汽车电子燃油泵中无刷无位置传感器直流电机控制研究

针对现有汽车电子燃油泵电机油品适应性差、噪声大、寿命短等问题,设计了一种无刷无位置传感器电机控制系统,并应用于电子燃油泵中.在分析无刷电机旋转原理的基础上,利用反电动势法在控制程序中构造虚拟中性点电压来确定反电动势过零点信号,并结合预定位启动法进行启动,从而实现了无刷无位置传感器电机的控制.实验表明:该系统能够准确判断电机反电动势过零点信号,电机运行平稳,且输出油量满足实际需求.     

基于HHT变换的起动电动机特征信号时频分析

针对车辆起动电动机电气和机械故障发生时特征信号的时变不平稳特性,进行了时频域分析处理,提出了利用现代信号处理方法对故障信号提取特征向量的方法,主要对起动电动机的电枢和轴承故障进行诊断。在构建电机故障测试实验平台的基础上,利用破坏性实验构造了故障类型,测取了电枢电流和振动信号,分别采用小波分析理论和HHT变换对信号进行分析,通过分解再重构的方式将信号分解成了频率由高到低的不同分量,并获得了故障的特征频率,提取了特征向量。实验结果表明,基于HHT变换的现代信号处理方法在处理时变非平稳信号方面比小波分析理论更具有自适应性,更易识别。     

Compensation of torque ripple in high performance BLDC motor drives

Brushless DC motor drives (BLDC) are finding expanded use in high performance applications where torque smoothness is essential. The nature of the square-wave current excitation waveforms in BLDC motor drives permits some important system simplifications compared to sinusoidal permanent magnet AC (PMAC) machines. However, it is the simplicity of the BLDC motor drive that is responsible for causing an additional source of ripple torque commonly known as commutation torque to develop. In this paper, a compensation technique for reducing the commutation torque ripple is proposed. With the experimental results, the proposed method demonstrates the effectiveness for a control system using the BLDC motors that requires high speed and accuracy.     

Novel bacterial foraging-based ANFIS for speed control of matrix converter-fed industrial BLDC motors operated under low speed and high torque

In this paper, a novel adaptive neuro-fuzzy inference system (ANFIS)-based control technique optimized by Bacterial Foraging Optimization Algorithm for speed control of matrix converter (MC)-fed brushless direct current (BLDC) motor is presented. ANFIS is considered to be one of the most promising technologies for control of electrical drives fed by MC. Optimizing the training parameters of ANFIS, to improve its performance, is still being considered by several researchers recently. Parameters of the online ANFIS controller such as learning rate (η), forgetting factor (λ) and steepest descent momentum constant (α) are optimized by using the proposed algorithm. For the purpose of comparison, proportional integral derivative controller, fuzzy logic controller, PSO-ANFIS and BAT-ANFIS are considered. Set point tracking performances of the proposed system are carried out at various operating points for an industrial BLDC motor operating at a maximum rated speed of 380 rpm and torque of 6.4 N m. Time domain specifications such as rise time, settling time, peak time, steady-state error and peak overshoot in the presence and absence of load torque disturbances are presented. Time integral performance measures such as integral square error, integral absolute error, and integral time multiplied absolute error are analyzed for various operating conditions. Speed fluctuation in the output of BLDC motor is dependent on the source current harmonics of the inverter/converter. To illustrate this, total harmonic distortion (THD) analysis is carried out for the existing PWM inverter and the proposed MC, and it is proved that MC results in reduced THD, as compared to PWM inverter. Simulation results confirm that the proposed controller outperforms the other existing control techniques under various set speed and torque conditions. Statistical analysis is effectively carried out to prove the effectiveness of the proposed controller. Experimental analysis is performed to validate the performance of the proposed control scheme.

     

FPGA‐based adaptive dynamic sliding‐mode neural control for a brushless DC motor

In the adaptive neural control design, since the number of hidden neurons is finite for real‐time applications, the approximation errors introduced by the neural network cannot be inevitable. To ensure the stability of the adaptive neural control system, a switching compensator is designed to dispel the approximation error. However, it will lead to substantial chattering in the control effort. In this paper, an adaptive dynamic sliding‐mode neural control (ADSNC) system composed of a neural controller and a fuzzy compensator is proposed to tackle this problem. The neural controller, using a radial basis function neural network, is the main controller and the fuzzy compensator is designed to eliminate the approximation error introduced by the neural controller. Moreover, a proportional‐integral‐type adaptation learning algorithm is developed based on the Lyapunov function; thus not only the system stability can be guaranteed but also the convergence of the tracking error and controller parameters can speed up. Finally, the proposed ADSNC system is implemented based on a field programmable gate array chip for low‐cost and high‐performance industrial applications and is applied to control a brushless DC (BLDC) motor to show its effectiveness. The experimental results demonstrate the proposed ADSNC scheme can achieve favorable control performance without encountering chattering phenomena. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Adaptive observer design for robust fault estimation

In this article, fault diagnosis for a class of linear systems based on adaptive observer is investigated. Linear systems without model uncertainty are first considered, and two adaptive observers are designed for fault identification. The first one uses optimal design for minimizing the estimation error. The second one can achieve asymptotic fault estimation. The general situation where the system is subjected to either model errors or external disturbance is then discussed. Robust adaptive control techniques are applied to guarantee the convergence to a bounded set. Simulation of sensor fault diagnosis for an induction motor is presented to verify the effectiveness of the proposed method.     

