Speed Control Of DC Motor Research Articles

An enhanced whale optimized fractional order piλ controller based power quality improvement for sensorless brushless dc motor under dynamic operating conditions

This paper presents an Enhanced Whale Optimization Algorithm (EWO) approach for tuning to perfection of Fractional Order Proportional Integral and integral order Controller (FOPI λ) is used to sensorless speed control of permanent magnet Brushless DC (PMBLDC) motor under the operating dynamic condition such as (i) speed change by set speed command signal (ii) varying load conditions, (iii) integrated conditions and (iv) controller parameters uncertainty. On the other hand, it deals with a reduced THD (Total Harmonic Distortion) under dynamic operating conditions to improve the power quality for the above control system. Here present are three optimization techniques, namely (i) Enhanced Whale Optimization (EWO), (ii) Invasive Weed Optimization (IWO), and (iii) Social Spider Optimization (SSO) for fine-tuning of the FOPI λ controller parameters with reduction of THD. The proposed optimization algorithm optimized FOPI λ controller are compared under various BLDC motor operating conditions. Based on the results of MATLAB/Simulink models, the proposed algorithms are evaluated. Here, both the simulation and the results of the experiments are validated for the proposed controller technique. It demonstrates that the effectiveness of the proposed controllers is completely validated by comparing the three intelligent optimization techniques mentioned above. The EWO optimized FOPI λ controller for speed control of sensorless PMBLDC motor clearly outperforms the other two intelligent controllers by minimizing the time domain parameters, THD, performance Indices error, convergence time, control efforts, cost function, mean and standard deviation.

DC 모터 속도 제어시스템을 위한 내부모델 원리 기반 제어기와 외란 관측기 기반 제어기의 등가 조건

Asymptotic tracking of a reference model and nonlinear disturbance rejection are two of the most important attributes of an effective control system. A DOB-based controller, which is designed based on an augmented system model including the disturbance dynamics, relies mainly on the disturbance estimation to provide robust reference tracking performance. While IMP-based controller design technique achieves the same result by including a dynamic model of the reference and disturbance in the control system model. However, the similarity of these two control techniques extends beyond basic design consideration. This paper presents an analytical study that reaches an equivalent condition between the two classes of controllers for a DC motor speed control problem. Based on the obtained condition, it is proposed that the DOB-based controller can be designed such that it shows the same performance as the IMP-based controller. This has been further verified through a simulation which shows the same robust performance in both cases.

Numerical Simulation and Experimental Verification of Fractional-Order PIλ Controller for Solar PV Fed Sensorless Brushless DC Motor using Whale Optimization Algorithm

This article presents a novel Whale Optimization Algorithm (WOA) approach for optimal tuning of Fractional-Order Proportional Integral and integral-order Controller (FOPIλ) is used to solar Photo-Voltaic (PV) fed sensorless speed control of Permanent Magnet Brushless DC (PMBLDC) motor under the various operating condition such as (i) speed change by set speed command signal, (ii) solar PV output voltage variations, (iii) varying load conditions, (iv) integrated conditions, and (v) controller parameters uncertainty. The PMBLDC motor is remarkably robust speed control against the uncertainties, Solar PV output power fluctuation, external load disturbances, the nonlinearity characteristics, and poor controllability. To overcome this above limitation, optimization algorithms are proposed namely Bat Algorithm (BA), Grey Wolf Optimization (GWO), and WOA optimized FOPIλ controllers. To develop a MATLAB 2019a/Simulink model of FOPIλ controller for Solar PV-fed sensorless speed control of PMBLDC motor using BA, GWO, and WOA and corresponding numerical simulation results are carried out under various working conditions. It shows the effectiveness of the proposed controllers is completely verified by comparing the above three intelligent optimization techniques. It is evident that out of these three intelligent controllers, the WOA-optimized FOPIλ controller for solar PV-fed sensorless speed control of PMBLDC motor gives enhanced performance by minimizing the time domain parameters, performance indices error, convergence time, control efforts, cost function, mean, and standard deviation.

Speed Control of DC Motor with MRPID Controller in the Presence of Noise

Speed Control of DC Motor with MRPID Controller in the Presence of Noise

MRAS Based Speed Control of DC Motor with Conventional PI Control — A Comparative Study

MRAS Based Speed Control of DC Motor with Conventional PI Control — A Comparative Study

A Theoretical Demonstration for Reinforcement Learning of Pi Control Dynamics for Optimal Speed Control of Dc Motors by Using Twin Delay Deep Deterministic Policy Gradient Algorithm

A Theoretical Demonstration for Reinforcement Learning of Pi Control Dynamics for Optimal Speed Control of Dc Motors by Using Twin Delay Deep Deterministic Policy Gradient Algorithm

Steady state VEP-based BCI to control a five-digit robotic hand using LabVIEW

This paper proposes steady state visual evoked potential signals for control of five digit robotic hand using LabVIEW as software platform. The experimental setup consist of Ag/Agcl electrodes with 10-20 gel, a low cost, rechargeable battery operated EEG amplifier, a handmade simulation panel flickering at a frequency of 21 Hz with light emitting diode as source, LabVIEW as software platform to implement wavelet analysis for feature extraction and linear discriminant analysis for classification and NI USB-DAQ to provide an interface between EEG acquisition and robotic hand. A state machine chart algorithm using PWM technique is implemented for speed control of miniature metal gear DC motor with 71 RPM, positioned in robotic hand. The experiment was carried out for five different subjects with each subject undergoing five trials, of which in each trial, subject undergoes two recordings of SSVEP signals. Experimental results indicate that the subject's SSVEP signals were used to control the robotic hand to pick up a woollen ball in achieving an accuracy of 84% and mean time of 44.6 seconds.

