Model Of Brushless DC Motor Research Articles

BLDC Motor Speed Control with Digital Adaptive PID-Fuzzy Controller and Reduced Harmonic Content

Low power brushless direct current (BLDC) motors are used in many consumer appliances. These motors have a relatively high winding resistance and therefore current control loop can be avoided in some cases, but fast and accurate speed regulation can be still needed. To minimize harmonics and generated sound noise, improved sinusoidal pulse width modulation (PWM) has been tested in the paper. As the most suitable commutation type, the sine wave with the third harmonic component has been selected. This type of communication reduced the torque ripple of the motor. This paper analyses the possibility to improve traditional proportional-integral-derivative (PID) speed regulator with Fuzzy logic block. A simulation model of BLDC motor, inverter, speed detection circuit and controller have been created. Simulation results showed that by applying the Fuzzy-based PID controller, the transient time can be reduced from 0.2 s to 0.05 s and overshoot can be avoided in comparison with traditional PID controller. Experimental results show a significant improvement in the motor dynamics—the overshoot and transient time were reduced twice. The difference with simulation results and experimental ones can be explained by delays introduced by the microcontroller.

Research on motor speed control based on the improved genetic algorithm to optimize fuzzy PID

This study aims to optimize the speed control effect of fuzzy PID controller in brushless DC motor control, and improve the traditional genetic optimization algorithm to improve fuzzy PID algorithm, mainly for genetic algorithm coding, crossover, and mutation process. In the three processes of the genetic algorithm, real number field coding is used, Taguchi orthogonal table is introduced into the cross-process, and Gaussian variation is applied to the mutation process. In the part of model construction and verification, this study uses Simulink software to build a brushless DC motor model and sets the same conventional parameters for simulation. The simulation results show that the algorithm overshoot and stability time of the fuzzy PID controller optimized by the improved genetic algorithm are obviously improved.

Autotuning of PID controller using the modified relay feedback test and optimization under uncertainty principle for robotic manipulators

AbstractThe development of an autotuning mechanism for robotic manipulators, inspired by the success of autotuners in industrial systems, aims to reduce the time and cost of tuning robotic manipulators. This paper introduces an auto‐tuning method for PID controllers in robotic manipulators using the Modified Relay Feedback Test (MRFT) and the Optimization Under Uncertainty (OUU) principle. The mechanism is versatile, suitable for manipulators of varying scales and motor power capacities. It incorporates actuator dynamics and a combined model of Brushless DC motors with an n‐link manipulator for TR development. Various manipulator models, including commercial and open‐source types were used to refine and test the TR. An algorithm is also proposed for tuning commercial manipulators' controllers without changing the existing closed‐loop system structure. Experimental and simulation tests demonstrate the method's effectiveness, showing better performance than traditional approaches, particularly in reducing overshoot and settling time.

SMO Based Position Sensorless BLDC Motor Drive Employing Canonical Switching Cell Converter for Light Electric Vehicle

In this paper, an adaptive sliding mode observer (ASMO) based rotor position sensorless brushless DC (BLDC) motor drive with energy regeneration for light electric vehicle (LEV) is designed. A canonical switching cell (CSC) converter is introduced for optimized MPPT performance and soft starting of the motor is assured during sensorless start-up. The CSC converter provides ripple free current in the output to the voltage source inverter (VSI) and minimizes the losses. It also acts as a fundamental building block for a DC-DC converter with the least element counts. For position sensorless commutation of BLDC motor drive, ASMO technique with self-correction ability of commutation error is used. Estimation of rotor angular position and continuous speed tracking for closed loop control are achieved using this method. This control is robust and derived from linear model of BLDC motor. Proper stability analysis of the observer is derived and ensured using pole placement method. Optimal gain scheduling technique is used in ASMO, which reduces the order of the error function making the computation easier. This model is truly adaptive to any parameter variation and load perturbation. To ensure green mobility, a pulse-width modulated regenerative braking algorithm is also integrated using the same VSI, which avoids any additional converter. Experimental results validate this method as effective for EV applications

Discrete sliding mode control method for particle swarm optimization-based brushless DC motor of electric vehicle

Brushless DC motor (BLDCM) is a multivariable nonlinear time-varying system, which is difficult to control. The discrete sliding mode control method for BLDCM of electric vehicle on the basis of particle swarm optimization (PSO) is studied to improve the application of BLDCM in electric vehicle. The mathematical model of BLDCM of electric vehicle is established using the state formula. Based on the mathematical model of BLDCM, through the analysis of electromagnetic torque control of BLDCM, it is clear that controlling the angle between rotor flux and stator flux can accurately control the electromagnetic torque of BLDCM. The adaptive discrete sliding mode controller (SMC) is set to control the electromagnetic torque of BLDCM of electric vehicle, and the PSO algorithm is adopted to obtain the optimal parameters of the adaptive discrete SMC to realize the discrete sliding mode control of BLDCM of electric vehicle. According to experimental results, the proposed method can achieve the accurate control of torque and speed of BLDCM of electric vehicle, and increase the application of BLDCM in electric vehicle.

