New Method to Restrain Pumping Voltage of Braking Procedure for Brushless DC Motor of Electric Armored Vehicle

In order to restrain the high pumping voltage of braking procedure which is harmful to the system of electric armored vehicle. Based on the analysis of pumping voltage of the braking procedure, the relation between pumping voltage and PWM ratio is derived and a new digital control method to restrain the pumping voltage by changing PWM ratio is put forward. Because the capacitance is decreased effectively, the volume of controller is reduced and the performance to price ratio is improved. The results of computer simulation and experiment proved that this method is feasible and valid.

Anti-reverse rotation startup method for sensorless brushless DC motor

In order to start up the brushless DC motor （BLDCM） without reverse rotation and smoothly switch the running state of the motor, a novel startup and smoothly switching method for a sensodess BLDCM is presented. Based on the saturation effect of the stator iron, six short voltage pulses are applied to determine the initial rotor position and the rotor can be found within 60°. After that, a series of short and long voltage pulses are used to accelerate the motor and the variation of the response current is utilized to detect the rotor position dynamically. When the motor reaches a certain speed at which the back-electromotive force （EMF） method can be applied, all the power devices are turned off and the running state of the motor is smoothly switched at the moment determined by the relationship between the terminal voltage waveform and the commutation phases. The experimental results verify the feasibility and validity of the proposed method.

Comparative Study between the PID Regulator and the Fuzzy Regulator Applied to the Operation of a Brushless DC Motor

This paper presents the results of research on speed regulation of a brushless DC motor. This is mainly a comparative study between a PID regulator and a fuzzy regulator applied to the operation of this type of engine in order to find the best control. The BLDC engine must operate under various speed and load conditions with improved performance and robust and complex speed control. Because of this complexity, the traditional PID command encounters difficulties in controlling the speed of a BLDC. Another control technique is currently developing and is producing good results. This is the fuzzy controller that handles process control problems, that is, managing a process based on a given set point per action on the variables that describe the process. To achieve the desired results, the brushless DC machine model will be studied. With the model obtained, both types of regulator will be tested. A synthesis of the observed comparison results will enable a conclusion to be drawn on the performance of the two types of regulators driving a BLDC (Brushless DC).

EFFECTIVE DOUBLE LOOP CONTROL FOR A SQUARE WAVE BRUSHLESS DC MOTOR

A new type of brushless DC motor has been developed by using a square wave rare earth permanent magnet synchronous motor with its double loop control circuit. The double loop control scheme of the drive system yields a combination of desired characteristics including simplified control structure, small ripple torque, high speed accuracy, wide operating speed range, and fast dynamic response. Experimental results confirm excellent characteristics of the motor.

Simulation Research of Fault Model of Detecting Rotor Dynamic Eccentricity in Brushless DC Motor Based on Motor Current Signature Analysis

基于Ansoft／Maxwell设置动态偏心故障，建立求解电机电感和磁链的有限元模型，通过仿真，证明了将感应电机动态偏心故障的特征频率经过简化后，同样适用于无刷直流电动机。基于Ansoft／Simplorer建立无刷直流电动机系统的仿真模型。在电机稳态运行下，对定子电流进行傅里叶分析，研究并建立基于定子电流监测动态偏心故障的仿真模型：动态偏心故障与特征频率的关系、动态偏心故障程度与特征频率幅值的关系。进而研究了无刷直流电动机稳态运行时转速波动对偏心故障监测的影响。仿真结果表明，转子偏心程度加大，特征频率的幅值增加。

Robust Control Strategy for the Speed Control of Brushless DC Motor

Brushless DC motor ( BLDCM) speed servo system is multivariable,nonlinear and strong coupling. The parameter variation, the cogging torque and the load disturbance easily influence its performance. Therefore,it is difficult to achieve superior performance by using the conventional PID controller. To solve the deficiency,the paper represents the algorithm of active-disturbance rejection control ( ADRC) based on back-propagation ( BP) neural network. The ADRC is independent on accurate system and its extended-state observer can estimate the disturbance of the system accurately. However,the parameters of Nonlinear Feedback ( NF) in ADRC are difficult to obtain. So in this paper,these parameters are self-turned by the BP neural network. The simulation and experiment results indicate that the ADRC based on BP neural network can improve the performances of the servo system in rapidity,control accuracy,adaptability and robustness.