Steady state VEP-based BCI to control a five-digit robotic hand using LabVIEW

This paper proposes steady state visual evoked potential signals for control of five digit robotic hand using LabVIEW as software platform. The experimental setup consist of Ag/Agcl electrodes with 10-20 gel, a low cost, rechargeable battery operated EEG amplifier, a handmade simulation panel flickering at a frequency of 21 Hz with light emitting diode as source, LabVIEW as software platform to implement wavelet analysis for feature extraction and linear discriminant analysis for classification and NI USB-DAQ to provide an interface between EEG acquisition and robotic hand. A state machine chart algorithm using PWM technique is implemented for speed control of miniature metal gear DC motor with 71 RPM, positioned in robotic hand. The experiment was carried out for five different subjects with each subject undergoing five trials, of which in each trial, subject undergoes two recordings of SSVEP signals. Experimental results indicate that the subject's SSVEP signals were used to control the robotic hand to pick up a woollen ball in achieving an accuracy of 84% and mean time of 44.6 seconds.

Artificial intelligence driven speed controller for DC motor in series

Recently a lot of work have been done to implement artificial intelligence controllers in the field of electrical motors. This paper presents a novel speed controller, developed through Reinforcement learning techniques, applied to series dc motors. We emphasize the ease of developed controller in available off the shelf hardware for industrial use. We used the open- source Python package gym-electric-motor [1] for environment setup, pytorch framework for developing the controller and .NET for performance evaluation.

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.

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.

Optimization of DC motor speed control based on fuzzy logic-PID controller

In this paper the PID controller and the Fuzzy Logic Controller (FLC) are used to control the speed of separately excited DC motors. The proportional, integral and derivate (KP, KI, KD) gains of the PID controller are adjusted according to Fuzzy Logic rules. The FLC cotroller is designed according to fuzzy rules so that the system is fundamentally robust. Twenty-five fuzzy rules for self-tuning of each parameter of the PID controller are considered. The FLC has two inputs; the first one is the motor speed error (the difference between the reference and actual speed) and the second one is a change in the speed error (speed error derivative). The output of the FLC, i.e. the parameters of the PID controller, are used to control the speed of the separately excited DC Motor. This study shows that the precisiom feature of the PID controllers and the flexibllity feature of the fuzzy controller are presented in the fuzzy self-tuning PID controller. The fuzzy self – tuning approach implemented on the conventional PID structure improved the dynamic and static response of the system. The salient features of both conventional and fuzzy self-tuning controller outputs are explored by simulation using MATLAB. The simulation results demonstrate that the proposed self-tuned PID controller i.plementd a good dynamic behavior of the DC motor i.e. perfect speed tracking with a settling time, minimum overshoot and minimum steady state errorws.

Making the PI and PID Controller Tuning Inspired by Ziegler and Nichols Precise and Reliable.

This paper deals with the design of a DC motor speed control implemented by an embedded controller. The design is simple and brings some important changes to the traditional Ziegler–Nichols tuning. The design also includes a novel anti-windup implementation of the controller and an integrated noise-reduction filter design. The proposed tuning method considers all important aspects of the control, such as pre-processing of the measured signals and filtering (to attenuate the measurement noise), time delays of the process, modeling and identification of the process, and constraints on the control signal. Three important aspects of designing PI and PID controllers for processes with noisy output on Arduino-type embedded computers are considered. First, it deals with the integrated design of the input filter and the controller parameters, since both are interdependent. Secondly, the method of setting the controllers from step responses by Ziegler and Nichols is modified for the case of digital signal processing (without drawing the tangent), while it recommends the suitability of its modification in terms of the use of both integral and static models. Third, the most suitable anti-windup solution for the given controller structure is proposed. In summary, the paper shows that an appropriate design of the embedded controller can achieve excellent closed-loop performance even in a noisy process environment with limited control signals.

A Study on Pesticide Sprayer Powered by Solar Energy Appropriate for Small Farms

The study was conducted on a knapsacksprayer powered by a DC motor through a 12 V lead acid battery charged from a solar panel (photovoltaic) directed to sun rays. The system was installed and tested in the western desert on a small-scale farm at Frafra Oasis (New val-ley). This study aimed to evaluate and develop a knapsack sprayer powered by a solar energy for overcoming operator fatigue and shortage of traditional energy in Egypt’s western desert. The experiments were conducted during the period which starts in August 2019 till Febru-ary 2020. The main parts of the system consist of photovoltaic (PV) or solar cells, charger controller, rechargeable battery, and DC motor speed control with dimmer and pump which was attached with a tank 20 liters capacity for sucking and pressurizing the liquid to the boom of the sprayer through a flexible hose. The main results of experiments were as fol-lows: The time for recharging the battery com-pletely was found 330 minutes (5.5 hours) to charge completely from 10.55 V – 13.85 V, with current intensity 1.67 Ampere. The bat-tery discharge time was varied due to drop the battery voltage from 13.85 – 10.55 Volts. De-pending on the motor speed, which is the time for discharging the battery was found 4 – 9 hours, when motor pulled 0.85 - 1.85 Amps at 2000 - 3400 rpm and this time at deferent speed is enough to spray area 3 - 6.82 fed/day day work = 6 hours with application rate 67 - 104.7 l/fed. The number of droplets and the area coverage percentage ranges from 30 – 146.33 drops and 13.96 – 27.97% for one cm2 of simulator targets at a speed ranging from 2000 – 3400 rpm, respectively.

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>

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.

Comparison of PI DC Motor Speed Controller and Fuzzy PI DC Motor Speed Controller

Comparison of PI DC Motor Speed Controller and Fuzzy PI DC Motor Speed Controller

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.