DC Motor Control Research Articles

Optimizing the control of a brushless DC motor

Wydawnictwo SIGMA-NOT wydaje czasopisma fachowe informujące swoich czytelników o najnowszych osiągnięciach naukowych i nowoczesnych rozwiązaniach technicznych w Polsce i na świecie, popularyzuje problemy techniczne oraz poszerza wiedzę i kulturę techniczną.

An optimized design modelling of PV integrated SEPIC-based four-switch inverter for sensorless PMBLDC motor control

The design of PV-based high gain SEPIC converter integrated with four-switch strategy, which has been used to achieve sensorless speed control of Permanent magnet Brushless DC motor (PMBLDC) is analysed in this work. Hence SEPIC converter coupled with Fuzzy Logic, MPPT Algorithm is employed to retain voltage. SEPIC converter is chosen as it has a continuous current operation with high gain; Fuzzy MPPT algorithm is used as it provides accurate results faster while the classical MPPT techniques provide the results with fluctuations in attaining the maximum power. Regarding the sensorless control of PMBLDC motor, the conventional six-switch strategy is replaced by four-switch strategy and the sensors are replaced by back EMF method. Four-switch strategy has the capability of reducing the losses, size, cost and complexity of control. For achieving the nominal speed, a closed-loop control is implemented with PI controller, which is tuned by GWO technique. The proposed methodology is more efficient as the motor speed remains unchanged even under the full load condition. The end result of traditional PI algorithm and PI algorithm, which have been tuned by GWO algorithm, is compared and simulated through MATLAB. This is also implemented and validated in hardware by FPGA Spartan 6E controller.

Development of a Solar Powered DC Drive for Rickshaw

The solar powered DC drive for a rickshaw is an automobile that uses solar energy to drive a DC motor, which in turn move a rickshaw. Due to the challenge of global warming, it has become necessary to make use of power sources that are environmentally friendly or renewable energy. Solar energy is used as the energy source in this design because it is cheap, clean and readily available during the day. This paper employed the use of solar PV which converts the solar energy into electrical energy. The electrical energy generated by the solar PV comes in the rated value of 12 V or 24 V which increases or decreases beyond this rated values depending on the sun intensity. To make use of the varying solar PV voltage, there is need to either buck (stepdown) or boost (step up) or buck-boost (stepdown-step up) the PV voltage to a constant voltage value. Since 12 V DC drive is used, it is required to step up or step down from the voltage range of 10 V to 25 V to 12 V via a buck-boost converter to power the 12 V DC drive. The DC motor control was implemented by embedding the functions of vehicle control in the system. The functions are; run, stop or break, left and right. This function makes the DC drive to control the rickshaw as vehicle. The results shows that rickshaw can be control and driven as a vehicle using solar energy.

Fuzzy algorithm implementation in microcontroller for DC motor control

In industrial automation, motor control technique plays the vital role. Motor consists of inductor or electromagnet. Causing inductor or electromagnet, magnetic inductions are produces which resists any change of motor speed. Hence, according to set point, precise speed control is challenging. However, using various control technique can be controls the speed of DC motor. The aim of the present paper is to implement hardware and control the speed of DC motor using embedded fuzzy logic. Set point have been applied externally and recorded the speed of motor through opto-isolator sensor module. In the hardware of DC motor control keypad, 2x16 LCD, DC motor driver and opto-isolator module are interfaced to PIC microcontroller. The Fuzzy algorithm is embedded in the microcontroller wherein input fuzification signals ‘error (Δe)’ and ‘change in error (e(n))’ and output fuzification signal ‘PWM’. The both of inputs of fuzzy algorithm are varied and record output of fuzzy algorithm which is PWM. Moreover, the hardware implementation has been tested for real time control of DC Motor.

