Model Of Brushless DC Motor Research Articles

Discrete Adaptive Control with Multiple-Step-Guess Estimation for Brushless DC Motor

The brushless DC motor (BLDCM) speed control system has various kinds of uncertainties, such as reference speed mutation, noise and parameters change, etc. However, proportional integral (PI) control method used widely cannot handle the uncertainties in the control system well. A novel discrete adaptive control with Multiple-Step-Guess (MSG) estimation for BLDCM speed control system is proposed in this contribution. MSG estimation is firstly developed and applied in BLDCM speed control system, which estimate the BLDCM model parameters online with only five steps history information sampled from the input signal and output signal. The tracking adaptive control law is designed to ensure the speed can track reference speed rapidly and accurately. Compared with PI control and recursive least square adaptive control (RLSAC), extensive simulations verify that the BLDCM speed response under MSG adaptive control (MSGAC) has better dynamic and steady state performance in the case of reference speed mutation and BLDCM parameters change. Simulation results illustrate that the novel proposed method is effective and robust for uncertainties of BLDCM speed control system.

Fuzzy PID speed control of BLDC motor based on model design

Brushless direct current (BLDC) motor has been widely used in industrial production. Based on the method of model design, BLDC motor speed control is studied. In Matlab/Simulink, a fuzzy PID speed control model of BLDC motor is established, and C Code is generated by Real-Time Workshop (RTW) technology. After that, Code Warrior is compiled into machine Code and written into ECU. The results show that the fuzzy Proportion Integration Differentiation (PID) controller can improve the speed performance of the BLDC.

Sensor less Speed Control for Permanent Magnet Motors with Non- Sinusoidal Back EMF

Brushless DC motor has a rotor with permanent magnet and has no mechanical commutator which leads to many advantages like less maintenance, long life, high reliability, low inertia, low friction and low noise. Since the brush system/commutator assembly is replaced by an electronic controller which can be easily integrated into other required electronics, thereby improving the effective power to weight and power to volume ratio. In this paper the direct torque control method is employed to improve its torque ripple content which is been compared with conventional method by using MATLAB. In this project the three phase brushless DC motor model is designed with proportional integral controller and tested in MATLAB software. The PI controller is used to control the speed of the brushless DC motor. On the other hand parameters like Back EMF, current; speed and torque are evaluated for the designed models of BLDC motor. Due to the easy implementation and simple control structure the convectional PI controller are used in industries.

High-Stability Position-Sensorless Control Method for Brushless DC Motors at Low Speed

In order to improve the stability of brushless DC (BLDC) motors at low speed, a high-stability position-sensorless control method is proposed in this paper. Because the back electromotive force (EMF) is very small at low speed, a novel algorithm is proposed to detect the zero crossing point of back EMF accurately. First, the line-to-line back EMF is computed based on the mathematical model of BLDC motors. Then, a low-pass filter (LPF) with alterable cutoff frequency is used to reduce the disturbance of the line-to-line back EMF. Last, the commutation signal is obtained by detecting the zero crossing point (ZCP) of the line-to-line back EMF. However, the commutation signal is delayed by the LPF. For this reason, based on the three-phase back EMF, a novel compensation algorithm including an open loop and a close loop is proposed to compensate for commutation error. Moreover, the speed feedback has a big delay at low speed. According to this, a novel speed calculation algorithm is presented to decrease the delay. Both the simulation and experimental results validate the high stability and reliability of the proposed method.

