DC Motor Control Research Articles

RANCANG BANGUN SISTEM KONTROL MOTOR LISTRIK BERBASIS RASPBERRY PI

This study will develop and execute control of DC motors and servo motors on a 4WD robot controlled by a keypad and equipped with grippers and a USB webcam to broadcast video so that the gripper robot vehicle can view items in front of it. Control actions were implemented using Python-based user defined programming on a Raspberry Pi 4 with motor driver controller. The challenge described in this article is how to optimize a tool design utilizing a driver loaded directly on an SBC. The goal of this study is to create technical ideas in the area of electric motor control and mechatronics in the form of applications. The design, production, and testing stages are followed utilizing the Raspberry Pi, RPi Motor Driver Board, and Python as the major components. The study's findings include a prototype wheeled robot with a camera for image capture and an actuator in the form of a servo motor with a gripper/clamp as an attachment. The study's conclusion is that the design outcomes of electric motor control utilizing Raspberry Pi-based can perform more efficiently.

Tilt-fractional order proportional integral derivative control for DC motor using particle swarm optimization

Introduction. Recently, the most desired goal in DC motor control is to achieve a good robustness and tracking dynamic of the set-point reference speed of the feedback control system. Problem. The used model should be as general as possible and consistently represent systems heterogeneous (which may contain electrical, mechanical, thermal, magnetic and so on). Goal. In this paper, the robust tilt-fractional order proportional integral derivative control is proposed. The objective is to optimize the controller parameters from solving the criterion integral time absolute error by particle swarm optimization. The control strategy is applied on DC motor to validate the efficiency of the proposed idea. Methods. The proposed control technique is applied on DC motor where its dynamic behavior is modeled by external disturbances and measurement noises. Novelty. The proposed control strategy, the synthesized robust tilt-fractional order proportional integral derivative speed controller is applied on the DC motor. Their performance and robustness are compared to those provided by a proportional integral derivative and fractional order proportional integral derivative controllers. Results. This comparison reveals superiority of the proposed robust tilt-fractional order proportional integral derivative speed controller over the remaining controllers in terms of robustness and tracking dynamic of the set-point reference speed with reduced control energy.

Design of Double Closed Loop Control System for DC Motor Based on TX8C1010 Single Chip

In order to improve the operational strength and control precision of the DC motor, a double closed-loop control system based on TAIXIN TX8C1010 MCU is proposed. The single chip is based on a high-speed 8051 core, which is applied to home appliances and can realize the double closed-loop motor control algorithm. The given speed value provided by the potentiometer is compared with the real-time speed value and inputs the error to the speed loop function, which is based on the incremental PID algorithm, to calculate the reference current value. Then it is compared with the real-time current value and inputs the error into the current loop. The PWM signal duty cycle is determined by the output value of the current loop to adjust and stabilize the motor speed. The drive circuit outputs the PWM signal in the inverse amplification way to control the operation of the DC motor, and finally tests the function on the MAGTROL M-TEST7 motor test platform. The test results show that the control system has fast response speed, strong load capacity, good robustness and safety. Hence, TAIXIN TX8C1010 single-chip machine is feasible in the DC motor control. It can be improved to strengthen its load characteristics according to the parameters and applied to a variety of power DC motor control.

Fuzzy type two self-tuning technique of single neuron PID controller for brushless DC motor based on a COVID-19 optimization

<span lang="EN-US">This paper presents an efficient interval type 2 fuzzy (IT2F) based on a single neuron proportional–integral–derivative (PID), also known as IT2FSNPID controller. The main purpose of the proposed control technique is to track the motion profile of the brushless DC (BLDC) motor. Also, a comparative study was investigated fuzzy type 1 (FT1) and IT2F. IT2F can treat the uncertainty and nonlinearity of the BLDC motor drive electric system in contrast to FT1. The parameters of each control technique were obtained using a new COVID-19 optimization algorithm according to an objective function. Moreover, several tests had been performed to ensure the ability of fuzzy type to absorb the system uncertainty and nonlinearity. All controllers were utilized to operate the BLDC motor sudden change in load and continuous load. The simulation results show that the IT2FSNPID can improve the dynamic response of linear and nonlinear of the same BLDC motor and accommodate the system uncertainty significantly.</span>

Design of optimal PI controller for torque ripple minimization of SVPWM-DTC of BLDC motor