Brushless DC Motor Modeling and Simulation in the MATLAB/SIMULINK Software Environment

The brushless DC motors (BLDCM) are capable of maintaining a constant speed in situations where speed and power are controlled at the same time. This motor, compared to DC motors, is able to generate far more power and simultaneously operate. An open-loop BLDCM output with hardware support and a three-segment method on closed-loop BLDCM were both examined in this study. The research provides a brushless DC motor model that considers the motor's commutation behavior. To make sure that the drive system for BLDC motors works properly, it is important to know the exact torque value, which is based on the back-EMF. The BLDC motor is simulated in MATLAB/Simulink after a basic mathematical model is developed. In open-loop circuits, the intensity may be adjusted by varying the pulse width (or duty), and the motor speed can be increased or decreased by altering the input voltage. Pulse widths and speeds are measured and compared to real-world hardware in this study. This paper presents a comparison of the outcomes of the BLDC motors based on the examination of time responses.

Mathematical modeling and analysis of fractional-order brushless DC motor

In this paper, we consider a fractional-order model of a brushless DC motor. To develop a mathematical model, we use the concept of the Liouville–Caputo noninteger derivative with the Mittag-Lefler kernel. We find that the fractional-order brushless DC motor system exhibits the character of chaos. For the proposed system, we show the largest exponent to be 0.711625. We calculate the equilibrium points of the model and discuss their local stability. We apply an iterative scheme by using the Laplace transform to find a special solution in this case. By taking into account the rule of trapezoidal product integration we develop two iterative methods to find an approximate solution of the system. We also study the existence and uniqueness of solutions. We take into account the numerical solutions for Caputo Liouville product integration and Atangana–Baleanu Caputo product integration. This scheme has an implicit structure. The numerical simulations indicate that the obtained approximate solutions are in excellent agreement with the expected theoretical results.

Loss and Efficiency Analysis of BLDC Motor and Universal Motor by Mathematical Modelling in the Mixer Grinder

The typical mixer grinders are universal motor, which operates on alternating current supply because of the higher starting torque characteristics and easy speed control. The absence of brushes in the Brushless DC (BLDC) motor and noise reduction improves its life and makes it perfectly suited for mixer grinder. The mathematical modelling and experimental demonstration of a conventional universal motor and proposed simulated model of BLDC motor was analysed using the MATLAB/Simulink. The loss and efficiency analysis of the permanent magnet BLDC and universal motor in the application of a mixer grinder are presented in this paper.

Modelling of Brushless DC Motor Drive for Electric Vehicle Application

In Electric vehicle, motor plays a significant role. In wheel, motor improves the efficiency and safety of high-performance Electric vehicle. There are a lot of motors available in market, out of all considering their advantages and disadvantages Brushless DC motor is chosen as an efficient motor for Electric vehicle propulsion. The reason behind to choose BLDC motor is because of its desired torque vs speed characteristics, cost of maintenance is low, efficiency is high and has high power density. In this model simulation of an accurate and precise BLDC drive for Electric vehicle application is done and Simulation results such as speed, Electromechanical torque, and various phase voltages graphs are obtained.

Genetic Algorithm-Based Optimal Design of a Rolling-Flying Vehicle

Abstract This work describes a design optimization framework for a rolling-flying vehicle consisting of a conventional quadrotor configuration with passive wheels. For a baseline comparison, the optimization approach is also applied for a conventional (flight-only) quadrotor. Pareto-optimal vehicles with maximum range and minimum size are created using a hybrid multi-objective genetic algorithm in conjunction with multi-physics system models. A low Reynolds number blade element momentum theory aerodynamic model is used with a brushless DC motor model, a terramechanics model, and a vehicle dynamics model to simulate the vehicle range under any operating angle-of-attack and forward velocity. To understand the tradeoff between vehicle size and operating range, variations in Pareto-optimal designs are presented as functions of vehicle size. A sensitivity analysis is used to better understand the impact of deviating from the optimal vehicle design variables. This work builds on current approaches in quadrotor optimization by leveraging a variety of models and formulations from the literature and demonstrating the implementation of various design constraints. It also improves upon current ad hoc rolling-flying vehicle designs created in previous studies. Results show the importance of accounting for oft-neglected component constraints in the design of high-range quadrotor vehicles. The optimal vehicle mechanical configuration is shown to be independent of operating point, stressing the importance of a well-matched, optimized propulsion system. By emphasizing key constraints that affect the maximum and nominal vehicle operating points, an optimization framework is constructed that can be used for rolling-flying vehicles and conventional multi-rotors.

