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

Sensorless drives for permanent magnet synchronous motors

Ph.DDOCTOR OF PHILOSOPH

Integrated Motor System Estimation Using Efficiency Maps

A motor combined with an inverter based variable speed drive and an end load device forms a motor system that can operate over a wide area of different speed and load combinations. The majority of the motor systems used in the world are low power systems that have poor motor and system efficiency, resulting in higher energy consumption. Because of cost considerations, such systems rarely include the sensors required for more efficient feedback control schemes. In cases where physical sensors are used, those motor systems experience higher cost and reduced reliability. Using models of the motor and/or load, it is possible for a variable speed drive to estimate some motor system quantities. Position sensorless control is the most common form of sensorless operation, but it is also possible to estimate motor torque, pump pressure and pump flow. Sensorless estimates can replace physical sensors, increasing reliability and reducing both the size and cost of the motor system. For efficient and effective sensorless motor operation, accurate knowledge of a motor system’s operation over a wide area must be understood in terms of the real time system state and the efficiency of the system components. This research considers sensorless state estimation of a low-cost motor system integrated with an end application/load. A focus is given to expanding the operating area of sensorless techniques, and to better understand a motor system’s performance over a wide operating area. Motor systems using permanent magnet (PM) machines were studied because of their high efficiency, high power density, and ability to operate using a range of position sensorless control schemes. An improved method of position sensorless control for brushless DC motors was developed, enabling wider speed operation compared to methods of similar complexity. The method was implemented on a low-cost motor drive, and the performance was verified experimentally. To better understand the performance of an integrated motor system over a large operating area, a method of autonomous testing was developed. The flexible hardware and software-based test system was adaptable to different motor system applications and collected large volumes of temperature-controlled efficiency data, allowing for a motor system to be characterised in greater detail over its operating area. Using large sets of experimental data, a new method for general motor state sensorless estimation was developed. Estimator models were developed for speed, torque, DC power, AC power, mechanical power, inverter efficiency, motor efficiency and system efficiency. The estimators were implemented in the firmware of a low-cost inverter, and the performance over the operating area of the motor system was experimentally verified. The method of sensorless state estimation was then extended to a pump system, demonstrating the method’s ability to model the nonlinear relationship between motor and pump quantities. Estimator models were developed for pump head pressure, flow, hydraulic power, efficiency and total volume pumped. Estimator performance over the system’s operating area was experimentally verified, with temperature changes and dynamic performance also being considered. The methods discussed are not limited to pump systems, but are applicable to fans, compressors, vehicles and other motor systems with multiple components, sensors, and room for efficiency improvements.Thesis (Ph.D.) -- University of Adelaide, School of Electrical and Electronic Engineering, 202

Control of a brushless permanent magnet machine using an integrated torque sensor in place of a rotor position sensor

The work presented in this thesis proposes the use of measured torque feedback from an integrated, low cost surface acoustic wave (SAW) torque transducer in place of a position sensor to control brushless permanent magnet (BLPM) machines. The BLPM machine closed loop control requires knowledge of the rotor position to control stator current and maximum torque per ampere. The electrical position feedback to control the phase current requires a position sensor or position sensorless technique. Position sensors such as absolute encoder or resolver are needed for position information, in the absolute encoder, an accurately patterned disk rotates between a light source and a detector giving a unique digital output signal for every shaft position. However, each bit in the digital world represents an independent track on the encoder disk, resulting in a complex and costly sensors. Brushless resolvers operation is based on inductive coupling between stator and rotor winding. The resolver with its resolver to digital converter also gives precise absolute position information, but again the cost is often prohibitive. So the disadvantages of the position sensors are the added cost and size to the machine. The position sensorless techniques for the BLPM machine are based on obtaining position from the terminal voltages and currents based on estimating the back electro-magnetic force (EMF), flux-linkage or inductance which from position can be estimated. The disadvantages of the back-EMF and flux-linkage techniques are (1) that they behave poorly at zero and low speed (2) behave poorly for load disturbances since load torque is estimated from machine parameters which can change. The inductance techniques work at zero and low speed, however the disadvantages are (1) in a surface mounted machine there is no saliency so any variation of winding inductances with rotor position arises from magnetic saturation; (2) the back-EMF dominates the rate-of-change in the current; (3) the variation of incremental inductances with rotor position undergoes two cycles per single electrical cycle of the brushless pm machine causing an ambiguity in sensed position; (4) the distortion due to the nonlinearities in the inverter; (5) the load offsets and the noise caused by signal injection. This thesis develops a start-up routine and operation algorithms that enhance the performance of position sensorless control of brushless permanent magnet machines at all speeds, including zero speed, and loads by using a machine integrated, low-cost, SAW torque transducer in place of the rotor position sensor.EThOS - Electronic Theses Online ServicePublic Authority of Applied Education in KuwaitGBUnited Kingdo

