Quantifying the commutation error of a BLDC machine using sensorless load angle estimation

BLDC motors are often used for high speed applications, for example in pumps, ventilators and refrigerators. For commutation discrete position information is necessary. This feedback is often provided by Hall sensors instead of more expensive encoders. However, even small misalignment of the Hall sensors in low cost BLDC motors can lead to unwanted torque ripples or reduced performance of BLDC motors. This misplacement leads not only to noise and vibrations caused by the torque ripples but also to lower efficiency. In this paper, a self-sensing technique to assess the misalignment is introduced. The objective is to obtain knowledge of the quality of the commutation by quantifying the misalignment. The method used in this paper is based on the fundamental components of voltage and current measurements and only needs the available current and voltage signals and electrical parameters such as resistance and inductance to estimate the misalignment

Fault Signature Identification for BLDC motor Drive System -A Statistical Signal Fusion Approach

A hybrid approach based on multirate signal processing and sensory data fusion is proposed for the condition monitoring and identification of fault signal signatures used in the Flight ECS (Engine Control System) unit. Though motor current signature analysis (MCSA) is widely used for fault detection now-a-days, the proposed hybrid method qualifies as one of the most powerful online/offline techniques for diagnosing the process faults. Existing approaches have some drawbacks that can degrade the performance and accuracy of a process-diagnosis system. In particular, it is very difficult to detect random stochastic noise due to the nonlinear behavior of valve controller. Using only Short Time Fourier Transform (STFT), frequency leakage and the small amplitude of the current components related to the fault can be observed, but the fault due to the controller behavior cannot be observed. Therefore, a framework of advanced multirate signal and data-processing aided with sensor fusion algorithms is proposed in this article and satisfactory results are obtained. For implementing the system, a DSP-based BLDC motor controller with three-phase inverter module (TMS 320F2812) is used and the performance of the proposed method is validated on real time data.Comment: 7 Pages, 7 figure

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

Design, Modeling and Control of a Two-wheel Balancing Robot Driven by BLDC Motors

The focus of this document is on the design, modeling, and control of a self-balancing two wheel robot, hereafter referred to as the balance bot, driven by independent brushless DC (BLDC) motors. The balance bot frame is composed of stacked layers allowing a lightweight, modular, and rigid mechanical design. The robot is actuated by a pair of brushless DC motors equipped with Hall effect sensors and encoders allowing determination of the angle and angular velocity of each wheel. Absolute orientation measurement is accomplished using a full 9-axis IMU consisting of a 3-axis gyroscope, a 3-axis accelerometer, and a 3-axis magnetometer. The control algorithm is designed to minimize deviations from a set point specified by an external radio remote control, which allows the remote operator to steer and drive the bot wirelessly while it remains balanced. Multiple dynamic models are proposed in this analysis, and the selected model is used to develop a linear-quadratic regulator based state-feedback controller to perform reference tracking. Controller tracking performance is improved by incorporating a prefilter stage between the setpoint command from the remote control and the state-feedback controller. Modeling of the actuator dynamics is considered brie y and is discussed in relation to the control algorithm used to balance the robot. Electrical and software design implementations are also presented with a focus on effective implementation of the proposed control algorithms. Simulated and physical testing results show that the proposed balance bot and controller design are not only feasible but effective as a means of achieving robust performance under dynamic tracking profiles provided by the remote control

Harjattoman tasavirtamoottorin arviointi opto-mekaanisessa paikkasäätösovelluksessa