An integrated converter topology for torque ripple minimization in BLDC motor using an ITSA technique

This paper proposed an enhanced DC–DC converter structure with hybrid control algorithm to diminish the torque ripple of a Brushless DC (BLDC) motor. Initially, the modeling of BLDC motor is designed with enhanced Cuk converter. Here, the operation and executions of Cuk converter is enhanced with the utilization of switched inductor. At that point, the control mechanism incorporates two controlling loops like speed and torque control loop are used to enhance the BLDC performance. So as to enhance the control loop operation, the Improved Tunicate Swarm optimization algorithm (ITSA) is presented. In this paper, the ITSA algorithm is explored to control the speed and torque error from BLDC motor. Here, the TSA search behavior is improved with crossover and mutation operator. In this paper, the output of proposed strategy is subjected with input of speed and torque controllers. Based on that, the best optimal gain parameters are resolved to upgrade the controller operation with the help of the required objective functions. The proposed controller is performed on the MATLAB/Simulink platform and the torque ripple minimization performance is compared with other existing systems like Particle Swarm Optimization algorithm and Bacterial Foraging algorithm.

Torque Control Oriented Modeling of a Brushless Direct Current Motor (BLDCM) Based on the Extended Park's Transformation

The wheeled mobile robots have recently become a better choice for repetitive tasks and especially in the agricultural field, but the existing constraint remains in its electrical motor, either in its consumption or its control. Therefore, we will focus on the Brushless Direct Current Motor (BLDCM) included on the robot wheels. Hence, the objective of this paper is to provide a model of BLDCM to have both maximum torque and a reduced torque ripple. Indeed, it is important to give a mathematical model that correctly represents the motor in three phases reference frame. To reduce the complexity of the model, we have used the extended park reference frame, which provides a biphasic representation in order to control the current using Proportional Integral Controller (PI Controller). The angular velocity of the motor is controlled using two types of regulators; ones called PI Controller and the other is Fuzzy Logic Controller (FLC) to compare its performance. The motor is attached to an inverter, which is controlled using a Full-Wave offset method. The modeling machine is done and validated using MATLAB Simulink Library. The simulation results of the modeling system are evaluated to have the profile of the wheel speed rotating freely, and the energetic efficiency of the BLDCM during functioning.

Investigation on modeling and simulation BLDC motor fed universal actuation system

This paper deals with mathematical modeling of Permanent magnet brushless DC (BLDC) motor in MATLAB-SIMULINK environment. Modeling of BLDC motor carried in transfer function, transfer equations and state space model to verify the performance as actuators. Mathematical switches to control electronic commutation of BLDC motor based on signals of Hall Effect position sensor using three-phase inverter drive. Performance of the simplified mathematical inverter fed BLDC motor under steady state and dynamic conditions analyzed. Due to the switching losses during PWM generation generates low ripple content in torque of BLDC motor which described and eliminated through state space model. Comparison made of proposed modeling of BLDC motor with motor parameters like back-EMF, stator current and speed of BLDC motor, proposed work suggests the state space modeling holds a superior method for design of BLDC motor during high dynamic load performance and operating ranges.

PI Controller Based Performance Analysis of Brushless DC Motor, Utilizing MATLAB Simulink Environment

Brushless DC Motor (BLDC) is gaining more and more popularity as one of the best electrical drives nowadays due to advantages like high efficiency, low maintenance, good reliability & wide dynamic response. The traditional brushed motor speed regulation is essentially effective in low speed and unable to lower the commutation torque ripple in high speed range. Speed regulation of Brushless DC (BLDC) motor is done by utilizing PI controller. The PI controller output act as the input to the variable voltage block. The mathematical modeling of BLDC motor is additionally shown here. The BLDC motor is supplied from the inverter while the rotor position and speed are the input here. The detailed mathematical model of the anticipated drive system is developed and simulated using MATLAB/Simulink environment. Principle of operation of using component is examined and therefore the simulation results are reported here to verify the theoretical analysis.