Improved single-neuron PID controller for DC motor applied in robot system

An improved single-neuron proportional integral derivative （ PID ） controller and a new method to build the DC motor system were presented in the article. In the simulation, the robot arm is considered as an external load to DC motor. Both the motor module and the load module are crea- ted in Simulink to achieve simulation results closer to real robot system. In this way, it can well veri- fy the performance of the improved single-neuron PID controller, which is a combined controller of normal PID controller and single-neuron PID controller. Besides, an intelligent switcher can help to realize the function of choosing a better control algorithm according to motor＇ s velocity output. Sim- ulated results confirm the rapid and stable response of the improved PID controller. Moreover, the improved single-neuron PID controller has an excellent ability to overcome the load impact and su- press the jamming signals. At last, a GUI interface platform is built to make the controller easier to be applied in other robot systems.

Steering Control of Wheeled Armored Vehicle with Brushless DC Motor

Considering the steering characters of one type of wheeled armored vehicle, a brushless direct current （DC） motor is adapted as the actuator for steering control. After investigating the known algorithms, one kind of algorithm, which combines the fuzzy logic control with the self-adapting PID control and the startup and pre-hrake control, is put forward. Then a test-bed is constructed, and an experiment is conducted. The result of experiment confirms the validity of this algorithm in steering control of wheeled armored vehicle with brushless DC motor.

Neural adaptive sliding mode speed tracking control of a DC motor

We propose a BPNN based adaptive sliding mode control scheme for speed tracking of a DC motor with unknown system nonlinearities. The input-output linearization technique is used to cancel the nonlinearities, and output of the BPNN is incorporated into the controller in the proposed scheme. It is shown that the rotor speed of a DC motor can follow any arbitrarily selected trajectories under variable load torque. Then the application of the approach is tested via some simulations.

A New Method for Reducing Commutation Torque Ripples in BLDC Motors

A new compensation method and an algorithm for compensating for the commutation torque ripples of the trapezoidal EMF brushless DC motor are put forward. Simulation and experimental results show that this method is correct and practical.

Fault detection and diagnosis of permanent-magnetic DC motors based on current analysis and BP neural networks

In order to guarantee quality during mass serial production of motors, a convenient approach on how to detect and diagnose the faults of a permanent-magnetic DC motor based on armature current analysis and BP neural networks was presented in this paper. The fault feature vector was directly established by analyzing the armature current. Fault features were extracted from the current using various signal processing methods including Fourier analysis, wavelet analysis and statistical methods. Then an advanced BP neural network was used to finish decision-making and separate fault patterns. Finally, the accuracy of the method in this paper was verified by analyzing the mechanism of faults theoretically. The consistency between the experimental results and the theoretical analysis shows that four kinds of representative faults of low power permanent-magnetic DC motors can be diagnosed conveniently by this method. These four faults are brush fray, open circuit of components, open weld of components and short circuit between armature coils. This method needs fewer hardware instruments than the conventional method and whole procedures can be accomplished by several software packages developed in this paper.

A neural network-based commutation optimization strategy and drive system design for brushless DC motor

An optimized commutation method based on backpropagation(BP)neural network is proposed to resolve the low stability and high-power consumption caused by inaccurate commutation point prediction in conventional commutation strategy during acceleration and deceleration.This article also builds a complete brushless DC motor drive system based on the GD32F103 micro control unit(MCU),with an Artix-7 XC7A35T field programmable gate array(FPGA)to meet the performance requirements of neural network calculation for real-time motor commutation control.Experimental results show that the proposed optimization strategy can effectively improve the system stability during system acceleration and deceleration,and reduce the current spikes generated during speed chan-ges.The system power consumption is reduced by about 11.7%on average.

Design and implementation of a position sensorless control method for brushless DC motors

Because brushless direct current(BLDC) motors have the advantages of a compact size, high power density, high efficiency, and long operating life time, they are widely used in many industrial products and electric traction systems. It is known that the BLDC motors have no brushes for commutation. They are commutated with electronically commutation. So, the rotor position information of the BLDC motors must be known to understand which winding will be energized according to the energizing sequence. In most of the existing BLDC motor drivers, rotor position information is detected by Hall effect sensors. This kind of mechanical position sensors will bring additional connections and costs, reliability decrease and noise increase. In order to improve the control performance and extend the range of speed regulation for BLDC motors, a position sensorless control method is proposed in this paper. In the proposed control method, rotor position information of the BLDC motors is detected from the back electromagnetic forces(back-EMFs) which are estimated by an unknown-input observer with line to line currents and line to line voltages. For the purpose of verifying the effectiveness of the proposed control method, a model is built and simulated on the Matlab/Simulink platform. The simulation results show that the speed regulation performance of BLDC motors is improved compared with using Hall effect sensors. At the same time, the reliability of the BLDC motors is improved and the costs of them are reduced because the position sensor is eliminated.

