Brushless DC Motor (BLDCM) Research Papers (original) (raw)

Sensorless operation of permanent magnet brushless direct current (BLDC) motor drive controls the rotating speed with different applied voltage. No phase lagging is produced which leads to increase the efficiency and minimize the torque pulsation of the BLDC motor. Initially, motor can be started by following the v/f method then allows the sensorless mode after reaching the minimum speed of 500-1000rpm. The Sensorless BLDC motors are highly used due to higher efficiency, reliability power, acoustic noise, smaller, lighter, greater dynamic response, better speed versus torque characteristics, higher speed range and longer life. Thus, the source voltage spikes and switching losses are reduced. This method can be demonstrated through MATLAB simulation and DSP TMS 320LF2407A is used in the experimental setup to get the output.

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- Sensorless Control, Brushless DC Motor (BLDCM)

AC motors have always been an area of interest in the field of electrical drives. With improvements in technology there is always a need of effective utilization of electrical power as well as the available resources. Nowadays the focus is given mainly on the efficiency of these drives with improvement in the performance of the motors used in the drives. Permanent magnet motors are classified as BLDC and PMSM among which Brushless DC Motor is one of the highly preferred AC motors used in various applications due to various advantages offered such as high efficiency, better speed versus torque characteristics. Although BLDC drives has several advantages it generates torque ripples which is a major concern in high precision applications especially in spacecrafts. Even though the torque generated is less when compared to BLDC motors, PMSM generates less torque ripples. Field oriented control of PMSM drives is becoming more popular especially in high precision applications. The main objective of this thesis is to make a comparison study between the FOC control of BLDC motor and PMSM using SVPWM technique in reducing torque ripples in reaction wheels used in satellites. FOC controlled AC motor drives provide better dynamic response and lesser torque ripples. The field oriented control scheme of the BLDC Motor drive and PMSM drive using SVPWM are simulated in MATLAB SIMULINK.

This Paper mainly deals with the implementation of vector control technique using the brushless DC motor (BLDC). Generally tachogenerators, resolvers or incremental encoders are used to detect the speed. These sensors require careful mounting and alignment, and special attention is required with electrical noises. A speed sensor need additional space for mounting and maintenance and hence increases the cost and size of the drive system. These problems are eliminated by speed sensor less vector control by using Extended Kalman Filter and Back EMF method for position sensing. By using the EKF method and Back EMF method, the sensor less
vector control of BLDC is implemented and its simulation using MATLAB/SIMULINK and hardware kit is implemented.

An intelligent control of Doubly Fed Induction Generator (DFIG) system using Proportional-Integral (PI)controller tuned by optimization techniques is proposed in this paper.System identification technique was presented in this work to estimate the transfer function of the reactive power loop and speed loop of the proposed system.An implemented laboratory prototype consists of 0.37kW, 220 V, 50Hz Brushless DC Motor (BLDC) and its drive circuit controlled by voltage source inverter for various wind speed.A 0.27 kW wound rotor induction machine, working as the DFIG, coupled with turbine machine by a coupler and driven through a back-to-back converter. This system can be applied as a stand-alone power supply system or as the emergency power system when the electricity grid fails. The rotor side converter is controlled using the field-oriented control to control the reactive power at different rotor speeds.Grey Wolf Optimizer (GWO) proposed in this study to tune the (PI) controller. Moreover, Particle Swarm Optimization (PSO) is also used to tune the PI controller for comparison. For studying the performance of each algorithm, different case studies are performed, such as step changes in the rotating speed andelectrical load. Experimentalresults showed that the proposed techniqueis adequate and sufficient to be used with off-grid stand-alone DFIG systems. It alsoshowed the improved performance of GWO over the PSOin tuning the PI controller.

This Paper mainly deals with the implementation of vector control technique using the brushless DC motor (BLDC). Generally tachogenerators, resolvers or incremental encoders are used to detect the speed. These sensors require careful mounting and alignment, and special attention is required with electrical noises. A speed sensor need additional space for mounting and maintenance and hence increases the cost and size of the drive system. These problems are eliminated by speed sensor less vector control by using Extended Kalman Filter and Back EMF method for position sensing. By using the EKF method and Back EMF method, the sensor less vector control of BLDC is implemented and its simulation using MATLAB/SIMULINK and hardware kit is implemented.