ANN‐based method for parametric modelling and optimising efficiency, output power and material cost of BLDC motor

This study presents a new method for parametric modelling and optimisation of a permanent-magnet brushless DC (BLDC) motor. We proposed an artificial neural network (ANN)-based metric technique to combine and optimise different objective functions of a BLDC motor using ANN-based models and compared with conventional optimisation methods with analytical models. To proceed with this optimisation problem, the function should be minimised. For applying constraints to this problem, a simple method called penalty factor is proposed, in which a penalty term was added to the function when the constraints are violated. We considered three goals in this optimisation: efficiency maximisation, speed maximisation and material cost minimisation. Since the load is constant torque in our case, more speed means more powerful motor, and to achieve the minimum material cost goal the volume of the magnet is set as an objective function. To find the optimum geometric parameters, we used gradient-based method subject to non-linear magnetic constraints. All the obtained results were validated by Ansoft Maxwell. Optimising using the proposed method including ANN-based models does not require knowledge about complicated electric/magnetic equations. Also, ANN-based BLDC motor model is more accurate than analytical models and faster than existing models in simulation tools.

Research on brushless DC hub motor control system

The brushless DC hub motor is an important part of the wheeled electric vehicle. It has the advantages of simple structure, reliable operation, high efficiency and good speed regulation performance. Based on the mathematical model of brushless DC motor (BLDCM), the ontology model of BLDCM is established based on Matlab2016/Simulink environment combined with S-function. Then the model of speed-current double closed-loop PID, fuzzy adaptive PID and sliding mode control system of brushless DC hub motor is established, and the control system of BLDCM is simulated according to the demand of the electric vehicle. The results show that the sliding mode control system has a good control effect and lays a solid foundation for the design of the actual control system.

Propeller speed estimation for unmanned aerial vehicles using Kalman filtering

This paper presents an online propeller speed estimation system for a multirotor unmanned aerial vehicle (UAV) equipped with brushless DC (BLDC) motors and powered by a lithium-polymer battery pack. Propeller speed estimation is based on battery drain current measurement extended with averaged state-space model of brushless DC motor utilised within a Kalman filter-based state estimator. Based on the BLDC motor and propeller physical parameters and utilising corresponding mathematical model, the estimation system is implemented within the flight computer on board the UAV. The proposed propeller speed estimation algorithm is verified experimentally for a wide range of propeller operating regimes, which has shown that the proposed method is able to provide efficient estimation of UAV propeller speed.

Non-linear discrete model of BLDC motor for studying the range of permissible values of the voltage vector in the state space

A nonlinear discrete model of a brushless direct current (BLDC) motor in a rotating coordinate system with vector control was developed. The synthesis of optimal control in the state space was performed using the Riccati equation. For practical problems of BLDC motor control, the maximum values of voltages and currents are found while maintaining the identifiability of the BLDC motor model. The study of the range of admissible values of the voltage vector in the state space for the obtained nonlinear discrete model of the BLDC motor is carried out.

Digital twin of mechatronic drive based on the optimal control model of BLDC motor

This paper presents a digital twin of a mechatronic drive based on a brushless DC (BLDC) motor model based on a nonlinear discrete optimal control model. Some parameters of a BLDC motor, such as resistance and inductance of windings, magnetic flux, viscous friction coefficient in bearings, angular velocity and electromagnetic moment, can change due to both degradation of structural elements and external forces. Simulation by a complete enumeration of the values of the parameters of the mechatronic device with a certain step will make it possible to adapt the program of the control device to changing operating conditions according to the criterion of minimizing the control energy by changing the parameters of the state matrices and control of the digital twin. As a result, the accuracy of the movement of the mechatronic device along the given trajectory will increase due to the greater correspondence of the control parameters to the real object.

Identification and implementation hybrid fuzzy logic and PID controller for speed control of BLDC motor

One of the problems in the optimization process in the Brushless Direct Current (BLDC) speed control system is to obtain a mathematical model in the form of a transfer function. The purpose of this study to mathematically model BLDC motors in transfer functions, and optimization using Proportional, Integral and Derivative (PID) controllers, and fuzzy logic to tune PID controller parameters. The first method used is the process of identifying input and output data from the BLDC motor physical system. The input and output data of the test results simulated to form a mathematical model. The mathematical model of BLDC motor used as the basis for carrying out the optimization process with open loop systems, PID controllers, and fuzzy logic. The results of the research on the optimization process of the BLDC motor speed control system with the fuzzy logic methods obtained the best value for rise time value of 1.25 seconds, settling time value of 382.10 seconds, and peak time is 382.2 seconds. With these results, it shows that the fuzzy logic has given a better transient response than the PID controller in increasing optimal stability.