Direct Current Motor Speed Research Articles

Investigation of the impact of different PWM frequencies on DC motor control based on 8051 series microcontroller

Abstract Pulse Width Modulation (PWM) is widely used for controlling electronic signal transmission, particularly in direct current (DC) motor control. This paper focuses on the application of PWM in controlling DC motors. The operating principle of PWM reveals that the frequency plays a crucial role in determining the precision of DC motor control. Generally, higher PWM frequencies result in smoother and more accurate motor operation. However, simulation experiments in this paper indicate that excessively high PWM frequencies can disrupt the linear relationship between control precision and frequency, leading to a significant decrease in motor speed. Based on the experiment conducted under Proteus simulation software and using an 8051 series microcontroller to generate PWM signals at various frequencies, this paper explores the impact of PWM frequency on DC motor speed. The analysis of simulated components demonstrates that at high PWM frequencies, the integrated circuit L293D experiences increased switching losses, and higher power dissipated. Additionally, the self-inductance phenomenon in DC motors becomes more pronounced with high-frequency signal transitions. Both the switching losses and self-inductance ultimately contribute to the reduction in motor speed, compromising control precision.

Enhancing DC Motor Speed Control Performance Using Heuristic Optimization and Comparative Analysis of Control Methods

Direct Current (DC) motors are an important component that converts electrical energy into mechanical energy, used in a wide range of applications from industrial applications to home appliances. DC motor speed control has an important role in industrial processes to increase efficiency, realize precise movements and optimize energy consumption. In this study, various control methods and parameter optimization techniques for speed control of DC motors, which have a wide range of applications, have been systematically analyzed. The aim of the study is to develop an effective control strategy to ensure that DC motors reach the determined target speed by monitoring them in real time at different speeds and to minimize fluctuations caused by variable loads or external factors. In our study, Proportional-Integral-Derivative (PID), Proportional-Integral (PI), and Proportional-Derivative (PD) control methods were used. The parameters of these controllers were tuned using Matlab Tuned, The Cheetah Optimizer (CO) Algorithm, a new generation heuristic optimization method, and Particle Swarm Optimization (PSO), a widely accepted optimization method. The performances of the controllers were determined using criteria such as Integral of Absolute Error (IAE), Integral Squared Error (ISE), and Integral of Time multiplied by Absolute Error (ITAE). According to the results obtained, it was found that the PID, PI and PD control parameters determined using the CO Algorithm performed better than the controllers created using Matlab Tuned and PSO methods. New optimization methods, such as the CO Algorithm, have been found to have significant potential to improve the performance of control systems. Thanks to this study, it offers a practical approach for optimizing DC motor speed control in industrial processes. As a result, it has been found that the control parameters determined by the CO Algorithm have significant potential in improving the performance of DC motor speed control and control systems compared to other optimization methods.

Optimization of the different controller parameters via OBL approaches based artificial ecosystem optimization involving fitness distance balance guiding mechanism for efficient motor speed regulation of DC motor

This study proposes a new optimization approach, which is called as artificial ecosystem optimization algorithm with fitness-distance balance guiding mechanism by using opposite based learning methods (FDBAEO_OBLs) for the speed regulation of direct current (DC) motor. The performance of the proposed FDBAEO_OBL algorithm is tested in two different experimental studies. In the first experimental study, the proposed approach is tested in the CEC2020 benchmark test functions and the FDBAEO algorithm, which included the best OBL approach, is determined using non-parametric Wilcoxon and Friedman statistical analysis methods. Second, the parameters of proportional integral derivative (PID), tilt integral derivative (TID), proportional integral derivative with filter (PIDF), tilt integral derivative with filter (TIDF), fractional-order proportional integral derivative (FOPID), fractional-order proportional integral derivative with filter (FOPIDF), proportional integral derivative with fractional-order filter (PIDFF) and fractional-order proportional integral derivative with fractional-order filter (FOPIDFF) controller structures to be used in DC motor closed loop speed control are determined with FDBAEO_OBL, and the performances of the controllers are investigated. Integral absolute error (IAE), integral time absolute error (ITAE), integral time squared error (ITSE) and integral squared error (ISE) performance indices are used as the objective function of the operation process in which the control parameters are determined. According to the comparative step response results of the controller structures, the four best controller structures for DC motor speed regulation are determined. The performances of these controllers are examined under different simulation conditions and according to the results obtained, it is seen that the best controller structure is FOPIDFF. The FDBAEO_OBL algorithm, which is used in both benchmark test functions and DC motor speed regulation, shows an effective, durable and superior performance in finding the optimal solution values during the optimization.