Sensorless position estimation in fault-tolerant permanent magnet AC motor drives with redundancy.

Safety critical applications are heavily dependent on fault-tolerant motor drives being capable of continuing to operate satisfactorily under faults. This research utilizes a fault-tolerant PMAC motor drive with redundancy involving dual drives to provide parallel redundancy where each drive has electrically, magnetically, thermally and physically independent phases to improve its fault-tolerant capabilities. PMAC motor drives can offer high power and torque densities which are essential in high performance applications, for example, more-electric airplanes. In this thesis, two sensorless algorithms are proposed to estimate the rotor position in a fault-tolerant three-phase surface-mounted sinusoidal PMAC motor drive with redundancy under normal and faulted operating conditions. The key aims are to improve the reliability by eliminating the use of a position sensor which is one of major sources of failures, as well as by offering fault-tolerant position estimation. The algorithms utilize measurements of the winding currents and phase voltages, to compute flux linkage increments without integration, hence producing the predicted position values. Estimation errors due measurements are compensated for by a modified phase-locked loop technique which forces the predicted positions to track the flux linkage increments, finally generating the rotor position estimate. The fault-tolerant three-phase sensorless position estimation method utilizes the measured data from the three phase windings in each drive, consequently obtaining a total of two position estimates. However, the fault-tolerant two-phase sensorless position estimation method uses measurements from pairs of phases and produces three position estimates for each drive. Therefore, six position estimates are available in the dual drive system. In normal operation, all of these position estimates can be averaged to achieve a final rotor angle estimate in both schemes. Under faulted operating conditions, on the other hand, a final position estimate should be achieved by averaging position estimates obtained with measurements from healthy phases since unacceptable estimation errors can be created by making use of measured values from phases with failures. In order to validate the effectiveness of the proposed fault-tolerant sensorless position estimation schemes, the algorithms were tested using both simulated data and offline measured data from an experimental fault-tolerant PMAC motor drive system. In the healthy condition, both techniques presented good performance with acceptable accuracies under low and high steady-state speeds, starting from standstill and step load changes. In addition, they had robustness against parameter variations and measurement errors, as well as the ability to recover quickly from large incorrect initial position information. Under faulted operating conditions such as sensor failures, however, the two-phase sensorless method was more reliable than the threephase sensorless method since it could operate even with a faulty phase.Thesis (Ph.D.) -- University of Adelaide, School of Electrical and Electronic Engineering, 201