Conventional direct torque control (CDTC) for brushless DC motor (BLDCM) using proportional integral (PI) controller suffers from torque ripple minimization and speed regulation issues. A novel method in fusion with space vector pulse width modulation (SVPWM) with DTC using optimal PI controller is developed to minimize these issues. In this method, SVPWM replaces switching table and hysteresis controllers in CDTC. To get better performance in steady state, conventional PI controllers are preferred in SVPWM-DTC for BLDCM. However, uncertainty arises due to PI controller tuning as well as load variations. In such a case, optimal PI controller tuned properly can minimize these uncertainties. Here, JAYA algorithm is used to tune controller gain parameters. Simulations of proposed PI controller of SVPWM-DTC for BLDCM are carried away in Simulink. To appreciate the performance of proposed optimal controller of SVPWM-DTC for BLDCM, the simulation results are compared with Conventional PI controller and particle swarm optimization (PSO) technique based tuned PI controller. This proposed technique reduces the toque ripple by 63.1% when compared to conventional PI and 58.5% when compared to PSO-PI controller. It also improves the settling time by 43.9% when compared to conventional PI and 46.79% when compared to PSO-PI controller.

Hybridized GWO-RUN optimized fractional order control for permanent magnet brush-less dc motor

The permanent magnet brush-less dc motor is a nonlinear, multivariable structure and magnetic coupled system wherein the load disturbances and the parameter uncertainties adversely affect the dynamic performance. From this, it required that the designed controller for the system must deal with the complexities. The objective of this paper is to introduce a novel fractional order proportional-integral controller for permanent magnet brush-less dc motor dealing with set parameter tracking problem. In this paper, in order to find the needed optimal gain parameters combination of metaheuristic and numerical optimization algorithm called Grey Wolf-Runge–Kutta algorithm is proposed. The efficacy of the proposed technique is demonstrated by comparing it with existing optimization technique viz. particle swarm, grey wolf, and Runge–Kutta algorithm. Furthermore, the stability and robustness analysis for the proposed controller is also studied for different operating conditions such as set speed variations, load disturbances and parameter variations. The simulation is performed using MATLAB toolbox. From the simulation results it is evident that FOPI controller optimized with novel hybrid algorithm guarantee the best set speed tracking and also capable to ameliorate the system robustness for parameter variations as well as external load disturbances.

Comparison of Machine Learning Algorithms for DC Motor PID Control with Genetic Algorithm

Proportional-Integral-Derivative (PID) is a closed loop control system based on dynamic system feedback commonly employed in industrial control systems that require continuous modulated control. The purpose of this project was to improve the efficacy of PID control, a branch of classical control theory, to allow for the application of PID controllers in more complex and non-linear environments. The research compares the results of different machine learning algorithms with genetic algorithms to optimize PID control parameters to achieve more precise control of 12V DC motors. Genetic Algorithm (GA) is a metaheuristic algorithm used to solve search and optimization problems in machine learning. GAs are developed based on the process of natural selection and genetics, relying on biologically inspired operators. After a series of selections and crossovers, the genetic algorithm selected the fittest generation of PID constants that would result in the optimal PID controller which minimizes system error value and linearizes the system behavior. Optimizing PID parameters to enhance the efficacy of classical control theory could increase machining precision in industrial production and improve the robustness of integrated PID controllers in complex environments such as the human body, enabling PID controllers to be an adaptive, simple, and viable option to be implemented in implantable artificial organs and motorized prosthetics. The results of the study suggest that GA optimized PID parameters are effective in regulating the system behavior of DC motors and the enhanced PID control system could be implemented in a wide range of rapidly changing and non-linear applications.

Artificial Intelligence-Enhanced UUV Actuator Control

This manuscript compares deterministic artificial intelligence to a model-following control applied to DC motor control, including an evaluation of the threshold computation rate to let unmanned underwater vehicles correctly follow the challenging discontinuous square wave command signal. The approaches presented in the main text are validated by simulations in MATLAB®, where the motor process is discretized at multiple step sizes, which is inversely proportional to the computation rate. Performance is compared to canonical benchmarks that are evaluated by the error mean and standard deviation. With a large step size, discrete deterministic artificial intelligence shows a larger error mean than the model-following self-turning regulator approach (the selected benchmark). However, the performance improves with a decreasing step size. The error mean is close to the continuous deterministic artificial intelligence when the step size is reduced to 0.2 s, which means that the computation rate and the sampling period restrict discrete deterministic artificial intelligence. In that case, continuous deterministic artificial intelligence is the most feasible and reliable selection for future applications on unmanned underwater vehicles, since it is superior to all the approaches investigated at multiple computation rates.

