Brushless DC Motor Control Research Articles

Analyzing the performance of metaheuristic algorithms in speed control of brushless DC motor: Implementation and statistical comparison.

A brushless DC (BLDC) motor is likewise called an electrically commutated motor; because of its long help life, high productivity, smaller size, and higher power output, it has numerous modern applications. These motors require precise rotor orientation for longevity, as they utilize a magnet at the shaft end, detected by sensors to maintain speed control for stability. In modern apparatuses, the corresponding, primary, and subsidiary (proportional-integral) regulator is broadly utilized in controlling the speed of modern machines; however, an ideal and effective controlling strategy is constantly invited. BLDC motor is a complex system having nonlinearity in its dynamic responses which makes primary controllers in efficient. Therefore, this paper implements metaheuristic optimization techniques such as Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Accelerated Particle Swarm Optimization (APSO), Levy Flight Trajectory-Based Whale Optimization Algorithm (LFWOA); moreover, a chaotic map and weight factor are also being applied to modify LFWOA (i.e., CMLFWOA) for optimizing the PI controller to control the speed of BLDC motor. Model of the brushless DC motor using a sensorless control strategy incorporated metaheuristic algorithms is simulated on MATLAB (Matrix Laboratory)/Simulink. The Integral Square Error (ISE) criteria is used to determine the efficiency of the algorithms-based controller. In the latter part of this article after implementing these mentioned techniques a comparative analysis of their results is presented through statistical tests using SPSS (Statistical Package for Social Sciences) software. The results of statistical and analytical tests show the significant supremacy of WOA on others.

A novel approach to design and analyze fractional order PID controller for speed control of brushless DC motor

A novel approach to design and analyze fractional order PID controller for speed control of brushless DC motor

A robust optimization strategy for brushless DC motor repetitive controller based on H-infinity

The control of Brushless DC (BLDC) motor has many challenges. A large number of harmonics are generated during motor operation, while it is susceptible to external disturbances such as noise. The large torque ripple is also a constraint to the large-scale popularity of BLDC motors in some high-end industries. Taking the BLDC motor control system as the research object, this paper proposes a control method of H-infinity repetitive control. First, the state space expression is derived from the mathematical model of the BLDC motor. Then, the parameters of the low-pass filter in the internal mode link are obtained for the design of the internal mode link in the repetitive control. In addition, the H-infinity repetitive control system for the BLDC motor is constructed and converted into the standard form of the H-infinity system. Finally, bringing the generalized controlled object in the standard form of the H-infinity into MATLAB to derive the compliant compensator. The simulation results show that the proposed method can not only suppress harmonics but also improve the robustness of the BLDC motor control system.

Experimental implementation of Speed Stabilizer Based Field Oriented Control of Brushless DC Motor for Scooter Applications

An electric scooter, as one type of Lightweight vehicles technology, is a motorized vehicle designed for short-distance travel and recreational purposes. It is powered by an electric motor and typically has a rechargeable battery that provides sufficient power to operate the vehicle. Electric scooters are similar to traditional scooters but are much quieter, eco-friendly, and more energy-efficient. They are commonly used as an alternative mode of transportation for commuting, sightseeing, and recreational activities. This work presents an experimental implementation of speed stabilizer of electric scooter. In fact, a constant speed function might be required in a specific case in the operation of the scooter. A Field Oriented Control (FOC) method was chosen to control the speed of 3-phase Brushless DC motor of the scooter using a PIC16F873A Microcontroller through a Driver circuit. The control algorithm is designed to produce pulse width modulation (PWM) signal with the PID controller of the pulse bandwidth equipped with this Microcontroller and compared to the return pulse of the speed sensor in order to create and apply a closed back-feed system for good stability performance of scooter speed at different load values. The results obtained from this work show that the possibility of obtaining a wide range of control of the speed of the scooter at different loads with high reliability.

Performance Investigation of Model Predictive Control for Brushless DC Motor

Performance Investigation of Model Predictive Control for Brushless DC Motor

Performance Comparison and Analysis of Four- and Six-Switch Inverter Controls of BLDC Motor Employing Deep Learning