Design and Realization of Control System of Brushless DC Motor Based on ARM

With the development of science and technology, people are demanding more and more sophisticated technology products, especially in some advanced and high-end research fields. For example, in terms of medical devices, home appliances, and transportation vehicles, there are higher demands on them that they can become more and more compact and sophisticated to meet people’s needs better. Most of the daily products are inseparable from the motor drive, and the convenience of motor drive will bring about rapid development and improvement in these fields to make these technologies meet people’s requirements. Based on the powerful simulation function of SIMULINK, the brushless DC motor control system simulation was completed. In the thesis, through the establishment of two simulation models, the theoretical analysis of the principle and mathematical model of brushless DC motor is carried out. Finally, the brushless DC motor control system is designed on the basis of simulation and theoretical analysis, and the hardware selection and circuit design are carried out. The main control chip of the control system is the LPC1766 of NXP Company, which is based on ARM’s latest Contex-M3 kernel. Besides, the data transmission is realized by the Modbus bus protocol in this control system, and the software programming is carried out in the U-Vision4 development environment, of which the implementation process is presented by flowchart.

Identification of Brushless DC Motor Current Ring System

The brushless DC motor (BLDCM) cannot adapt to the fast response speed because of its nonlinear, multivariable, time-varying parameters, strong disaster and so on. On the basis of analyzing and simplifying the mathematical model of brushless DC motor current loop, a recursive least square method is proposed to identify the current loop model of brushless DC motor. This method can accurately identify the mathematical model of the motor current loop, and the established model is effective and feasible.

High efficiency motor design of electric air-suction Seed metering device

In order to improve the working time of the planter.Aiming at the problems of low efficiency and high power consumption of the electric air-suction metering device drive motor, an efficient brushless DC motor is proposed. Based on the calculation of the structural parameters of the brushless DC motor, this machine analyzes the electromagnetic field and electromagnetic performance. A brushless DC motor model was built on the Motor Solve software and simulated. The simulation results show that there is no demagnetization phenomenon at the magnet, and the flux linkage is reasonable. The efficiency point can reach 85% at the rotation speed required by the seeding device.

Design and Implementation of Closed-Loop Controller with Current Sensors for Brushless DC Motor Drives

This paper is concerned with the design and implementation of closed-loop controller with current sensors for brushless DC motor drives to yield good speed regulation, low current harmonic and stable output speed. The design of controller is conducted by digitizing the mathematical model of brushless DC motors. The regulation and limitation of control parameters are also studied. The saliency of brushless DC motor yields larger quadrature-axis inductance than that of the direct-axis. This results in reluctance torque accordingly. By appropriately adjusting the direct-axis current, the reluctance torque can be controlled to achieve high-efficiency operation. The proposed control strategy will not only yield optimal efficiency but also result in the immunity of performance to the variation of machine parameters. A predicted current estimator is proposed to function under stationary frame for harmonic current suppression. In this paper, the mathematical models of the brushless DC motor is built and simulated by Matlab/Simulink. Then, a high-performance and low-cost digital signal processor (DSP, TMS320F28335) is used to control the system for reducing the circuit complexity. A digital control system for 750 W brushless DC motor is realized. Experimental results indicate that the total harmonic distortion of current is reduced by the proposed closed-loop controller with current sensor.

Brushless DC motor speed control strategy of simulation research

In view of the brushless DC motor speed regulation problem, an ideal control strategy is designed. Through the model and analysis of Brushless DC motor, the mathematical model of the brushless DC motor is obtained. By comparing three control strategies of PID control strategy, fuzzy control strategy and fuzzy PID control strategy, PID controller, fuzzy controller and fuzzy PID controller are designed respectively for simulation test. The simulation results show that the fuzzy PID controller has good control effect.