Hands on Remote Learning Using a DC Motor Controller

An Electrical and Computer Engineering Laboratory exercise has been included in the junior year fall term to partially bridge the theory to practice gap for multiple basic electrical and computer engineering topics. The laboratory investigates facets of power electronics and computer control for students who have recently been introduced to the theory of transistor bridge circuits for voltage polarity control in their electronics class, and digital pulse width modulation for DC motor speed control in their first computer logic class. After brief study of its specification, students follow a lab sequence to set up and instrument a commercial H-bridge to first record polarity and magnitude of output voltages under specified input conditions, and subsequently to observe DC motor response to the same inputs. DC power polarity and averaging through Pulse Width Modulation (PWM), as well as the collateral realities of brushed DC motor noise and logic versus power supply levels become clear to most student for the very first time.

Virtual DC Motor Stability Control Method Considering New Energy Generation Fluctuation

With the rapid development of new energy generation, the intermittence and randomicity of its power output will have a significant impact on the transmission capacity of DC motor. Therefore, a virtual DC motor stability control method considering the fluctuation of new energy generation is proposed. The natural frequencies and modes of the virtual DC motor shafting rotor are analyzed by means of a steady sinusoidal excitation at zero speed. Considering the transient dynamic response of the shafting rotor of virtual DC motor under the fluctuation of new energy generation, Taylor series and transfer acceleration matrix method are used to calculate the transient dynamic response of shafting rotor under the fluctuation of new energy generation, and the parameters of virtual DC motor are identified and estimated. Based on this, a proportional resonance controller is designed to realize the stability control of virtual DC motor. Experimental results show that the interactive power curve between virtual DC motor and regional distribution network is smoother after optimal control, and this method can effectively improve the power balance ability of virtual DC motor.

Design analysis and control of a permanent magnet brushless DC motor in micro electric vehicle

ABSTRACT This paper analyses the performance of permanent-magnet brushless DC motors, simulates the influence of motor parameters on the ripple torque and average torque, compares the performance with that of permanent-magnet synchronous motors, and obtains a design for permanent-magnet BLDC motors. The BLDC motor has the advantages of high torque density and small inverter capacity and is better than PMSM in driving performance.

Monitoring and controlling the speed and direction of a DC motor through FPGA and comparison of FPGA for speed and performance optimization

<span lang="EN-US">We are living in the 21<sup>st</sup> century, an era of acquiring necessity in one click. As we, all know that technology is continuously reviving to stay ahead of advancements taking place in this world of making things easier for mankind. Technology has been putting his part in introducing different projects as we have used the field programmable gate arrays (FPGAs) development board of low cost and programmable logic done by the new evolvable cyclone software is optimized for specific energy based on Altera Cyclone II (EP2C5T144) through which we can control the speed of any electronic device or any Motor Control IP product targeted for the fan and pump. Altera Cyclone FPGAs’ is a board through which we can monitor the speed and direction of the DC motor. As we know how to make understand, dynamic analog input using an A-to-D convertor and we know how to create pulse width modulation (PWM) output with FPGA. Therefore, by combining these two functions we can create an FPGA DC motor controller. Our paper is divided into three parts: First, all of us will attempt to imitate the issue and can try to look for its answer. Secondly, we will try to verify the solution for real-time. In addition, in the last step, we will verify the solution on the real-time measurements.</span>