Discerning Discretization for Unmanned Underwater Vehicles DC Motor Control

Discretization is the process of converting a continuous function or model or equation into discrete steps. In this work, learning and adaptive techniques are implemented to control DC motors that are used for actuating control surfaces of unmanned underwater vehicles. Adaptive control is a strategy wherein the controller is designed to adapt the system with parameters that vary or are uncertain. Parameter estimation is the process of computing the parameters of a system using a model and measured data. Adaptive methods have been used in conjunction with different parameter estimation techniques. As opposed to the ubiquitous stochastic artificial intelligence approaches, very recently proposed deterministic artificial intelligence, a learning-based approach that uses the physics-defined process dynamics, is also applied to control the output of the DC motor to track a specified trajectory. This work goes further to evaluate the performance of the adaptive and learning techniques based on different discretization methods. The results are evaluated based on the absolute error mean between the output and the reference trajectory and the standard deviation of the error. The first-order hold method of discretization and surprisingly large sample time of seven-tenths of a second yields greater than sixty percent improvement over the results presented in the prequel literature.

Hybrid Gray Wolf Optimization–Proportional Integral Based Speed Controllers for Brush-Less DC Motor

For Brush-less DC motors to function better under various operating settings, such as constant load situations, variable loading situations, and variable set speed situations, speed controller design is essential. Conventional controllers including proportional integral controllers, frequently fall short of efficiency expectations and this is mostly because the characteristics of a Brush-less DC motor drive exhibit non linearity. This work proposes a hybrid gray wolf optimization and proportional integral controller for management of the speed in Brush-less DC motors to address this issue. For constant load conditions, varying load situations and varying set speed situations, the proposed controller’s efficiency is evaluated and contrasted with that of PID controller, PSO-PI controller, and ANFIS. In this study, two PI controller are used to get the more stability of the system based on tuning of their coefficients with meta heuristic method. The simulation findings show that Hybrid GWO-PI-based controllers are in every way superior to other controllers under consideration. In this study, four case studies are presented, and the best-case study was obtained 0.18619, 0.01928, 0.00030, and 0.01233 for RMSE, IAE, ITAE, and ISE respectively.

Modelling and Analysis of Ziegler-Nichols and Chien-Hrones-Reswick Tuning PID on DC Motor Speed Control

There have been many studies on the methods used to control DC motors, one of which is the proportional-integral-derivative (PID) control method. The tuning procedure is carried out by trial and error whose results are not necessarily correct, to overcome this problem an alternative is needed that can achieve a better and faster value for the tuning process. Our contribution is explained the conventional PID (Ziegler - Nichols) and modificated PID (Chien - Hrones - Reswick). We also analyze the response of the speed-controlled DC motor using the Proportional, Integral, and Derivative parameters obtained from the tuning method described above. We also discuss the advantages and disadvantages of each formula of these methods. Implementing both methods using a Matlab, creating a more convenient and user-friendly environment for engineering students and practicing engineers to better understand the formulas of his PID controller tuning method.

Mixed-potential-function-based large-signal stability analysis of DC microgrid with constant power loads

With the development of power grid technology, a large number of constant power loads (CPL) are introduced into the power grid system. The negative damping characteristic of CPL poses a great challenge to the stability of power grid under the condition of large signal disturbance. This paper focuses on the large signal stability and dynamic response characteristics of DC microgrid with CPL. The mixed potential function (MPF) method is adopted to model the large signal characterstic of load converter controlled by the traditional PI control strategy and the new virtual DC motor (VDM) control strategy. The large signal stability criteria and the asymptotic stability region of the DC microgrid system under the two control strategies are derived. The dynamic response characteristics of the DC microgrid are compared under three large signal operation conditions of the load step, load linear change and the load fault. The results show that VDM control strategy can effectively improve dynamic response characteristics of the DC microgrid system compared with those of PI control, and enhance the stability and anti-interference ability of the DC microgrid system.