Robust and effective control of brushless dc (BLDC) motors is paramount in modern-day motion control. The BLDC motor is known for its high speed, high torque, small size, low noise, and equally low maintenance requirements compared to the brushed DC motor. Nowadays, it can be found in the areas of robotics, aerospace, military, and industrial machines, among others. In this paper, two inverter topologies, the threephase six-switch driver circuit (TPSSDC) and the three-phase four-switch driver circuit (TPFSDC), are used to drive and control the speed of the BLDC motor. For the control technique, however, a fitting neural network from the deep learning (DL) toolbox is employed to train and improve the speed performance of the motor. Both TPSSDC and TPFSDC are simulated and tested in MATLAB Simulink, and the resultant output is analyzed graphically and analytically. Graphical observation shows that the TPSSDC control approach is superior in terms of reference tracking and has less ripple, and better rise and settling times when compared to the TPFSDC control approach, however, the TPFDC is considered for its low cost and simple circuitry. Numerically, the TPSSDC also outperforms at 1000 rpm with a 1.685 ms rise time and a 1.420 ms settling time compared to the TPFSDC, which rises at 6.526 ms and settles at 5.237 ms. At 3000 rpm motor speed, the TPSSDC is better, having a rise time of 5.815 ms and settling time of 4.048 ms, when compared to the TPFSDC, which rises at 11.277 ms and settles at 10.067 ms.

Closed-Loop Direct Power Control of Brushless DC Motor in Field Weakening Region

Closed-Loop Direct Power Control of Brushless DC Motor in Field Weakening Region

BLDC Motor based Air Dessert Cooler

Abstract: This paper proposes a new position sensorless drive for brushless DC (BLDC) motors.Growing need for high productivity is placing new demands on mechanisms connected with electrical motors. The demand for low costBrushless DC (BLDC) motor has increased in industrial applications. A simple BLDC motor control algorithm forlow cost motor drive applications using general purpose microcontrollers has been created and presented in this paper. Proposed design will allow the user to rotate the motor either clockwise or counter clockwise direction. Dependingon the rotor position the sensor will give response to the controller circuit. Then the controller circuit will fix the direction of current following to the stator. The design controller circuit is also implemented. The overall design consists of microcontroller circuit, logic gates, switching devices (MOSFET/BJT), BLDC motor, sensors.

Optimization of BLDC-based electric vehicles: Vehicle dynamics modelling through dual-motor approach and designing a novel augmented TLBO algorithm for PID control

The work primarily focuses on increasing the efficiency of EV drive in electric two-wheeler by working on several aspects, such as modulating the vehicle’s design, optimizing the control strategy, and increasing the speed range using a dual-motor approach. The dynamics of electric two-wheeler have been discussed with a mathematical vehicle model and further tuning of several aspects. Besides, this paper also introduces a novel Augmented Teaching and Learning based Optimization (ATLBO) technique designed exclusively to control BLDC motors for the electric two-wheeler vehicle. Besides, the designed technique has been implemented for the widely used commercial e-bike of Hero Company. Most two-wheeler electric motors are mounted on the rear side of the wheel, which has a limited speed range. Therefore, an analysis has been performed to increase the vehicle’s speed range using a dual motor, from 45 km hr−1 to 62 km hr−1, proving to be a viable alternative to a single motor generally used in an electric bike. ATLBO technique has been designed against a conventional TLBO to optimize the proportional-integral-derivative (PID) controller for the speed control of a linear brushless DC (BLDC) motor. The proposed method has several advantages, including ease of implementation, a consistent convergence characteristic, and high computational efficiency. Furthermore, the literature has validated the merits of the presented novel control technique. The only disadvantage of using a dual motor is the initial cost, but the overall cost is moderated in the long-term usage for its augmented performance parameters. The performance parameters of the above technique are analyzed against other optimization techniques like conventional Teaching and Learning based optimization (TLBO), Particle Swarm Optimization (PSO). MATLAB/Simulink models the brushless DC motor and implements ATLBO, TLBO, and PSO algorithms. It has been found that the response obtained from ATLBO is comparatively much faster than other optimization techniques, which supports the motor for quick acceleration as well as more efficient in improving the step response characteristics such as rise time, settling time, and steady-state error in the speed control of a linear BLDC motor.

Design and Implementation of Sensored Brushless DC Motor Control Using dsPIC30F4012 for CW/CCW Bidirectional Rotation

This paper presents the development of BLDC (Brushless DC) motor control based on dsPIC30F4012. The system is designed to control motor rotation in clockwise (CW) and counter clockwise (CCW) directions. There are 3 input pins used to facilitate the control interface, namely ground (GND) pin, control (CTRL) pin and enable (EN) pin. An analog input was assigned as command input for rotation in CW/CCW direction. The CTRL pin input has full-scale of analog input that comprises of three regions, namely CCW rotation input (0V~2.4V), CW rotation input (2.6V~5V), and neutral input (2.5±0.1)V as the deadband. The commutation sequences of six steps for direction of motor rotation, while P-I (Proportional-Integral) controller is used to control the motor current. The control law and commutation sequences are implemented in single chip dsPIC30F4012 microcontroller. To verify the system performance, we test the system implementation to drive BLDC motor and measured current are sent to the host PC through serial communication. From the experimental results it is shown that the realizing P-I controller is good performance in small frequency where the bldc motor current can follow the input commands. The bandwidth of motor control to characterize the performance of designed system is also presented.