Web-Based DC Motor Speed Design and Control

This research discusses about web based speed control of DC motor. This research is a development research. The development that is design and assemble a speed control of DC motor so that becomes function work well. The result of research is speed control of DC motor and monitoring value of voltage, current and rotation speed from DC motor. Speed control of DC motor using PWM on the NodeMCU while monitoring of voltage, current and rotation speed from DC motor using voltage sensor, current sensor, and inductive proximity sensor. The results show that the system is great working. On the readings of the voltage value using voltage sensor and multimeter digital, there is a difference value between 0,01 to 0,13 Volt. While for the readings of the current value using current sensor ACS712 and multimeter digital, there is a difference average 0,01 Ampere. Proximity sensors are used by monitoring of the rotation speed of DC motor. The result is this sensor is working and function well.
 Penelitian ini membahas tentang pengendalian kecepatan motor DC menggunakan web. Penelitian ini merupakan penelitian pengembangan. Pengembangan yang dilakukan ialah merancang dan merakit sebuah web dan alat pengendali kecepatan motor DC sehingga menjadi satu kesatuan dan dapat berfungsi dengan baik. Hasil dari penelitian ini adalah mengatur kecepatan motor DC serta monitoring nilai tegangan, nilai arus dan nilai kecepatan putaran pada motor DC. Pengendalian kecepatan motor DC dilakukan dengan mengatur PWM pada NodeMCU sedangkan monitoring tegangan, arus dan kecepatan putaran motor DC menggunakan sensor tegangan, sensor arus ACS712 dan sensor proximity induktif. Hasil penelitian menunjukkan bahwa sistem bekerja dengan baik. Pada pembacaan tegangan menggunakan sensor tegangan dan multimeter digital terdapat selisih antara 0,01 sampai 0,13 Volt. Sedangkan untuk pembacaan nilai arus menggunakan sensor arus ACS712 dengan multimeter digital, terdapat selesih rata-rata 0,01 Ampere. Sensor proximity digunakan sebagai monitoring kecepatan putaran pada motor DC. Hasil menunjukkan bahwa sensor ini bekerja dengan baik sesuai dengan fungsinya.

Implementation of speed control of sensorless brushless DC motor drive using H-infinity controller with optimized weight filters

<span>The hardware implementation of sensorless brushless direct current motor drive incorporating H-infinity control strategy with optimized weights by particle swarm optimization in the speed control is carried out in this work. The methodology involved in the design of brushless direct current (BLDC) motor control with sensorless position detection technique, the design of H-infinity speed controller, steps involved in particle swarm optimization for optimizing coefficients of its weights and the hardware implementation is discussed in detail in this paper. Texas Instruments microcontroller board C2000 Delfino Launchpad LAUNCHXL F28377S and driver BOOSTXL DRV8301 are used for realization of the speed controller. The code is developed using C2000 hardware support package in MATLAB/SIMULINK platform. A comprehensive performance analysis is accomplished during starting of the motor and during the fast application and removal of load. This strategy is found to be robust resulting in faster load disturbance rejection and better reference speed tracking. The experimental results of the proposed strategy are compared with that of conventional proportional-integral (PI) controller. The time domain parameters are also compared. It is found that the proposed strategy exhibits better performance characteristics during transients and sudden disturbances in load.</span>

Research on brushless DC motor control system based on Fuzzy Adaptive PID control

Conventional PID control is often difficult to achieve ideal control effect when the actual object is nonlinear, time-varying uncertainty and strong interference. In order to make brushless DC motor have faster response speed and higher anti-interference performance, a fuzzy adaptive PID control speed and current double closed-loop system is proposed. The simulation model of BLDCM is built in Simulink and the dynamic simulation is carried out. The simulation results show that the fuzzy adaptive PID control of Brushless DC motor has better robustness, response speed and anti-interference ability than the conventional PID control.

Characteristic of Fuzzy, ANN, and ANFIS for Brushless DC Motor Controller: An Evaluation by Dynamic Test

Brushless DC (BLDC) motors are the most popular motors used by the industry because they are easy to control. BLDC motors are generally controlled by artificial controls such as Fuzzy Logic Controller (FLC), Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS). However, the performance of the BLDC control system in previous studies was compared separately with their respective parameters, making it difficult to evaluate comprehensively. Therefore, in order to investigate the characteristic performance of Fuzzy, ANN, and ANFIS, this article provides a comparison of these artificial controls. Two scenarios of the dynamic tests are conducted to investigate control performance under constant torque-various speed and constant speed-various torque. By dynamic testing, characteristics of Fuzzy, ANN, and ANFIS can be observed as real applications. The testing parameters are: Settling Time, Overshoot and Overdamp (in the graph and average value), and then statistic performance are: Integral Square Error (ISE), Integral Absolute Error (IAE), Integral Time Absolute Error (ITAE), and Mean Absolute Error (MAE). The test result in scenario 1 showed that the ANN has a better performance compared to other controllers with the MAE, IAE, ITAE, and ISE value of 31.3003; 105.6280; 208.0630; and 5,7289 e4, respectively. However, in scenario 2, ANN only has a better performance compared to other controllers on just a few parameters. In scenario 2, ANN is indeed able to maintain speed but it has a more ripple value than ANFIS. Even so, the ripple that occurs in ANN does not have too much value compared to the setpoint. Therefore, the MAE value of the ANN is smaller than the ANFIS (18.8937 of ANN and 28.4685 of ANFIS).