Development of a microcontroller and resistive touchscreen-based speed monitoring and control system for DC motor

<span>Speed control is a key requirement in direct current (DC) motor applications where accuracy, reliability, flexibility, and safety are of high importance. In this study, a microcontroller and resistive touchscreen-based DC motor speed monitoring and control system were developed. The core components employed in the development of the system include Arduino ATMega328P microcontroller, thin film technology (TFT) resistive touch screen, L293D motor driver, and infrared (IR) sensor module. ATMega328P microcontroller is the brain of the system around which the overall circuit design was modeled. TFT resistive touch screen displays the motor speed and also, enables the users to set a desired speed. L293D motor driver regulates the voltage and current supplied to the motor, and a feedback loop comprising an IR sensor module ensures the maintenance of the motor speed at the desired level. A performance test was conducted on the developed system to ascertain its correct functionality. The developed speed monitoring and control system operated satisfactorily during testing; achieving a speed control in the range of 800 to 3000 rpm. The developed device is useful and can be scaled up for various domestic and industrial applications.</span>

Controlling the operation of the dc motor by using pid with metaheuristic technology

This paper presents an investigation into precise trajectory tracking and synchronization of two-axes direct current (DC) motor control, with an emphasis on a cascade proportional-plus-integral (P-PI) controller to regulate the speed and position of a single-axis permanent magnet DC (PMDC) motor. Various methods were explored for the controller’s design process, including classical methods (CM) and three optimization strategies: genetic algorithm (GA), dandelion optimization algorithm (DOA), and butterfly optimization algorithm (BOA), with the latter found to be the most effective. Simulation was a crucial component in assessing the efficiency of these methods. A comparative analysis of four tuning strategies (CM, GA, BOA, and DOA) was conducted to ascertain optimal settings for the P-PI cascade controller. The DOA outperformed the others, providing accurate tracking with no deviation from the reference location or speed overshoot. Moreover, DOA ensured safety by limiting voltage and current to prevent potential damage to the motor. The findings thus suggest that the proposed P-PI controller with DOA can serve as a reliable solution for speed and position control in single-axis PMDC motors.

Speed control of brushless DCmotors using (conventional, heuristic, and intelligent) methods-based PID controllers

One of the most often utilized types of direct current (DC) motors in both the industrial and automotive sectors are brushless DC motors (BLDC). This research presents a comparative analysis on brushless DC motor speed management. A mathematical model of the BLDC motor is developed using MATLAB/Simulink, and its speed is tested using three alternative controller types. The first controller is a traditional proportional integral derivative (PID) controller for BLDC motor speed control. The second controller used the particle swarm optimization (PSO) approach with PID which give the best response for BLDC motor speed. The PID controller in the third method based on neural network also give best reaction on motor speed. Finally, comparison made in speed and torque profiles by using sudden changes in speed and load torque under the three proposed methods. The results show when using first controller the speed rise to 1,526 r.p.m and drop to 1,400 r.p.m at the test conditions. These oscillations will disappear when using the second and third controller.

A concepts and techniques related to the DC motor speed control system design: Systematic Review

The first sources of direct current (DC) were invented, DC machines were one of the first types of electro-mechanical machines used. DC machines are more advantageous over AC machines as regards to Speed regulation and versatility. A DC motor is an electrical actuator with a lot of control that is used in a lot of applications, like robotic manipulators, guided vehicles, steel rolling mills, cutting tools, overhead cranes, electrical traction, and other applications. Due to their speed-torque characteristics and ease of control, DC motors are utilized extensively in industries for demanding variable speed applications. In terms of controller design and implementation, the process control industry has seen numerous advancements over the past two decades. In the industry, there is a great demand for automatic controllers that can respond quickly and accurately to perform precise tasks. The feedback loop is an essential component of system control that must be utilized in order to achieve the desired performance in the majority of systems. Numerous control strategies have been developed for various feedback control systems in order to achieve rapid system dynamic response. Controls in a drive system are crucial if the reference speed is to be accurately and quickly tracked, with little or no steady-state error and as little overshoot as possible. This paper presents the Systematic literature review that was conducted as covers pertinent established concepts and techniques related to the DC motor speed control system design, for applications that require actuators with accurate speed characteristics. Simulation and real time implementation results employed for DC motor speed control systems in various literature are analysed and discussed.