Enhancement of Variable Speed Brushless DC Motor using Neural Network

Background/Objectives: Direct Current (DC) motor plays an important role, especially in the industry as it is used in various applications as consequences from its unique characteristic. However, some limitations of variable speed DC motor cause the application of DC motor become less effective whereas some available speed controllers have difficulties in controlling the motor speed. An improved design based on neural network controller for the Brushless Direct Current (BLDC) permanent magnet motor drives is introduced in this paper. The variable input speed is applied to the controller in order to maintain the BLDC motor output speed. Methods/Statistical Analysis: In the proposed enhanced scheme, the mathematical model of BLDC motor and a back propagation Artificial Neural Network (ANN) algorithm are considered and included to replace the conventional method of Proportional Integral Derivative (PID). The proposed ANN algorithm is based on the plant information, where the number of input and output neurons in each layer of the network are designed to be equivalent to the input and output number of signals of the proposed system. The architecture of the proposed ANN controller is designed to has a 2-3-1 structure of the network, where the two input neurons will exist in the input layer. For the first incoming neuron is the different signal among the desired and the real signal. Meanwhile, the second input neuron illustrates the differ between earlier error signal and the ongoing error signal. The network parameter is updated once the process for learning model is defined to ensure the ANN is appropriate to be applied in the proposed system and meet the motor features. Findings: From the analysis, the output error of the system effectively decreases once the training method is implemented where sufficient training dataset for the input-output mapping plant is significantly acquired. The analysis of overshoot, rise time and steady state error for the speed range between 1000 and 1500 rpm indicates that the proposed Neural Network Control (NNC) technique has successfully improved the performance of the BLDC motor drive and outperforms the conventional state-of-the-art methods. Applications/Improvements: The complete simulation model of BLDC motor using NNC controller has been developed. In order to make it more effective, the implementation of the simulation work into the real hardware to investigate the performance of BLDC motor become more interesting. Besides, the NNC controller can be implemented in other applications such as DC-DC converter, permanent magnet synchronous motor or servo motor.

Fuzzy Generalized Predictive Control for Nonlinear Brushless Direct Current Motor

Abstract In the study, a novel fuzzy generalized predictive control (GPC) scheme is proposed for the stability control of nonlinear brushless DC motor (BLDCM). First, the fuzzy predictive model of BLDCM is presented via Takagi–Sugeno fuzzy model. Then, based on the controlled autoregressive moving average (CARMA) model transformed by Takagi–Sugeno fuzzy model of BLDCM, a new fuzzy GPC scheme for the nonlinear BLDCM is designed by combining fuzzy techniques and GPC theory, and the rigorous mathematical derivation is given. Finally, numerical simulations are implemented to verify the effectiveness and superiority of the proposed scheme. It also provides reference for other nonlinear even chaos control in actual project.

BLDC Drive Control using Artificial Intelligence Technique

This paper proposed a control scheme of a neural network for the brushless direct current (BLDC) permanent magnet motor drives. The behavior of BLDC motor drive is nonlinear, cause it is complex to handle by using conventional proportional-integral (PI) controller. In order to overcome this main problem, artificial neural network controller technique is developed. The controller is intended to tracks variations of speed references and stabilizes the output speed during load variations. The mathematical model of BLDC motor and artificial neural network algorithm is derived. The effectiveness of the proposed method is established by developing simulation model in MATLAB/ Simulink. The simulation results show that the proposed Artificial neural network controller construct substantial improvement of the control performance compare to the PI controller for both condition controlling speed reference variations and load disturbance variations.

Modelling of BLDCM with a double 3-phase stator winding and back EMF harmonics

Abstract In this paper the mathematical model of the brushless DC motor (BLDCM) with a double 3-phase stator winding is analysed. Both the 3-phase windings are mutually displaced by 30 electrical degree. Special care has been sacrificed to influence of higher harmonics of induced electromotive forces (EMF) on electromagnetic torque and zero sequence voltages that may be used for sensorless control. The mathematical model has been presented in natural variables and, after transformation to symmetrical components, in a vector form. This allows, from one side, for formulating the equivalent circuit suitable for circuit oriented simulators (e.g.: Spice, SimPowerSystems of Simulink) and, from the other point of view, for analysis of higher harmonics influence on control possibilities. These considerations have been illustrated with some results of four quadrant operation obtainded due to simulation at automatic control.