In this paper, A novel design of an adaptive neuro fuzzy inference system(ANFIS) for controlling some of the parameters, such as speed, torque, flux, voltage, current, etc. of the brushless dc motor is presented.. exact quickened torque control for a brushless dc motor (BLDCM) is accomplished by electromagnetic torque control and influence torque concealment. To start with, the electromagnetic torque ripple is diminished in commutation and conduction areas. In previous case, the ripple is smothered by covering recompense control and improving the obligation proportion of the dynamic controller. In recent, the unbalance ripple caused by the uneven three phase windings is diminished by the proposed asymmetry compensation capacity, the aggravation ripple made by the back electromotive power (EMF) is repaid by feed forward control. Second, the aggravation torque has been observed and compensated through the unsettling influence torque controller whose compensation coefficient is acquired by line-to-line back EMF coefficient estimation. In order to verify the effectiveness of the controller, the simulation results are compared with PI controller. The simulation result show that the overall performance of ANFIS based BLDC motor is much better when compared to PI controller under different operating conditions

- by IJSRD Journal
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- Brushless DC Motor (BLDCM), Electromagnetic Torque, Torque Ripple

This paper presents a low-cost Brushless DC (BLDC) motor drive system with fewer switches. BLDC motors are widely utilized in variable speed drives and industrial applications due to their high efficiency, high power factor, high torque, low maintenance, and ease of control. The proposed control strategy for robust speed control is dependent on two feedback signals which are speed sensor loop which is regulated by Sliding Mode Controller (SMC) and current sensor loop which is regulated by Proportional-Integral (PI) for boosting the drive system adaptability. In this work, the BLDC motor is driven by a four-switch three-phase inverter emulating a three-phase six switch inverter, to reduce switching losses with a low complex control strategy. In order to reach a robust performance of the proposed control strategy, the Lévy Flight Distribution (LFD) technique is used to tune the gains of PI and SMC parameters. The Integral Time Absolute Error (ITAE) is used as a fitness function. The simulation results show the SMC with LFD technique has superiority over conventional SMC and optimization PI controller in terms of fast-tracking to the desired value, reduction speed error to the zero value, and low overshoot under sudden change conditions.

- by Abbas Dairawy
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- Brushless DC Motor, Brushless DC Motor (BLDCM)

This paper presents an enhanced nonlinear PID (NPID) controller to follow a preselected speed profile of brushless DC motor drive system. This objective should be achieved regardless the parameter variations, and external disturbances. The performance of enhanced NPID controller will be investigated by comparing it with linear PID control and fractional order PID (FOPID) control. These controllers are tested for both speed regulation and speed tracking. The optimal parameters values of each control technique were obtained using Genetic Algorithm (GA) based on a certain cost function. Results shows that the proposed NPID controller has better performance among other techniques (PID and FOPID controller).

This paper presents a novel hybrid control of a BLDC motor using a mixed sliding mode and fuzzy logic controller. The objective is to build a fast and robust controller which overcome classical controllers' inconveniences and exploit the fast response of brushless dc motors characterized by an intense torque and fast response time. First the paper study pros and cons of both sliding mode and fuzzy logic controllers. Then the novel controller and its stability demonstration are presented. Finally the proposed controller method is used for the speed control of a BLDC motor 3KW. The obtained results are compared with those of a fuzzy logic and a conventional sliding mode controller. It allows to show performance of the proposed controller in terms of speed response and reaction against disturbances, which is improved more than 5 times without losing stability or altering tracking accuracy.

This paper presents modeling, performance evaluation, and comparative analysis of speed performance of brushless DC motor (BLDCM) by using digital controllers. Speed performance analysis is carried out by using time response specifications which are useful for determining the effectiveness of the digital controllers. The wide spread of BLDCM in many areas due to the advantages of BLDCM over the conventional widely used motors such as induction motor and brushed DC motor. Advantages of BLDCM include higher efficiency, lower maintenance, longer life, reduced losses, single excitation, etc. Controllers are used to improve the transient and steady state speed response of the BLDCM. In many applications conventional PID controller is widely used to control the speed of the BLDCM but the main issue with the conventional PID controller is that it requires manual tuning of the parameters such as proportional, integral, and derivative gain constant. Even though the autotuning methods are available with the PID controller it is not adaptive itself to handle the conditions such as variations in parameters, disturbances in load, etc. In this Paper the Fuzzy-PID controller is used to control the speed of the BLDCM and Transient and steady state speed performance analysis is carried out using conventional PID controller and Fuzzy-PID to showcase the comparative analysis between two controllers. MATLAB/SIMULINK environment is used for modeling of the BLDCM and its drive/control system.