DC Motor Speed Control with Proportional Integral Derivative (PID) Control on the Prototype of a Mini-Submarine

DC (Direct Current) motors are widely used as controllers in the industrial and robotics fields. In the rotation of the DC motor, there is still an unstable rotational speed so that a controller is needed to be able to stabilize the speed at the rotation of the DC motor. The control used in this study used the PID (Proportional Integral Derivative) control method. The PID control system works by processing calculations based on control variables Kp, Ki, and Kd to achieve conditions according to the expected setpoints. To achieve the expected conditions, the trial and error method is used. PID control in this study was implemented on a DC motor with a brushed type using an Arduino Mega microcontroller. The speed of the DC motor is read by the encoder sensor and entered in the PID equation. The output of the PID value will produce data in the form of PWM (Pulse Width Modulation) which will be the input of the L298N driver via Arduino Mega. The DC motor will produce a rotational speed in the form of RPM (Revolution Per Minute) data up to the specified set point. The implementation of PID was produced by giving parameter values to Kp, Ki, and Kd. The best PID parameter usage in this study was in the form of Kp = 0.6; Ki=0.3; and Kd=0.01. The application of the PID parameter obtains a stable system response curve at a predetermined set point. The resulting Kp, Ki, and Kd parameter data is used as graph data in MATLAB software.

Design of a fuzzy logic proportional integral derivative controller of direct current motor speed control

Direct current (DC) motor speed control is useful. Speed can be modified based on needs and operations. DC motors cannot control their speed. To control the DC motor’s speed, a dependable controller is needed. The DC motor speed will be controlled by a fuzzy logic proportional integral derivative controller (FLC-PID). The DC motor circuit’s electrical and mechanical components have been modeled mathematically. Ziegler-Nichols is used to tune the PID controller’s gain parameters. The FLC controller employs 3×3 membership function rules in conjunction with the MATLAB/Fuzzy Simulink toolbox. Real hardware was attached to the simulation to evaluate the DC motor speed control using the fuzzy logic PID controller. DC motors with FLC PID controllers, FLC controllers, and DC motors alone will be compared for the transient response. The DC motor with an FLC PID controller performed better in this study.

Design and Simulation of a PID Neural Network Controller for PMDC Motor Speed and Position Control

Direct current (DC) motors have many difficulties when controlling angular velocity in a variety of applications. The perfect controller cannot be carried out by traditional control alone due to the nonlinear properties of DC motors, design constraints, and mechanical variations caused by the operation conditions. This study proposes a design for an artificial neural network based PID controller (ANNPID) to control the speed of a permanent magnet DC motor (PMDC) in two methods. A detailed analysis is performed based on the simulation results of both methods. The proposed controllers are numerically simulated for various test conditions including; set-point changes, step changes in the load torque, and parameter variations, then the suggested techniques were compared in a comparative study with a traditional PID controller based on the transient response specifications and the performance indices to validate the performance of the controllers. The simulation results demonstrated that the controllers have improved dynamics, static performance, and less overshoot. The methods described here achieve control more effectively than the conventional control approaches under both nominal and disturbed test conditions over different operating ranges.

Kendali Kecepatan Motor DC Menggunakan Pengendali PID dengan Encoder sebagai Feedback

<p><em>PID (Proportional Integral Derivative)</em><em> </em><em>controllers are widely used to improve system response , as they offer clear, simple functionality and eas</em><em>y application</em><em> and use. One of the important roles of the PID controller can be seen in its application to control and stabilize the angular velocity in the reference value of a direct current (DC) motor. The purpose of this </em><em>research</em><em> is to implement a PID controller as a DC motor speed controller so that it can understand the characteristics of each PID controller parameter on the system response curve. The test results show that each PID value parameter (Proportional, Integral, Derivative) has different characteristics to the system response. For example, adding the Kp parameter can reduce the steady-state error, reduce the rise time, but can increase the overshot value. Meanwhile, adding the Ki parameter can affect the increase in overshot, reduce the rise time, and reduce or even almost eliminate the steady state error value. Then, adding the Kd parameter can reduce the overshot, reduce the rise time, and increase the undershot.</em></p>