Sensorless Speed Control of Brushed DC Motor Based at New Current Ripple Component Signal Processing

Signal processing of the brushed DC motor current was developed in this paper to obtain information about a rotor speed from a measured motor current. The brushed DC motor current contains a signal with a frequency proportional to the rotor speed. This signal is the outcome of a commutation process occurring in the brushed DC motor, and it is called a ripple component. Since the number of ripples in the measured motor current per one rotation is constant, the rotor speed can be estimated. A discrete bandpass filter with a floating bandwidth was developed as the main part of signal processing. This new interpretation of the bandpass filter was used to extract a frequency of the ripple component from the measured motor current. This frequency was used to acquire information about the estimated rotor speed. The estimated speed was set as a feedback value to a cascade control structure to provide sensorless speed control. The advantages and limitations of this approach are presented in this paper. Based on simulations and experimental results, it was confirmed that the proposed sensorless speed control is robust, accurate, and works precisely in a wide range of speeds.

Systematic design of multi-objective enhanced genetic algorithm optimized fractional order PID controller for sensorless brushless DC motor drive

PurposeThe puspose of this paper, a novel systematic design of fractional order proportional integral derivative (FOPID) controller-based speed control of sensorless brushless DC (BLDC) motor using multi-objective enhanced genetic algorithm (EGA). This scheme provides an excellent dynamic and static response, low computational burden, the robust speed control.Design/methodology/approachThe EGA is a meta-heuristic-inspired algorithm for solving non-linearity problems such as sudden load disturbances, modeling errors, power fluctuations, poor stability, the maximum time of transient processes, static and dynamic errors. The conventional genetic algorithm (CGA) and modified genetic algorithm (MGA) are not very effective in solving the above-mentioned problems. Hence, a multi-objective EGA optimized FOPID (EGA-FOPID) controller is proposed for speed control of sensorless BLDC motor under various conditions such as constant load conditions, varying load conditions, varying set speed (Ns) conditions, integrated conditions and controller parameters uncertainty.FindingsThis systematic design of the multi-objective EGA-FOPID controller is implemented in MATLAB 2020a with Simulink models for optimal speed control of the BLDC motor. The overall performance of the EGA-FOPID controller is observed and evaluated for computational burden, time integral performance indexes, transient and steady-state characteristics. The hardware experiment results confirm that the proposed EGA-FOPID controller can precisely change the BLDC motor speed is desired range with minimal effort.Research limitations/implicationsThe conventional real time issues such as nonlinearity characteristics, poor controllability and stability.Practical implicationsIt is clearly evident that out of these three intelligent controllers, the EGA optimized FOPID controller gives enhanced performance by minimizing the time domain parameters, performance Indices error and convergence time. Also, the hardware experimental setup and the results of the proposed EGA-FOPID controller are presented.Originality/valueIt shows the effectiveness of the proposed controllers is completely verified by comparing the above three intelligent optimization algorithms. It is clearly evident that out of these three intelligent controllers, the EGA optimized FOPID controller gives enhanced performance by minimizing the time domain parameters, performance Indices error and convergence time. Also, the hardware experimental setup and the results of the proposed EGA-FOPID controller are presented.

Speed and Position Control of DC Motor using LabVIEW

New applications requiring both speed and position control have emerged as a result of technological advancements. In many applications, a DC motor's speed and position can be adjusted. The PID controller controls the speed and position of individually stimulated DC motors. Using LabVIEW software, this study attempts to control the speed and position of a DC motor as well as calculate the closed loop system model. Nonlinearity in any system has undesirable implications in normal operation. Unwanted outcomes including dead zones, saturation, and system reaction.