Effective speed control of brushless DC motor using cascade 1PDf-PI controller tuned by snake optimizer

This paper introduces a cascade one proportional derivative incorporating filter (1PDf)-proportional integral (PI) controller abbreviated as c-1PDf-PI to deal effectively with the speed control issue of brushless DC (BLDC) motors. Two problems exist with implementing this controller such as iterated integral overflow and derivation-based chattering owing to the noise. The former is resolved by using an equivalent expression for the integral operation, while the latter is addressed by putting a first-order filter on the derivative term. To achieve the best performance from the controller, snake optimizer (SO) is fruitfully employed for optimizing the controller parameters without need for expert knowledge/interpretation. Here, a more reasonable cost function to assess the candidate solutions is also described. Simulations and laboratory experiments using DSP of TI TMS320F28335 are performed and the results are presented which show that the reference tracking performance, torque disturbance capability and robustness of the c-1PDf-PI controller have potential. These results are also contrasted by those offered by PI and 1PDf speed control schemes individually, affirming the superior performance of our proposal. As per the results, discussion and observation of this research, we stress that good performance and simplicity are salient advantages of the c-1PDf-PI controller, rendering it a good alternative over the complicated controller designs.

Research on motor speed control based on the improved genetic algorithm to optimize fuzzy PID

This study aims to optimize the speed control effect of fuzzy PID controller in brushless DC motor control, and improve the traditional genetic optimization algorithm to improve fuzzy PID algorithm, mainly for genetic algorithm coding, crossover, and mutation process. In the three processes of the genetic algorithm, real number field coding is used, Taguchi orthogonal table is introduced into the cross-process, and Gaussian variation is applied to the mutation process. In the part of model construction and verification, this study uses Simulink software to build a brushless DC motor model and sets the same conventional parameters for simulation. The simulation results show that the algorithm overshoot and stability time of the fuzzy PID controller optimized by the improved genetic algorithm are obviously improved.

Application of Tilt Integral Derivative for Efficient Speed Control and Operation of BLDC Motor Drive for Electric Vehicles

This study presents the tilt integral derivative (TID) controller technique for controlling the speed of BLDC motors in order to improve the real-time control of brushless direct current motors in electric vehicles. The TID controller is applied to the considered model to enhance its performance, e.g., torque and speed. This control system manages the torque output, speed, and position of the motor to ensure precise and efficient operation in EV applications. Brushless direct current motors are becoming more and more popular due to their excellent torque, power factor, efficiency, and controllability. The differences between PID, TID, and PI controllers are compared. The outcomes demonstrated that the TID control enhanced the torque and current stability in addition to the BLDC system’s capacity to regulate speed. TID controllers provide better input power for BLDC (brushless DC) drives than PI and PID controllers do. Better transient responsiveness and robustness to disturbances are features of TID controller design, which can lead to more effective use of input power. TID controllers are an advantageous choice for BLDC drive applications because of their increased performance, which can result in increased system responsiveness and overall efficiency. In an experimental lab, a BLDC motor drive prototype is implemented in this study. To fully enhance the power electronic subsystem and the brushless DC motor’s real-time performance, a test bench was also built.

The Direct Torque Control of Brushless DC Motor Based on Sliding Mode Variable Structure

The Direct Torque Control of Brushless DC Motor Based on Sliding Mode Variable Structure

Decoupling control of bearingless brushless DC motor using particle swarm optimized neural network inverse system

Neural network inverse system decoupling control can effectively solve the nonlinear strong coupling problem of bearingless brushless DC motor, but it has the problem of slow convergence and easy to fall into local extreme value. An Improved Particle Swarm Optimization (IPSO) algorithm has been employed to optimize the initial weights of the BP neural network inverse system. Comparisons between traditional inverse system decoupling control and the proposed method through simulations were conducted to confirm the efficacy and superiority of the proposed decoupling strategy. Furthermore, experiment research indicates that effective decoupling control can be achieved for both speed and radial displacement, as well as between the x and y-axis radial displacements, showcasing the good dynamic decoupling performance and stability of the proposed decoupling control strategy.