Design and Implementation of a Four-Quadrant DC-DC Converter Based Adaptive Fuzzy Control for Electric Vehicle Application

Electric vehicles (EVs) provide an excellent opportunity for limiting the emission of a variety of environmentally hazardous gases caused by gasoline and diesel-based vehicles. These propelled vehicles require a forward and backward motion as well as a variable speed operation. Hence, the use of a four-quadrant (4-Q) direct current (DC) converter becomes a necessity. This paper aims to analyse the traction system of an electric automobile along with the improvement of energy efficiency. Inserting a bi-directional DC-DC converter between the battery and the four quadrant-DC chopper assembly allows the power flow from the battery to the motor and the other way around during regenerative braking. Therefore, increasing the limited driving range of the EV. This paper also focused on the application of model reference adaptive fuzzy control (MRAFC) in order to adjust the direct current bus voltage and the DC motor speed. The proposed system has been tested on an experimental bench and the results have been analysed.

Improving Neural Network Based on Seagull Optimization Algorithm for Controlling DC Motor

This article presents a direct current (DC) motor control approach using a hybrid Seagull Optimization Algorithm (SOA) and Neural Network (NN) method. SOA method is a nature-inspired algorithm. DC motor speed control is very important to maintain the stability of motor operation. The SOA method is an algorithm that duplicates the life of the seagull in nature. Neural network algorithms will be improved using the SOA method. The neural network used in this study is a feed-forward neural network (FFNN). This research will focus on controlling DC motor speed. The efficacy of the proposed method is compared with the Proportional Integral Derivative (PID) method, the Feed Forward Neural Network (FFNN), and the Cascade Forward Backpropagation Neural Network (CFBNN). From the results of the study, the proposed control method has good capabilities compared to standard neural methods, namely FFNN and CFBNN. Integral Time Absolute Error and Square Error (ITAE and ITSE) values from the proposed method are on average of 0.96% and 0.2% better than the FFNN and CFBNN methods.

Performance evaluation of a novel improved slime mould algorithm for direct current motor and automatic voltage regulator systems

This study deals with the controlling the speed of a direct current (DC) motor via a fractional order proportional–integral–derivative (FOPID) controller and maintaining the terminal voltage level of an automatic voltage regulator (AVR) via a proportional–integral–derivative plus second order derivative (PIDD2) controller. To adjust the parameters of those controllers, a novel improved slime mould algorithm (ISMA) is proposed. The latter is a novel metaheuristic algorithm developed in this work. The proposed algorithm aims to improve the original SMA in terms of exploration with the aid of a modified opposition-based learning scheme and in terms of exploitation with the aid of the Nelder–Mead simplex search method. A time domain objective function, which includes time response specifications of steady state error and maximum overshoot along with rise and settling times, is used as a performance index to design the FOPID controller-based DC motor system and PIDD2 controller-based AVR system. The performance of the proposed novel approaches for both systems are assessed through time and frequency domain simulations along with statistical tests which show the greater performance of the improved algorithm. Further to this, the efficacy of the proposed approaches for both systems is compared with other available and effective approaches in the literature. The extensive comparative results demonstrate the proposed method to be superior to those state-of-the-art approaches for both DC motor speed and AVR control systems.

Sensor-less Brushed DC Motor Speed Control with Intelligent Controllers

A Direct Current (DC) Motor is usually supposed to be operated at a desired speed even if the load on the shaft is exposed to changes. One of its applications is in automatic door controllers like elevator automatic door drivers. Initially, to achieve this aim, a closed loop control can be applied. The speed feedback is usually prepared by a sensor (encoder or tachometer) coupled to the motor shaft. Most of these sensors do not always perform well, especially in elevator systems, where high levels of noise, physical tensions of the mobile car, and maintenance technicians walking on the car, make this environment too noisy. This Paper presents a new approach for precise closed loop control of the DC motor speed without a feedback sensor, while the output load is variable. The speed here is estimated by the Back EMF (BEMF) voltage obtained from the armature current. First, it is shown that a PID controller cannot control this process alone, and then intelligent controllers, Fuzzy Logic Controller (FLC) and Adaptive Neuro Fuzzy Inference Systems (ANFIS), assisting PID are applied to control this process. Finally, these controllers’ performance subjected to a variable mechanical load on the motor shaft are compared.