Parameter Identification of BLDC Motor Model Via Metaheuristic Optimization Techniques

The brushless dc (BLDC) motor has been increasingly used in industrial automation, automotive, aerospace, instrumentation and appliances. Analysis and design of the BLDC motor efficiently require its accurate model and parameters. In this paper, the parameter identification of the BLDC motor model via well-known metaheuristic optimization search techniques is proposed. Two trajectory-based methods, i.e. adaptive tabu search (ATS) and intensified current search (ICS) are employed to estimate the BLDC motor parameters. As simulation results of model identification and validation, both ATS and ICS can provide optimal BLDC model parameters. The BLDC models obtained show a very good agreement to actual system dynamics. However, the ICS can pro-vide optimal model parameters faster than the ATS.

Mathematical Model of Brushless DC Motor in Phase Loss Operation Mode

A mathematical model of a three-phase brushless DC motor was developed for studying the phase loss mode and developing fault-tolerant algorithms. The motor model is obtained on the basis of a synchronous motor with rotor position feedback and is comprised of a matrix of failures. The adequacy of the developed model is shownby the comparative analysis of simulation results of the motor phase loss and experimental results received on the test bench with the functioning recovery algorithm.

Simulation and Research of Dual Brush-Less DC Motor Synchronization Control System

For dual synchronous brush-less DC motor control system’s operation problems due to load disturbance caused by poor synchronization performance, this article establish a mathematical model of brush-less DC motors and synchronous control system, based on the basis of comparison of various control methods and control synchronization control, use VUFPID control algorithm to compensate for the bias adjustable dual brush-less DC motor control program, and compare with conventional PID and fuzzy PID algorithm. Through the simulation results and conclusions show that VUFPID control algorithm can improve the control system coupled deviation synchronization accuracy, superior to conventional PID and fuzzy PID algorithm system.

The Research on Electric Process State Spaces of BLDCM Main Module

Based on analyzing the mathematical model of Brushless DC Motor (BLDCM) and the key researching on electric process state spaces of BLDCM main module, taking its one state space as an example, BLDCM system is proposed as a new method of simulation model. This method establish BLDCM dynamic mathematical model, and the simulation is made to verify the theoretical model. Experimental results demonstrate the new method is successfully applied to the design of the electric vehicle drive system simulation.

English

BLDC motors have been gaining attention from various Industrial and household appliance manufacturers, because of its high efficiency, high power density and low maintenance cost. After many research and developments in the fields of magnetic materials and power electronics, their applications to electric drives have increased to a significant extent. In this paper, the modeling of Brushless DC motor drive system along with control system for speed and current has been presented using MATLAB / SIMULINK. In order to evaluate the model, various cases of simulation studies are carried out. Test results thus obtained show that, the model performance is satisfactory.

Control and Diagnostic Model of Brushless Dc Motor

Abstract A simulation model of brushless DC motor (BLDC) control and diagnostics is considered. The model has been developed using a freeware complex “Modeling in technical devices”. Faults and diagnostic parameters of BLDC are analyzed. A logicallinguistic diagnostic model of BLDC has been developed on basis of fuzzy logic. The calculated rules determine dependence of technical condition on diagnostic parameters, their trends and utilized lifetime of BLDC. Experimental results of BLDC technical condition diagnostics are discussed. It is shown that in the course of BLDC degradation the motor condition change depends on diagnostic parameter values

Modeling and Simulation of Brushless DC Motor Speed Control System Based on MATLAB/SIMULINK

Based on analysis of the mathematical model of the brushless DC motor (BLDCM), a method for modeling and simulation of BLDCM speed control system is developed in this paper. The simulation model of BLDCM could be established by combination of the functional blocks and S-functions in MATLAB/SIMULINK. In the double loop of control system, a PID controller was adopted in the speed loop and a current controller was completed in the current loop on the principle of hysteresis current track PWM inverter. The modeling method has merits in rapidity, practicality and has guiding significance to designing actual brushless DC motor control system.