Practical torque sensorless super-twisting control of manipulators based on a novel integral non-singular fast terminal sliding mode with fixed-time convergence

ABSTRACT In this paper, a robust controller has been proposed for first-order uncertain non-linear systems by introducing a novel hybrid sliding surface. Using this method, contrary to previous proposed sliding surfaces, the state trajectory begins moving on the correct sliding mode and the reaching phase can be reduced to the minimum time. Thus, the transient response speed of the system and consequently the efficiency and robustness of the system increase. Moreover, the convergence of the system state in bounded time, independent of the initial conditions of the system, is guaranteed without singularity problems. Then, for torque sensorless control of permanent-magnet DC motors, as one of the most prevalent joint actuators in robotics, an adaptive super-twisting controller, based on the proposed hybrid sliding surface, has been presented. Using this method, continuous finite-time control of the DC motor’s torque without a torque sensor is realized. The DC motor load-side torque is estimated based on an adaptive wavelet neural network and it is compensated in the control loop. In addition to the stability proof and fixed-time convergence of control schemes, a set of simulation and experimental studies have been provided to evaluate the efficiency of control algorithms and investigate their feasibility.

Sensing System Assisted Novel PID Controller for Efficient Speed Control of DC Motors in Electric Vehicles

PID controllers are widely used in different systems including electric vehicles (EVs) for their simplicity and practical application. However, there are errors between the controlled signal and the desired signal. This system controls EV speed using a sensor feedback-supported novel controller based on a novel dandelion optimization PID controller (DO-PID). This letter aims to reduce system error between the reference and output signals. The results obtained from the proposed controller are compared with classical PID control, firefly-PID controller, teaching learning-based optimization-PID controller, and particle swarm optimization-PID controller. Simulation results show that the novel controller outperforms the existing controllers. Various types of errors are found for all the controllers, and it is seen that the error obtained from DO-PID is less than the error from other controllers. It is found that the integral square error, integral absolute error, integral error, integral time square error, and integral time absolute error in controlling the speed of EV with DO-PID controller are obtained as 0.04161, 0.1115, 0.04022, 0.02068, and 0.02068, respectively. Finally, analysis has been done to check the disturbance rejection capability of the proposed controller.

Optimization of PID Controller Parameters for Speed Control of DC Motor Using Firefly and Fminsearch Algorithms

Optimization of PID Controller Parameters for Speed Control of DC Motor Using Firefly and Fminsearch Algorithms

Retracted: DC Motor Control Technology Based on Multisensor Information Fusion.

[This retracts the article DOI: 10.1155/2022/1447333.].

Improving the Efficiency of DC Motor Control to Adjust the Reflective Surface of the Space-Based Reflector

Improving the Efficiency of DC Motor Control to Adjust the Reflective Surface of the Space-Based Reflector

Speed Control of DC Series Motor PV System via Improved Harmony Search Algorithm

This article introduces the speed adjustment of the DC motor solar system. The developed design matter of the speed controller is established as an optimization problem. Improved Harmony Search Algorithm (IHSA) is employed to seek for optimum Proportional Integral (PI) parameters of speed controller by diminishing the time domain cost function. The performance of the suggested IHSA based speed control of DC motor has been contrasted with Particle Swarm Optimization (PSO) and the traditional PI controller found by Ziegler Nichols (ZN) under diverse operating cases and disturbances. The results of the suggested IHSA are confirmed via time domain analysis, and diverse performance indices. Simulation results have proven the influence of the developed approach in adjusting the speed of the DC motor over PSO and traditional algorithms.

Improved Harmony Search Algorithm for Speed Control of DC Series Motor PV System

This article introduces the speed control of the DC motor solar system. The developed design matter of the speed controller is established as an optimization problem. Improved Harmony Search Algorithm (IHSA) is employed to seek for optimum Proportional Integral (PI) parameters of speed controller by diminishing the time domain cost function. The performance of the suggested IHSA based speed control of DC motor has been contrasted with Particle Swarm Optimization (PSO) and the traditional PI controller found by Ziegler Nichols (ZN) under diverse operating cases and disturbances. The results of the suggested IHSA are confirmed via time domain analysis, and diverse performance indices. Simulation results have proven the influence of the developed approach in adjusting the speed of the DC motor over PSO and traditional algorithms.

Fuzzy PID Speed Controller of DC Motor Based on MATLAB

Because of its few of components and low price, DC motors frequently serve in the field of automatic control. The control accuracy and stability of the DC motor depend on the parameters of the control system, so it is of great significance to design the controller and optimize the control parameters. This paper presents a Fuzzy PID control framework, which successfully works on the performance of DC motor. The controller is based on the basic rules of fuzzy control theory and uses MATLAB simulation to realize its function. To look at the control precision of Fuzzy PID and the dependability of motor activity, Fuzzy PID algorithm and PID algorithm are simulated in MATLAB programming, separately. The simulation data show that the control effect of Fuzzy PID controllor is better than PID controllor in the DC motor control system, so Fuzzy PID controllor is worth popularizing.