High temperature brushless DC motor system and its operation mode control

The high temperature (175 ℃) operation of a motor spells out special requirements for control algorithms, materials and elements. The stability of motor characteristic is guaranteed by the digital control strategy. Constant velocity operation is achieved by phase locked loop (PLL), and constant power operation is achieved by a current restricting circuit. A motor for constant speed and constant power operation has been built and the speed control system is tuned by MATLAB simulation. Experimental and simulation results for operation mode control of brushless DC motor are presented.

Application of Diagonal Recurrent Neural Network toDC Motor Speed Control Systems

A new kind of dynamic neural network--diagonal recurrent neural network (DRNN) and its learning method and architecture are presented. A direct adaptive control scheme is also developed that is applied to a DC (Direct Current) speed control system with the ability to auto-tune PI (Proportion Integral) parameters based on combining DRNN with PI controller. The simulation results of DRNN show better control performances and potential practical use in comparison with PI controller.

DC Motor Speed Regulator via Active Damping Injection and Angular Acceleration Estimation Techniques

This paper suggests a novel model-based nonlinear DC motor speed regulator without the use of a current sensor.The current dynamics,machine parameters and mismatched load variations are considered.The proposed controller is designed to include an active damping term that regulates the motor speed in accordance with the first-order low-pass filter dynamics through the pole-zero cancellation.Meanwhile,the angular acceleration and its reference are obtained from simple first-order estimators using only the speed information.The effectiveness is experimentally verified using hardware comprising the QUBEServo2,myRIO-1900,and LabVIEW.

An experiment to estimate the revolution of DC motor

In this paper we have reported a simple and cost effective lab level experiment to determine the revolution of DC motor at various voltages.This experiment is designed on the basis of laser light pulses observed in cathode ray oscilloscope(CRO)output through an aperture.These output waveforms are directly related with the revolution of DC motor.A laser light beam was allowed to pass through a slot attached with the shaft of DC motor.The outcoming light is in the form of light pulses that falls on the photodetector which was connected to CRO.The frequency of light pulses is the function of applied voltage to DC motor.The CRO output was measured at different voltages.The visual waveforms on CRO interpret the different values of revolutions per minute(rpm)at different values of voltages given to DC motor.Analysis was made to determine the voltage depended revolution.

Power Factor Improvement of AC-DC Converter Based on Separately Excited DC Motor Using Passive Filter

In industries DC motor drives are very essential due to their high performance applications such as its reliability, ease of control, low cost and simplicity. And speed control of these motors is very easy due to power electronic AC-DC converters. These power electronic converters are with prominent low power factor and higher Total Harmonic Distortion (THD). These converters operate only for short time resulting non-sinusoidal waveform. This problem of harmonic distortion can be mitigated by reshaping the non-sinusoidal waveform to pure sine wave. Different wave shaping techniques have been developed by using different filters among which one is tuned passive filter. This paper proposed power factor improvement and harmonic mitigation of AC-DC converters based on separately excited DC motor using tuned passive filter. In this context experimental model is designed and results are analyzed by power quality analyzer.

Field Weakening Control of a Separately Excited DC Motor Using Neural Network Optemized by Social Spider Algorithm

This paper presents the speed control of a separately excited DC motor using Neural Network (NN) controller in field weakening region. In armature control, speed controller has been used in outer loop while current controller in inner loop is used. The function of NN is to predict the field current that realizes the field weakening to drive the motor over rated speed. The parameters of NN are optimized by the Social Spider Optimization (SSO) algorithm. The system has been implemented using MATLAB/SIMULINK software. The simulation results show that the proposed method gives a good performance and is feasible to be applied instead of others conventional combined control methods.

Solar PV System for Energy Conservation Incorporating an MPPT Based on Computational Intelligent Techniques Supplying Brushless DC Motor Drive

This paper proposes an effective Maximum Power Point Tracking (MPPT) controller being incorporated into a solar Photovoltaic system supplying a Brushless DC (BLDC) motor drive as the load. The MPPT controller makes use of a Genetic Assisted Radial Basis Function Neural Network based technique that includes a high step up Interleaved DC-DC converter. The BLDC motor combines a controller with a Proportional Integral (PI) speed control loop. MATLAB/Simulink has been used to construct the dynamic model and simulate the system. The solar Photovoltaic system uses Genetic Assisted-Radial Basis Function-Neural Network (GA-RBF-NN) where the output signal governs the DC-DC boost converters to accomplish the MPPT. This proposed GA-RBF-NN based MPPT controller produces an average power increase of 26.37% and faster response time.