Speed Control Of DC Motor Research Articles

Trainer Kit Module for DC Motor Speed Control Using Voice Commands Based on Arduino

Based on the results of observations made by researchers through direct observation of lecturers in the field of electricity, Electrical Engineering Education Study Program, it was found that there is a need for additional trainers as learning media in the electrical machine control course. This study aims to find out how to design a trainer kit design module for DC motor speed control using voice commands by utilizing the Arduino microcontroller. We also learned the results of testing the design of a trainer kit design module for DC motor speed control using voice commands by utilizing the Arduino microcontroller. The research method researchers use is the Research and Development (R&D) method, which aims to develop new products or innovations that benefit. Based on the results of expert validation, it can be concluded that the Arduino Microcontroller-Based Trainer Kit Practical Module obtained a high percentage of media experts at 96%, material experts at 95.5%, and language experts at 80%. These results indicate that this module gets the "Very Eligible" category to be applied to the Electrical Machine Control course.

DC Motor Speed Control using Particle Swarm Optimization based on Labview

In Industrial applications, DC motors are commonly applied because of high reliability, ease of control and ability to provide accurate speed. However, to get accurate speed control under several operation conditions such as disturbances and changes in the load is significant challenge. This research explores the implementation of particle swarm optimization (PSO) to tune parameters of proportional-integral (PI) controller. PSO that is a population-based optimization technique, is inspired by the social behavior of swarms. It is a population-based optimization technique. By automation process in the algorithm. Using the tuning process of PSO, it can effectively obtain the parameters of PI controller. experimental hardware using DIGIAC 1750 is used to assess the performance of the proposed method. The parameters of and are 0.7492 and 0.2007, respectively. The results show that the performance of the DC motor using PSO tuned by PI for , , and are 0.3687 s, 0.5106 s, and 0.6051 s, respectively. Furthermore, when the system is given a disturbance, the response can come back again following the setpoint and when the setpoint is changed, the response can follow the setpoint quickly as well. The proposed method can address the challenges associated with DC motor speed control.

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.

Design of active disturbance rejection controller for the system with unknown model using Laguerre functions

This paper concentrates on the design problem of linear active disturbance rejection control (LADRC) for uncertain systems, whose order and parameters are unknown. In general, LADRC has diversified structural forms due to different order and parameters selection. To clarify the prerequisites for constructing the LADRC strategy, a Laguerre-based LADRC (LLADRC) method is proposed, which provides controller design guidance for systems with unknown models by capturing the impulse response. Then, intensive attention is focused on establishing internal stability conditions of the feedback system and parameter tuning rules of the prescribed controller. To gain a better understanding, the internal correlation between LLADRC and standard LADRC of the same order is analyzed. It is shown that observer and controller gains of the standard LADRC can be tuned via a workable LLADRC to achieve better control performance. Finally, a numerical example and an application design of DC motor speed control are studied, both of which demonstrate the feasibility and validity of the proposed method.

Efficient DC motor speed control using a novel multi-stage FOPD(1 + PI) controller optimized by the Pelican optimization algorithm.

This paper introduces a novel multi-stage FOPD(1 + PI) controller for DC motor speed control, optimized using the Pelican Optimization Algorithm (POA). Traditional PID controllers often fall short in handling the complex dynamics of DC motors, leading to suboptimal performance. Our proposed controller integrates fractional-order proportional-derivative (FOPD) and proportional-integral (PI) control actions, optimized via POA to achieve superior control performance. The effectiveness of the proposed controller is validated through rigorous simulations and experimental evaluations. Comparative analysis is conducted against conventional PID and fractional-order PID (FOPID) controllers, fine-tuned using metaheuristic algorithms such as atom search optimization (ASO), stochastic fractal search (SFS), grey wolf optimization (GWO), and sine-cosine algorithm (SCA). Quantitative results demonstrate that the FOPD(1 + PI) controller optimized by POA significantly enhances the dynamic response and stability of the DC motor. Key performance metrics show a reduction in rise time by 28%, settling time by 35%, and overshoot by 22%, while the steady-state error is minimized to 0.3%. The comparative analysis highlights the superior performance, faster response time, high accuracy, and robustness of the proposed controller in various operating conditions, consistently outperforming the PID and FOPID controllers optimized by other metaheuristic algorithms. In conclusion, the POA-optimized multi-stage FOPD(1 + PI) controller presents a significant advancement in DC motor speed control, offering a robust and efficient solution with substantial improvements in performance metrics. This innovative approach has the potential to enhance the efficiency and reliability of DC motor applications in industrial and automotive sectors.