This thesis evaluates the applicability of a micro-sized brushless direct current (DC) mo- tor in an opto-mechanical positioning application. Brushless DC motors are electronically commutated motors that use permanent magnets to produce the airgap magnetic field. The motor is powered through an inverter or switching power supply which produces an AC electric current to drive each phase of the motor. Optimal current waveforms are determined by the motor controller based on the desired torque, speed or position requirements. The benefits of a brushless motor over conventional brushed DC motors are a high power to weight ratio, low noise and a long operating life. The purpose of this thesis is to find out the performance potential of such motors and determine methods to achieve it. Firstly, a motor model and an exact motor classification is presented. A literature review is made to discuss state of the art control methods and hardware configurations for dynamic position control. Based on the literature review, a control scheme with field-oriented control based torque control and cascaded PI controlled speed and position loops was selected for further evaluation. Experimental positioning tests were executed for two motors with different power transmission setups. Tests were performed with both, a hardware and software implemented, motor controllers. Results show promising performance. It was shown that the required acceleration is feasible with both, geared and direct drive, transmissions. Field-oriented control was shown as a well performing method to control torque but special caution was needed to implement a reliable position sensing solution in a small size as the control algorithm is intolerant for inaccurate and noisy position data. The conventional PI based position controller was effective in cases with no feedback related harmonics or motor related torque ripple but was not capable in handling ripple caused by a non-ideal system. Quality variances were seen between motors which were originated from mechanical defects and non-idealities in the stator structure. Further research is needed to achieve a better settling performance through filtering undesired feedback harmonics, better tuning and thus minimizing undesired vibrations.Tämän diplomityön tarkoituksena on arvioida pienikokoisen harjattoman tasavirtamoottorin soveltuvuutta opto-mekaaniseen paikkasäätösovellukseen. Harjattomat tasavirtamoottorit ovat elektronisesti ohjattuja moottoreita, joissa ilmavälin magneettivuo luodaan kestomagneeteilla. Moottorille syötetään virtaa taajuusmuuttajalta, joka muodostaa halutunlaisen vaihtovirran jokaiselle moottorin vaiheelle. Syötettävää virtaa ohjataan moottorinohjaimelta määritettyjen vääntö-, nopeus- ja paikkavaatimusten perusteella. Harjattoman DC-moottorin edut verrattuna perinteiseen harjalliseen DC-moottoriin ovat hyvä teho-painosuhde, hiljainen käyntiääni ja pitkä käyttöikä. Diplomityön tavoitteena on kartoittaa kyseisen moottorityypin suorituskyky paikkasäädössä ja tutkia keinoja saavuttaa haluttu taso. Alan tutkimuksessa ja kirjallisuudessa tunnettuja suorituskykyisiä säätömenetelmiä ja laite- sekä komponenttikokoonpanoja on koostettu kirjallisuuskatsauksessa. Tämän perusteella kokeellisiin testeihin valittiin säätöarkkitehtuuri vektorisäätöön perustuvalla virransäädöllä sekä PI-pohjaisilla nopeus- ja paikkasäätimillä. Kokeellisilla paikoitustesteillä arvioitiin kahden moottorin suorituskykyä erilaisilla voimansiirtovaihtoehdoilla. Testit suoritettiin sekä ohjelmistopohjaisella että sovelluskohtaiseen mikropiiriin toteutetulla laitepohjaisella säätimellä. Tulokset osoittavat että vaaditun kiihtyvyyden saavuttaminen on mahdollista sekä vaihteellisella että suoravetoisella voimansiirrolla. Vektorisäätö osoittautui suorituskykyiseksi virransäätömenetelmäksi, mutta moottorin asentomittauksen luotettava toteutus vaati erityishuomiota, sillä vektorisäätöalgoritmi on herkkä paikkadatan tarkkuudelle. PI-säätimillä toteutettu paikkasäätö osoittautui toimivaksi, mutta herkäksi moottorin epäideaalisuuksille sekä häiriöille takaisinkytkennässä. Moottoreiden välillä havaittiin laatueroja mekaanisissa toleransseissa ja staattorin rakenteessa. Lopullisen asettumisajan saavuttaminen vaatii lisätutkimusta. Erityishuomiota on kiinnitettävä harmonisten komponenttien suodattamiseen sekä systeemin säätöön, jotta ei-toivotut värinät saadaan minimoitua

Simulation of Four Four Quadrant Operation of Three Phase BLDC Motor Using Fuzzy

Abstract-Brushless DC (BLDC) motor drives are becoming more popular in industrial and traction applications. The control of BLDC motor in four quadrants is very vital. This paper deals with the fuzzy logic control of three phase BLDC motor. Without any loss of power the motor can be controlled in all the four quadrants. During regenerative braking period energy is conserved. The calculation capability of digital signal processor and controlling capability of fuzzy logic controller is used to achieve a precise control. Index terms-BLDC motor. dsPIC, fuzzy logic control, four quadrants, regenerative braking

A Review of Modeling and Diagnostic Techniques for Eccentricity Fault in Electric Machines

Research on the modeling and fault diagnosis of rotor eccentricities has been conducted during the past two decades. A variety of diagnostic theories and methods have been proposed based on different mechanisms, and there are reviews following either one type of electric machines or one type of eccentricity. Nonetheless, the research routes of modeling and diagnosis are common, regardless of machine or eccentricity types. This article tends to review all the possible modeling and diagnostic approaches for all common types of electric machines with eccentricities and provide suggestions on future research roadmap. The paper indicates that a reliable low-cost non-intrusive real-time online visualized diagnostic method is the trend. Observer-based diagnostic strategies are thought promising for the continued research

New Prognostic Method Based on Spectral Analysis Techniques Dealing with Motor Static Eccentricity for Aerospace Electromechanical Actuators

The prognostic algorithms are important to identify the precursors of incipient failures of electromechanical actuators (EMA) applied to aircraft primary flight controls. Keeping in mind the risk related to the performed functions of these actuators the anticipation of an incoming failure is really useful: a correct interpretation of the failure degradation pattern, in fact, can trig an early alert of the maintenance crew, who can properly schedule the servomechanism replacement, increasing the aircraft safety and reliability. In this paper the authors propose an innovative prognostic model-based approach, able to recognize the symptoms of an EMA degradation before the explicit exhibition of the anomalous behavior. The identification/evaluation of the considered incipient failures is performed analyzing proper critical system operational parameters, able to put in evidence the corresponding degradation path, by means of a numerical algorithm based on spectral analysis techniques. Subsequently, these operational parameters are correlated with the actual health condition of the considered system by means of failure maps created by a reference monitoring model-based algorithm. In the present work, the proposed method has been applied to an EMA with a brushless DC motor affected by a progressive increase of the static eccentricity of the rotor. In order to evaluate the performances of the aforesaid prognostic method a test simulation environment, able to manage different failure modes, has been defined. This numerical test case simulates the dynamic behaviors of the EMA taking into account nonlinear effects related to different kinds of progressive mechanical failures (such as transmission backlash, friction, and rotor static eccentricity). Results show that the method exhibit adequate robustness and a high degree of confidence in the ability to early identify a malfunctioning, minimizing the risk of fake alarms or unannounced failures