Position estimation and performance prediction for permanent-magnet motor drives

PhD ThesisThis thesis presents a theoretical and experimental development of a novel position estimator, a simulation model, and an analytical solution for brushless PM motor drive. The operation of the drive, the position estimation model of the test motor, development of hardware, and basic operation of inverter are discussed. Starting with the well-known continuous-time model of brushless PM motor, a sampled-data model is developed that is suitable for th6, application of real-time position estimator. An analytical methodo f calculating the steady-stateb ehaviouro f the brushlessP M motor for 1200in verter operation is presentedT. he analysisa ssumesth at the machinea ir gap is free of saliency effects, and has sinusoidal back EMF. The analytical solution is derived for 60" electrical of the whole period. By experimental results, it is shown that the method of analysis is adequate to predict Ihe motor's performance for typical operating points including phase advance and phase delay operation. C) I A computer simulation model for prediction of the performance of brushless PM moto rs is presented. The model is formulated entirely in the natural abc frame of reference, which allows direct comparison of the simulation and corresponding experimental results. The equations and diagrams are put into a convenient form for the simulation and future developments and library modules. The simulation model and corresponding experimental data of the brushless PM motor drive is given. The thesis describes a modem solution to real-time rotor position estimation, which has been subject to intense research activity for the last 15 years. The implemented new algorithm for shaft position sensorless operation of PM motors is based on the flux linkage and line current estimation. The position estimation algorithm has also been verified by both off-line and on-line experiments (accomplished by a DSP, TMS320C30), and a wide range of steady-statea nd transient results have been 0gi0v en including starting from rest. The position estimation method effectively moves the position measurement point in the drive from the mechanical side to the motor's terminals. As well as eliminating the mechanical shaft position sensor, the investigated method can be used for high performance torque control of brushless PM motors. The thesis demonstrates that, in contrast to many other "sensorless" schemes, the new position estimation method is able to work effectively over the full operating range of the drive, and is applicable to a wide range of motor/converter types. Since the hardware is straightforward, only the new position estimation algorithm differentiates a system. Therefore, if a DSP control system is already implemented in the drive, the position estimator can be implemented at low cost.Istanbul Technical University and Higher Education Counci

Computational framework for real-time diagnostics and prognostics of aircraft actuation systems

Prognostics and Health Management (PHM) are emerging approaches to product life cycle that will maintain system safety and improve reliability, while reducing operating and maintenance costs. This is particularly relevant for aerospace systems, where high levels of integrity and high performances are required at the same time. We propose a novel strategy for the nearly real-time Fault Detection and Identification (FDI) of a dynamical assembly, and for the estimation of Remaining Useful Life (RUL) of the system. The availability of a timely estimate of the health status of the system will allow for an informed adaptive planning of maintenance and a dynamical reconfiguration of the mission profile, reducing operating costs and improving reliability. This work addresses the three phases of the prognostic flow - namely (1) signal acquisition, (2) Fault Detection and Identification, and (3) Remaining Useful Life estimation - and introduces a computationally efficient procedure suitable for real-time, on-board execution. To achieve this goal, we propose to combine information from physical models of different fidelity with machine learning techniques to obtain efficient representations (surrogate models) suitable for nearly real-time applications. Additionally, we propose an importance sampling strategy and a novel approach to model damage propagation for dynamical systems. The methodology is assessed for the FDI and RUL estimation of an aircraft electromechanical actuator (EMA) for secondary flight controls. The results show that the proposed method allows for a high precision in the evaluation of the system RUL, while outperforming common model-based techniques in terms of computational time.Comment: 57 page

Precision Control of a Sensorless Brushless Direct Current Motor System

Sensorless control strategies were first suggested well over a decade ago with the aim of reducing the size, weight and unit cost of electrically actuated servo systems. The resulting algorithms have been successfully applied to the induction and synchronous motor families in applications where control of armature speeds above approximately one hundred revolutions per minute is desired. However, sensorless position control remains problematic. This thesis provides an in depth investigation into sensorless motor control strategies for high precision motion control applications. Specifically, methods of achieving control of position and very low speed thresholds are investigated. The developed grey box identification techniques are shown to perform better than their traditional white or black box counterparts. Further, fuzzy model based sliding mode control is implemented and results demonstrate its improved robustness to certain classes of disturbance. Attempts to reject uncertainty within the developed models using the sliding mode are discussed. Novel controllers, which enhance the performance of the sliding mode are presented. Finally, algorithms that achieve control without a primary feedback sensor are successfully demonstrated. Sensorless position control is achieved with resolutions equivalent to those of existing stepper motor technology. The successful control of armature speeds below sixty revolutions per minute is achieved and problems typically associated with motor starting are circumvented.Research Instruments Ltd