Research on the Current Control Strategy of a Brushless DC Motor Utilizing Infinite Mixed Sensitivity Norm

During the brushless DC (BLDC) motor working process, the system encounters inevitable uncertainties. These ambiguities stem from potential fluctuations, random occurrences, measurement inaccuracies, varying operational conditions, environmental shifts such as temperature alterations, among other factors. Uncertainties, an inherent aspect of any real control system, can be broadly classified into two categories: sensor signal uncertainties and discrepancies between the mathematical and actual models due to parameter perturbations. To mitigate the impact of sensor noise and parameter perturbations on the BLDC motor, a robust control strategy utilizing infinite norm mixed sensitivity based on PI control strategy (PI-H∞-MIX) is proposed in this paper. Firstly, the closed-loop control structure and transfer function model of the BLDC motor control system current loop are analyzed based on the current loop circuit topology, and then, the model parameters perturbation is analyzed, and the multiplicative uncertainty bound is given. In addition, the appropriate weighting function is selected to ensure the robustness of the system. In this case, the controller design problem is transformed into the H∞ standard control problem, and then, the system augmentation matrix is established, and the controller is solved by Matlab/Simulink. Finally, the performances of the traditional PI control strategy and the PI-H∞-MIX are compared and analyzed. The results show that (1) the proposed PI-H∞-MIX strategy can improve the control system robustness under the parameter perturbation condition effectively, and (2) the proposed PI-H∞-MIX strategy can suppress the noise signal of the sensor.

Design of Plastic Sealed Brushless Motor Controller Based on Hall Effect

With the rapid development of electronic technology, brushless DC motors (BLDCM) are widely used in various fields such as electronics, industrial control, and the medical field because of their high efficiency, noiselessness, and high speed. However, the traditional BLDCM has large torque and cannot accurately determine the rotor position information. Therefore, this paper proposes the use of a Hall sensor controller to control the BLDCM and carries out the hardware circuit design of the motor driver circuit, Hall circuit, power supply circuit, etc., and the program control of STM32, and carries out the software design of PWM interrupt subroutine, AD picking like the program, etc., to realize the Hall effect sensor. The control of BLDCM is carried out.

Research on Active Disturbance Rejection Control of Brushless DC Motor Based on Vector Control

A Active Disturbance Rejection Control strategy based on vector control is proposed to address the problems of poor speed overshooting immunity in the control system of brushless DC motor. The motor magnetic chain and torque are decoupled by vector control method to simplify the control, and the traditional PI controller in the speed loop is replaced by a active disturbance rejection controller. The transition process is arranged by tracking differential (TD) to smooth the given speed, which overcomes the contradiction between speed overshoot and rapidity, and improves the response capability of the system; a second-order extended state observer (ESO) is introduced, which estimates and compensates the total system disturbance and improves the system's immunity to disturbance; and the nonlinear law state error feedback (NLSEF) is utilized by the Through the nonlinear law state error feedback (NLSEF), the control accuracy of the system is improved by utilizing the nonlinear control principle of "large gain with small error and small gain with large error". The simulation results show that the control strategy proposed in this paper is characterized by fast response, no overshooting, strong resistance to load disturbance, and strong robustness to changes in load and speed, which proves the feasibility and advancement of this strategy.

Parametric Observer Controlled Motor Invariant Electronic Commutation of Photovoltaic Powered BLDCM Without Position Sensor

The development of parameter independent position sensor free control of permanent magnet brushless DC (PMBLDC) motor is one of the most challenging tasks. A motor parameter independent parametric observer based electronic commutation control is developed here without using rotor position sensor. The effect of motor parameter variation along with the change of motor, on electronic commutation control without using the rotor position sensor is considered as the disruption parameter for the developed control algorithm to mitigate the external disruption. Mitigation of control loop disturbances is done using the parametric observer based feedback control. A hybrid maximum power extraction algorithm using traverse and incremental conductance is implemented here for effective solar maximum power extraction. The implemented parametric observer motor invariant (POMI) based position sensor free control algorithm is verified using experimental process. Performance of the implemented POMI control is observed better in performance in comparison with the conventional position sensor free control as parametric variation is not considered in conventional control. The solution proves to be the best fit for any type of BLDC motor based water pumping solution irrespective of the pump configuration.

Optimized Model Torque Prediction Control Strategy for BLDCM Torque Error and Speed Error Reduction System

This paper presents an improved whale optimization algorithm (IWOA) for optimizing the model predictive torque control (MPTC) of brushless DC motor (BLDCM) to further reduce the problems of strong torque pulsation and high ripple caused by the special structure of BLDCM. IWOA adds a randomized convergence factor strategy to the original algorithm, enabling the parameter weights to be adjusted in time. The relative error between the training set and the predicted values is reduced, and a suitable interval is selected for the target. The proposed method takes into account the switching frequency loss factor in the MPTC system of BLDCM, discarding the traditional trial-and-error method and choosing to control the parameter adjustment by the degree of deviation. The IWOA is compared with the popular whale optimization algorithm (WOA), dragonfly algorithm (DA), ant colony optimization (ACO) algorithm, and grey wolf optimization (GWO) algorithm on the MATLAB SIMULINK platform to verify the effectiveness of the method in dealing with improved chain tracking, reduced torque pulsation, and reduced speed error. The simulation results show that IWOA performs well, with an efficiency of 94.32%.