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

Achieving Robust and Optimal Speed Control of DC Motor through Sliding Mode Control Tuned by Genetic and Particle Swarm Optimization Algorithms

Achieving Robust and Optimal Speed Control of DC Motor through Sliding Mode Control Tuned by Genetic and Particle Swarm Optimization Algorithms

Comparison Between Conventional PID Controller and Neural Network PID Controller Based on DC Motor Speed Control

DC motor is a multivariable, strong coupling and nonlinear controlled system, and is difficult to setting up the accurate mathematical model. So, it cannot achieve good control effect using traditional PID control method. To tackle this problem ANN based PID controller is proposed in this paper. The objective here is to compare the response of DC motor controlled by two different PID controllers.

RANCANG BANGUN MODUL TRAINER KIT SISTEM KENDALI BERBASIS DIGITAL

A control system is defined as a combination of several components that aim to produce a system response in accordance with the desired process variables. Therefore, control systems are now widely used in various fields such as industry, automotive, public facilities, and household appliances. Especially in the industrial field, control systems play a role in improving production quality and reducing production costs. To create qualified human resources who have a good understanding in this field, universities have an important role in realizing this. One of them is Udayana University through the Electrical Engineering Study Program in the Independent Learning Curriculum in the Control System + Lab Course. This study aims to design a Digital-Based Control System + Lab Trainer Kit Module using Arduio Uno to support the process of implementing practicum in the laboratory. In the trainer kit module, there will be three systems designed, which are DC motor direction and speed control system using L298N motor driver, water heater control system using DS18B20 temperature sensor, and also water level control system using HC-SR04 ultrasonic sensor.

Analysis of the PID Controller Parameters for A Surface Mobility Platform Mobile Robot

Surface mobility control is a fundamental challenge in robotics, and its applications range from autonomous vehicles to industrial automation. The main aim of the paper is to analyze the PID controller parameters for the speed control of a surface mobility platform mobile robot. MATLAB simulation toolbox and mathematical model of the DC motor are used to find the optimal PID controller parameters to complete the DC motor speed control system. For the SMP motion control system, the mobile robot kinematic model is supported as a differential drive system. In this paper, the PID controller parameters for the DC motor control system are analyzed with simulation results using various KP, KI, KD gains. The experimental results of the SMP mobile robot with optimal PID controller parameters are analyzed by driving on the rough, slope and even terrains. According to simulation results and experimental tests, the proposed system effectively controls the speed of the SMP mobile robot, demonstrating satisfactory performance in settling time, rise time, and system overshoot. Among PI and PID controllers, the designed PID controller can quickly control to reach the desired DC motor speed. Using the optimal PID coefficient parameters, all motors reached their desired speed in 0.12 seconds during simulations. In the experimental tests on various terrain (even, slope and rough), all motors achieved their desired speed simultaneously within 25 seconds.Keywords: Differential drive, PID controller, motion trajectory, real-time speed control. skid steering, SMP mobile robot, surface mobility control

DC motor speed control using various Technology Controllers. Review

DC motor speed control using various Technology Controllers. Review

DC motor speed control using boost converter DC-DC chopper type based on the PID controller

Abstract The boost converter with PID-based control is designed to be able to control the speed of the DC motor. In particular, the boost converter based on the PID able to produce output from the control process that is more intelligent, efficient and effective in maintaining the stability of the DC motor rotation. From the results, It can shows the difference and effect of output when using only boost converter and when implemented with PID control. The motor control is carried out with different torque conditions. The expected result is controlling the motor speed by the boost converter based on the PID and its performance at the time of starting as well as to changes in the reference speed of rotation and load according to the set point. From the experiment, it was found that the boost converter can increase the voltage according to changes in load torque and PID control can eliminate overshoot on the system and can reach the reference speed quickly, it only takes 1 second. Based on the data analysis that has been conducted, it can be concluded that the boost converter with PID controller functions quite well to regulate the DC motor speed to remain at the reference speed.

Control system of the electric drive of a robot with a dc motor

Problem. Robotics and flexible production systems are the most important technical basis for the intensification of production. The use of robotic systems is expanding every year. This paper considers a two-circuit servo control system using the example of a DC motor current and speed control system and examines the functional diagram of the third circuit, a digital angle control system. Using the Matlab + SIMULINK software package, the servo drive and the digital system for controlling the angular position of the robot manipulator were modelled. Goal. As a result of the analysis, the purpose of the study was highlighted: the development of a digital system for adjusting the angular position of the manipulator, improve the quality of its positioning. Tasks: to study work management systems; to study the drives of robots; to get a mathematical model of an electric motor; to develop a machine diagram of a digital electric drive; to synthesize a three-loop robot positioning control system. Methodology. The research problems should be solved by methods of analysis and synthesis of automatic control systems. Results. In the work, a three-circuit control system of the electric drive of a robot with a direct current motor was developed. Thus, as a result of modeling the operation of a digital electric drive with three control circuits for current, speed and positioning, an aperiodic transient process was provided with quality indicators. Originality. Thus, the results of simulation of the two-loop servo control system showed that the dynamic characteristics of the electric motor in terms of current and speed of rotation, which meet the requirements of the task, are provided by the PI controller in the current control circuit and the PID controller in the speed control circuit. Thus, as a result of modeling the operation of a digital electric drive with three control circuits for current, speed and positioning, an aperiodic transient process was provided with quality indicators. Practical value. The following results were obtained during the research: the best control law for the current circuit is the proportional-integral (PI) law; the best control law for the speed contour is the proportional-integral-differential (PID) law; the best control law for a digital positioning control loop is the proportional (P) law.