Neural network based output feedback control for DC motors with asymptotic stability

Measurement noise and unknown dynamics including friction, parametric uncertainties, external disturbances and unmodeled dynamics, broadly exist in any electromechanical system and worsen their control performance. To address this issue, this paper develops an output feedback control scheme with neural network (NN) based unknown dynamics compensation for DC motor systems. First, to avert using the noise-polluted velocity signal, a state observer with an online adapted gain is adopted, which attenuates the impact of measurement noise on tracking accuracy and reduces the conservatism of observer gain selection. Second, one NN with low computation and ease to design, is employed for unknown dynamics compensation, which is beneficial for practical applications. Then a composite control law is constructed to achieve high-accuracy tracking performance. The stability analysis reveals the tracking error can asymptotically converge to zero while facing time-variant unknown dynamics. Results of comparative experiments validate the superiority of the presented method.

Implementing optimization of PID controller for DC motor speed control

The point of this paper presents an optimization technique which is flexible and quick tuning by using a genetic algorithm (GA) to obtain the optimum proportional-integral-derivative (PID) parameters for speed control of aseparately excited DC motor as a benchmark for performance analysis. The optimization method is used for searching for the proper value of PID parameters. The speed controller of DC motor using PID tuning method sincludes three types: MATALB PID tunner app., modified Ziegler-Nicholsmethod and genetic algorithm (GA). PID controller parameters (Kp, Ki and Kd) will be obtained by GA to produce optimal performance for the DC motor control system. Simulation results indicate that the tuning method of PID by using a genetic algorithm is shown to create the finest result in system performance such as settling time, rise time, percentage of overshoot and steady state error. The MATLAB/Simulink software is used to model and simulate the proposed DC motor controller system.

Параметрическая оптимизация системы автоматического регулирования частоты вращения вала двигателя постоянного тока судна

DC motor speed control can be done either manually or by using an automatic controlling tool. In order to perform parametric optimization of a DC motor rotational speed automatic control system, we have developed a simplified methodology. The said methodology is able to obtain a transfer function for an automatic control system being based on several initially given system parameters. It is a tree-step way according to the research goals. We offer model patterns where parameters can be designated. Time constant for a PI controller is being presented by a process time constant for a controlled DC motor for the purpose of this particular research.

ATOM SEARCH OPTIMIZATION – NEURAL NETWORK FOR DRIVING DC MOTOR

DC motor applications are very widely used because DC motors are very suitable for applications, especially control. Thus, a proper DC motor controller design is required. DC motor speed control is very important to maintain the stability of motor operation. A recent type of metaheuristic algorithm that mimics the motion of atoms is introduced. Atom search optimization (ASO) is a mathematical model and duplicates the behavior of atoms in nature. Atoms intercommunicate with each other via the delivering contact force in the form of the Lennard-Jones potential and the constraint force produced from the potential bond length. The algorithm is simple and easy to be applied. In this study, the atomic search optimization (ASO) algorithm is proposed as a speed controller for the control dc motor. First, the ASO proposed by the algorithm is applied for the optimization of the neural network. Second, the ASO-NN proposal was the result compared to other algorithms. This paper compares the performance of two different control techniques applied to DC motors, namely the ASO-NN and proportional integral derivative (PID) methods. The results show that the proposed method has effectiveness. The calculation of the proposed ASO-NN control shows the best performance in the settling time. The ASO-NN method has the capability of settling time 0.04 seconds faster than the PID method.

Adaptive Neuro Fuzzy Technique for Speed Control of Six-Step Brushless DC Motor

The brushless DC motors with permanent magnets (PM-BLDC) are widely used in a miscellaneous of industrial applications. In this paper, The adaptive neuro fuzzy inference system (ANFIS) controller for Six-Step Brushless DC Motor Drive is introduced. The brushless DC motor’s dynamic characteristics such as torque , current , speed, , and inverter component voltages are showed and analysed using MATLAB simulation. The propotional-integral (PI) and fuzzy system controllers are developed., based on designer’s test and error process and experts. The experimential and hardware resuts for the inverter- driver circuits are presented. The simulation results using MATLAB simulink are conducted to validate the proposed (ANFIS) controller’s robustness and high performance relative to other controllers.