using extended Kalman filter (EKF) and unscented Kalman filter (UKF) algorithms. An internal model control (IMC) strategy is introduced to control the AFPMSM drive through currents, leading to an extension of PI control with integrators added in the off-diagonal elements to remove the cross-coupling effects between the applied voltages and stator currents in a feed-forward manner. The reference voltage is applied through a space vector pulse width modulation (SVPWM) unit. A diverse set of test scenarios has been realized to comparatively evaluate the state estimation of the sensor-less AFPMSM drive performances under the implemented IMC-based control regime using a SVPWM inverter. The resulting MATLAB simulation outcomes in the face of no-load, nominal load and speed reversal clearly illustrate the well-behaved performances of the two estimation algorithms. The UKF seems to be more promising under noisy conditions. Nevertheless, there is no clear preference for either where steady-state performance is more critical

Robust position control of ultrasonic motor using VSS observer

Intrinsic properties of ultrasonic motor (high torque for low speed, high static torque, compact in size, etc.) offer great advantages for industrial applications. However, when load torque is applied, dead-zone occurs in control input. Therefore, sliding mode controller, which is a nonlinear controller, is adopted for ultrasonic motor. The state quantities, such as acceleration, speed, and position are needed to apply the sliding mode controller for position control. However, rotary encoder causes quantization errors in the speed information. This paper presents a robust position control method for ultrasonic motor by using Variable Structure System(VSS) observer. The state variables for sliding mode controller are estimated by the VSS observer. Besides, a small, low cost, and good response sliding mode controller is designed in this paper by using a micro computer that is essential in embedded system for the developments of industrial equipments. The effectiveness of the proposed method is verified by experimental results

FPGA-based implementation of the back-EMF symmetric-threshold-tracking sensorless commutation method for brushless DC-machines

The operation of brushless DC permanent-magnet machines requires information of the rotor position to steer the semiconductor switches of the power-supply module which is commonly referred to as Brushless Commutation. Different sensorless techniques have been proposed to estimate the rotor position using current and voltage measurements of the machine. Detection of the back-electromotive force (EMF) zero-crossing moments is one of the methods most used to achieve sensorless control by predicting the commutation moments. Most of the techniques based on this phenomenon have the inherit disadvantage of an indirect detection of commutation moments. This is the result of the commutation moment occurring 30 electrical degrees after the zero-crossing of the induced back-emf in the unexcited phase. Often, the time difference between the zero crossing of the back-emf and the optimal current commutation is assumed constant. This assumption can be valid for steady-state operation, however a varying time difference should be taken into account during transient operation of the BLDC machine. This uncertainty degrades the performance of the drive during transients. To overcome this problem which improves the performance while keeping the simplicity of the back-emf zero-crossing detection method an enhancement is proposed. The proposed sensorless method operates parameterless in a way it uses none of the brushless dc-machine parameters. In this paper different aspects of experimental implementation of the new method as well as various aspects of the FPGA programming are discussed. Proposed control method is implemented within a Xilinx Spartan 3E XC3S500E board

FPGA-based real-time simulation of sensorless control of PMSM drive at standstill

This paper presents a real-time simulation of a sensorless control method for IPMSM drives based on Park Transformation with a reference frame fixed to the rotor and assuming a sinusoidal flux (sinusoidal back-emf). The objective of this paper is to Hardware In Loop (HIL) evaluation of a sensorless position estimation of the Permanent Magnet Synchronous Motor (PMSM) drive at standstill as well as the current controller. An anti-windup method is integrated within the controller to ensure a good dynamic performance during transients. Test voltages vectors are injected in such a manner the current samples are not affected. An asymmetric Pulse Width Modulation (PWM) allows to apply these test vectors each PWM period

Robust position control of ultrasonic motor considering dead-zone

Intrinsic properties of ultrasonic motor (high torque for low speed, high static torque, compact in size, etc.) offer great advantages for industrial applications. However, when load torque is applied, dead-zone occurs in the control input. Therefore, a nonlinear controller, which considers dead-zone, is adopted for ultrasonic motor. The state quantities, such as acceleration, speed, and position are needed to apply the nonlinear controller for position control. However, rotary encoder causes quantization errors in the speed information. This paper presents a robust position control method for ultrasonic motor considering dead-zone. The state variables for nonlinear controller are estimated by a Variable Structure System (VSS) observer. Besides, a small, low cost, and good response nonlinear controller is designed by using a micro computer that is essential in embedded system for the developments of industrial equipments. Effectiveness of the proposed method is verified by the experimental results

Evaluation of a dual-T-type converter supplying an open-end winding induction machine

The multilevel inverter is a promising technology compared to two-level inverters in the applications of ac-drives and smart-grid applications. In this paper, a dual-T-type three-level inverters is used to drive an open-end winding induction machine. The Space-Vector Pulse-Width Modulation is selected as a good-performing control strategy to control the dual-inverter. Furthermore, an optimized method is used to select the proper switching state for the new configuration to decrease the converter losses. A comparison between the proposed configuration and the conventional diode clamped converter is made. The proposed drive system is designed and modelled by using Matlab/Simulink. It is shown that the converter can give the same hexagon, wave forms and harmonic spectrum of the five level converter. An optimized switching state selection is used to reduce the converter losses. The advantages and drawbacks of the dual-T-type configuration are discussed. In addition, the harmonic analysis and the loss calculations of the dual-T-type converter are provided and compared to the T-type three-level converter and the conventional five-level diode-clamped-converter

Open-loop behaviour of back-EMF based self-sensing BLDC drives

In brushless dc machines the current commutation between phases is triggered based on information of the permanent-magnet flux position. In most BLDC drives, either sensors are used to detect this flux position or a self-sensing algorithm is applied to estimate the flux position instead. Over the last decades, different techniques have been developed to detect the current commutation instants from the speed-induced back-emf signal. Several of these methods when used in open-loop speed control show speed variations at an increasing acceleration. These speed variations are often reduced by closing the speed control loop that adapts the current amplitude to generate a torque for stable speed operation. This paper studies the open-loop behaviour of back-emf based self-sensing BLDC drives and gives a criterium for which the average speed acceleration over a time period is zero. The criterium depends on the machine inertia, speed and torque and indicates the robustness of the self-sensing method against speed variations. Fulfilling the criterium reduces the burden on the speed-controller to guarantee stable speed operation. Even in the ideal case where noise on the measured back-emf is absent and small voltages can be measured accurately, the application of a self-sensing method can be hampered due to a lack of robustness against speed variations if not well-designed