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.

Digital PID Controller based Speed Control of DC Motor

PID controllers are commonly utilised in industry because of their ease of use and adaptability. To adjust these controllers for desirable responses, a number of tuning rules have been developed. This work aims to regulate a robotic vehicle's speed by utilising a PID controller to adjust the motor speed in reaction to impediments. We offer a technique that uses an Arduino microcontroller to automatically tune the PID controller to control the speed of a DC motor. An Arduino Uno device interfaces the DC motor with Simulink and functions as an affordable data collecting device. In order to maintain the motor speed within setpoint limitations, the PID controller modifies it in response to object distance data obtained from sensors. The study suggests utilising a PID controller to regulate the DC motor's angular speed and a microcontroller to implement it in hardware. The L298 motor controller is utilised, the encoder sensor determines angular speed, while the Arduino Uno is utilised for data processing. The proportional controller affects rising time, overestimation, and steady-state errors in the hardware implementation. Overshoot and undershoot are impacted by the integral controller, although overshoot is very slightly impacted by the derivative controller. Top of Form Keywords— Arduino Controller, LCD Display, PID Controller, DC Motor, Motor Driver etc.

A Comparative Study of DC Motor Speed Control Techniques Using Fuzzy, SMC and PID

A Comparative Study of DC Motor Speed Control Techniques Using Fuzzy, SMC and PID

IOT Based DC Motor Speed and Direction Control

This paper introduces an innovative Internet of Things (IoT) based system designed to control the speed and direction of a DC motor using the Blynk mobile application. The system integrates an OLED display (SSD1306), an ESP8266 microcontroller, a 300 RPM DC motor, and an L293D motor driver. With this system, users can remotely manage the speed and direction of the DC motor through the Blynk app, accessible on smartphones or tablets. Real-time feedback on the motor's speed and direction is provided via the OLED display, enhancing user interaction and experience. The ESP8266 microcontroller acts as the core control unit, interfacing with the Blynk app via Wi-Fi to receive user commands and adjust the motor's operation accordingly. The bidirectional control feature of the L293D motor driver enables precise manipulation of the motor's speed and direction. By leveraging IoT technology and the Blynk app, users gain the flexibility to control the motor from any location with internet connectivity, offering convenience and accessibility. Additionally, the OLED display enhances the user interface, enabling intuitive monitoring and adjustment of motor parameters. In conclusion, this system offers a versatile and user-friendly solution for remotely managing DC motor speed and direction, with potential applications spanning across home automation, robotics, and industrial control systems.

Analysis of DC motor for process control application using neural network predictive controller

A DC motor is a critical actuator in process control systems. This study investigates the effectiveness of a deep learning (DL) based Neural Network Predictive Controller (NNPC) for precise DC motor speed control. The NNPC anticipates the motor’s future behaviour based on its current state and control inputs. The controller then optimally generates inputs to minimise tracking errors and enhance system performance. The NNPC demonstrated a remarkable reduction in Mean Squared Error (MSE), achieving a training MSE of 2.75 × 10−14 and the best validation MSE of 9.2023 × 10−14. These quantitative outcomes affirm the reliability and robustness of the proposed NNPC for speed control in DC motor systems across diverse applications.

Design and Implementation of Buck Boost Converter for fuzzy Logic Controller Based DC Motor Speed Control

Design and Implementation of Buck Boost Converter for fuzzy Logic Controller Based DC Motor Speed Control

Development of DC Motor Speed Control Using PID Based on Arduino and Matlab For Laboratory Trainer

DC motors are widely used as propulsions, including in electric bicycles. The problem faced by students in the DC motor control laboratory working using software simulation is that they do not have practical learning experience using digital instruments. This article aims to develop a DC motor speed control that can be used to learn practical Proportional Integral Derivative (PID) control in the laboratory. The DC motor speed control was developed using a combination of Arduino UNO microcontroller and Matlab software. The PID method was used because it is still broadly studied and applied in industries. The test results showed that the developed trainer can work well with PID variable values that can be entered via the keypad, and DC motor transient responses can be displayed in Matlab. From the experimental results, it was found that the optimal PID variable values were Kp=0.04, Ki=0.05, and Kd=0.004, where the controller produced a low overshoot value, i.e., 0.73% of its set point and a settling time of 10.66 seconds. The test results of using the developed trainer in the Fundamental of Control Engineering laboratory work showed that the developed trainer gave students practical learning experience in designing PID control for DC motor speed by using digital equipment, i.e., microcontroller and actual DC motor as well as analyzing its corresponding transient response in Matlab software environment