Direct torque control of brushless DC drives with reduced torque ripple

The application of direct torque control (DTC) to brushless ac drives has been investigated extensively. This paper describes its application to brushless dc drives, and highlights the essential differences in its implementation, as regards torque estimation and the representation of the inverter voltage space vectors. Simulated and experimental results are presented, and it is shown that, compared with conventional current control, DTC results in reduced torque ripple and a faster dynamic response

Competence Amelioration of PMBLDC Motor using LQR- PID, Kalman Filter- PID and LQG Based on Kalman Filter-PID optimal Controllers for disturbance attenuation

In this paper, modeling, simulation and performance analysis of the permanent magnet brushless direct current (PMBLDC) motor using classical controller (PID Controller) and optimal controllers ( Linear Quadratic Regulator (LQR) and Linear Quadratic Gaussian (LQG) optimal Controllers Based on Kalman Filter) for disturbance attenuation and noise suppression is presented. The applications of the permanent magnet brushless direct current (PMBLDC) motor are increasing day by day. In order to have proper utilization of these motors and to control them effectively it is important to have proper mathematical modeling of these motors. Similarly effective control these motors are also essential to have successful application of the devices across multiple domains. This paper handles both these important aspects. A mathematical model has been derived to represent permanent magnet brushless direct current (PMBLDC) motor model to study the stability and performance. In order to maintain the stability and to achieve the best performance by reducing disturbance attenuation and noise suppression, the three optimal controllers are developed in this paper. the system performance simulation of these optimal controllers with PID controller is presented using MATLAB program to control the modeled permanent magnet brushless direct current (PMBLDC) motor for disturbance attenuation and noise suppression.. The simulation results show that and Linear Quadratic Gaussian (LQG) Based on Kalman Filter with PID controller  provides best as compared to PID controller, Linear Quadratic Regulator (LQR) with PID controller and Kalman Filter with PID controller

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

Variable gain control of brushless DC motor control with low resolution sensors

In this work, feasibility of using low cost, low resolution sensor for high performance brushless dc (BLDC) motor speed control is investigated. Conventional control, using a tachometer or high resolution encoder, suffers from drawbacks such as high cost, large physical volume, and high sensor processing bandwidth. On the other hand, sensorless BLDC motor, appealing in its hardware simplicity, does not provide sufficient fast performance. Using a standard low resolution sensor, such as a hall sensor or commutation encoder, a compromise between cost and performance can be obtained. However, the use of a low resolution sensor does pose a challenge to the control design: the sensor signal is discrete and speed dependent. Together with the nonlinear drive voltage/speed characteristic of the motor, control of the BLDC motor requires a more advanced algorithm than fixed gain control. This thesis presents a speed dependent control scheme to produce optimal performance. The characteristics of the control scheme is first assessed by numerical simulation, based on the mathematical model of the BLDC motor. This is followed by experimental verification of the BLDC motor. From the available data, it is concluded that speed dependent control provides significant advantages over fixed gain control when low resolution sensor is used

Application of Soft Computing Techniques for Speed Control of Brushless DC Motors

Nowadays Brushless DC (BLDC) motors are treated as the most popular motors, which are applied widely due to their higher efficiency and excellent torque characteristics. Moreover they are operated with DC supply and without using brushes. But BLDC motor operate with wide speed range and therefore it is required to regulate the speed of DC motor using different advanced techniques. In this work, the fuzzy sliding mode control (FSMC) is employed for controlling the speed of BLDC motor. The proposed method is compared with the conventional Sliding mode control (SMC) and the results are analyzed using MATLAB/ Simulink tool

Rotor Position Identification for Brushless DC motor

Permanent magnet BLDC motors are characterized by a central magnetic core, called the rotor, and fixed electric coils (usually six) equally spaced in a ring around the core, called the stator. Motor movement is controlled by alternately energizing and de-energizing the stator coils to create a rotating magnetic field that propels the rotor. In order for this process to work correctly, BLDC motors required a technology called electronic commutation, in which the coil currents must be very carefully synchronized to rotor position to ensure that the rotating field is correctly aligned with the permanent magnetic field in the rotor. Usually rotor position is measured by external sensors such as Hall-effect sensors and optical encoders and these external sensors increase the system cost and reduces reliability. In order to control the price and make it more reliable this thesis propose to infer the rotor position from voltage and current measurement of motor. The most common approaches to sensorless control are based on the measurement of the electromotive force (back-EMF), that is induced by the rotor motion. As the back-EMF is nearly zero at very low speed and at stationary position, and can not be measured. Therefore a separate algorithm is required for start-up and control at low speed. The other method of sensorless control involves the inference of rotor position from the variation in inductance caused by rotor position. This thesis presents a prototype system for sensorless control of BLDC motors over the entire speed range of the motor, including stall (zero speed) conditions using the voltage and current signals from the motor

Adaptive control of sinusoidal brushless DC motor actuators

Electrical Power Assisted Steering system (EPAS) will likely be used on future automotive power steering systems. The sinusoidal brushless DC (BLDC) motor has been identified as one of the most suitable actuators for the EPAS application. Motor characteristic variations, which can be indicated by variations of the motor parameters such as the coil resistance and the torque constant, directly impart inaccuracies in the control scheme based on the nominal values of parameters and thus the whole system performance suffers. The motor controller must address the time-varying motor characteristics problem and maintain the performance in its long service life. In this dissertation, four adaptive control algorithms for brushless DC (BLDC) motors are explored. The first algorithm engages a simplified inverse dq-coordinate dynamics controller and solves for the parameter errors with the q-axis current (iq) feedback from several past sampling steps. The controller parameter values are updated by slow integration of the parameter errors. Improvement such as dynamic approximation, speed approximation and Gram-Schmidt orthonormalization are discussed for better estimation performance. The second algorithm is proposed to use both the d-axis current (id) and the q-axis current (iq) feedback for parameter estimation since id always accompanies iq. Stochastic conditions for unbiased estimation are shown through Monte Carlo simulations. Study of the first two adaptive algorithms indicates that the parameter estimation performance can be achieved by using more history data. The Extended Kalman Filter (EKF), a representative recursive estimation algorithm, is then investigated for the BLDC motor application. Simulation results validated the superior estimation performance with the EKF. However, the computation complexity and stability may be barriers for practical implementation of the EKF. The fourth algorithm is a model reference adaptive control (MRAC) that utilizes the desired motor characteristics as a reference model. Its stability is guaranteed by Lyapunov’s direct method. Simulation shows superior performance in terms of the convergence speed and current tracking. These algorithms are compared in closed loop simulation with an EPAS model and a motor speed control application. The MRAC is identified as the most promising candidate controller because of its combination of superior performance and low computational complexity. A BLDC motor controller developed with the dq-coordinate model cannot be implemented without several supplemental functions such as the coordinate transformation and a DC-to-AC current encoding scheme. A quasi-physical BLDC motor model is developed to study the practical implementation issues of the dq-coordinate control strategy, such as the initialization and rotor angle transducer resolution. This model can also be beneficial during first stage development in automotive BLDC motor applications