Design and application of an optimally tuned PID controller for DC motor speed regulation via a novel hybrid Lévy flight distribution and Nelder–Mead algorithm

This paper deals with the design of an optimally performed proportional–integral–derivative (PID) controller utilized for speed control of a direct current (DC) motor. To do so, a novel hybrid algorithm was proposed which employs a recent metaheuristic approach, named Lévy flight distribution (LFD) algorithm, and a simplex search method known as Nelder–Mead (NM) algorithm. The proposed algorithm (LFDNM) combines both LFD and NM algorithms in such a way that the good explorative behaviour of LFD and excellent local search capability of NM help to form a novel hybridized version that is well balanced in terms of exploration and exploitation. The promise of the proposed structure was observed through employment of a DC motor with PID controller. Optimum values for PID gains were obtained with the aid of an integral of time multiplied absolute error objective function. To verify the effectiveness of the proposed algorithm, comparative simulations were carried out using cuckoo search algorithm, genetic algorithm and original LFD algorithm. The system behaviour was assessed through analysing the results for statistical and non-parametric tests, transient and frequency responses, robustness, load disturbance, energy and maximum control signals. The respective evaluations showed better performance of the proposed approach. In addition, the better performance of the proposed approach was also demonstrated through experimental verification. Further evaluation to demonstrate better capability was performed by comparing the LFDNM-based PID controller with other state-of-the-art algorithms-based PID controllers with the same system parameters, which have also confirmed the superiority of the proposed approach.

The MRAC based-adaptive control system for controlling the speed of direct current motor

<p><span>This research aims to propose an adaptive control system for controlling the speed of the Direct Current (DC) motor. The system consists of two control loops: the first control loop is a traditional PID controller and the second control loop is an adaptive controller. The role of the adaptive controller is adjusting the output of the control object follows with the output of the reference model. The adjustment mechanism is very simple, but the quality of the whole system is very high: the conversion time is short and there isn't overshoot. The quality of the proposed adaptive control system is also compared to the traditional PID control system to show the advantages of the new system.</span></p>

SPEED CONTROL OF DC MOTOR USING ACO BASED FOPID CONTROLLER

Direct current (DC) motors are widely used because of their easily controllable and reliable access. DC motors have many salient features that includes a wide range of torque speed control, reliable operation with greater efficiency and a greater starting torque and many more. A nonlinear model for a DC motor can be represented for considering all uncertainties and non-linearities. In this paper, DC motor is studied which is having a second order system. Many controllers are being used for controlling speed of DC motor. An analysis is carried out on mathematical model of DC motor whose field is excited by an external or separate supply. Tuning of FOPID parameters using Ant Colony Optimization Technique (ACO) is discussed here.

The sensorless control system for controlling the speed of direct current motor

<p>This research aims to propose an algorithm for controlling the speed of the Direct Current (DC) motor in the absence of the sensor of speed. Based on the initial mathematical model of DC motor, the authors build the dynamic state equation of DC motor, and then build an estimation model to determine the speed of the DC motor without a sensor. The advantages of the proposed method are demonstrated through the closed-loop control model using the PID controller. In order for the results to be objective, we assume that the parameters of the DC motor in the estimation model are not known correctly. The results show that the quality of control in the absence of a sensor is equivalent to the case with the sensor.</p>

Design and simulation of model-free lag-lead controller for DC motor speed control

This paper presented a new Model-free Controller for speed control of Direct-Current (DC) motors. The design objectives were to combine the benefits of proportional-integralderivative (PID) controllers (simplicity in the structure and the usage) and performances of the advanced controllers such as linear-quadratic regulator (LQR), Fuzzy Logic Controllers (FLC), with the advantage of not depending on the model. The Model free Lag-Lead controller (MFLL) is compared against the Fuzzy, LQR, PI controllers for speed regulation of DC Motor and showed the ability to produce better performances. The MFLL is also easy to design and to tune without any special rule, and can be implemented by non-professional.Key words: Direct current (DC) motor, Fuzzy Logic controller (FLC), Linear-quadratic regulator (LQR), Model-free controller, Proportional Integral Derivative controller (PID).