电动机故障诊断技术探讨

介绍了电动机常见电气故障和机械故障的类型及产生原因,详细阐述了短时傅里叶变换、小波变换、小波包变换、经验模态分解等基于信号处理的诊断方法,以及基于专家系统、模糊理论、支持向量机、神经网络等的智能诊断方法在电动机故障诊断中的应用,指出多种诊断方法相结合以及信息融合方法是电动机故障诊断技术的发展趋势。     

基于单片机的直流电动机的信号采集系统设计

装甲车辆起动过程中,直流电动机部分容易发生故障,传统的电机诊断方法都是定期制定维修计划,这种检修方式容易造成维修不足、维修过量以及盲目维修的问题,为了深入分析电动机故障发生时的参数信息的变化,构建一套能够采集电机运行参数的系统,对采集电枢电流和振动信号进行时频域分析,能够反映故障发生的特点;该系统以STM32103C8T6为主控芯片,设计了电流、振动信号采集电路,信号调理电路,对A/D转换模块、数据存储模块进行了编程实现,能够对直流电动机的电枢电流和振动信号进行实时采集,并将数据保存到上位机中进行后续的调用处理;通过测量对比直流电动机起动过程轴承部位发生不同故障时的电流和振动信号,利用MATLAB仿真实现时域内的信号显示,并在MATLAB平台中,编程实现了振动信号的时频域分析;仿真结果表明,该采集系统能够准确测量信号,具有成本低,体积小,精度高等优点,能够为故障特征提取提供较好的数据基础。     

基于DSP/BIOS的无刷直流电机控制系统的研制

无刷直流电机继承了直流电机运行效率高、调速性能好等优点,同时,克服了直流电机电刷带来的噪声、火花等缺点,在诸多领域得到广泛应用.为改善无刷直流电机控制系统的性能,本文研究模糊自适应PI控制器在无刷直流电机控制系统的应用.系统以TMS320F2812数字信号处理器为控制器设计了控制系统硬件,基于DSP/B10S嵌入式操作...     

基于STM32的双无刷直流电机闭环控制系统

针对无刷直流(BLDC)电机应用要求的提高,设计了基于STM32单片机的双无刷直流电机闭环控制系统。该系统分别根据各个电机的转速和电流反馈,采用PID控制算法,调节PWM输出信号,实现两台无刷电机的双闭环控制。详细介绍了系统的硬件设计和软件控制,并给出系统运行数据,验证了该系统运行稳定、响应速度快、具有良好的动静态性能。     

基于ST7的无传感器BLDCM控制系统设计

介绍了基于ST7FMC1K2单片机控制的无位置传感器无刷直流电机控制系统的设计。该系统通过反电动势法实现位置检测,同时给出了系统的硬件的构成。阐述了利用ST7FMC1K2芯片实现无位置传感器无刷直流电机的三段式起动方法;最后通过实验证明该系统具有较宽的凋速范围、优越的静态和动态特性。此方案电路简单、可靠性高,具有较高的应用价值。     

Adaptive diagnosis of DC motors using R-WDCNN classifiers based on VMD-SVD

Traditional fault diagnosis methods of DC (direct current) motors require high expertise and human labor. However, the other disadvantages of these methods are low efficiency and poor accuracy. To address these problems, a new adaptive and intelligent mechanical fault diagnosis method for DC motors based on variational mode decomposition (VMD), singular value decomposition (SVD), and residual deep convolutional neural networks with wide first-layer kernels (R-WDCNN) was proposed. First, the vibration signals of a DC motor were collected by a designed acquisition system. Subsequently, VMD was employed to decompose the raw signals adaptively into several intrinsic mode functions (IMFs). Moreover, the transient frequency means method, which can quickly and accurately obtain the optimal value of K, is proposed. SVD was applied to reduce the dimensionality of the IMF matrix for further feature extraction. Finally, the reconstructed matrix containing the main fault feature information was used to train and test the R-WDCNN. Based on residual learning, identification and classification of four types of vibration signals were achieved, while the R-WDCNN was optimized by the adaptive batch normalization algorithm (AdaBN). The recognition rate and the convergence were improved by this classifier. The results show that the method proposed in this paper has better adaptability and intelligence than other methods, and the R-WDCNN can reach a 94% recognition rate on unknown samples. Therefore, the proposed method is more intelligent and accurate than other methods.

     

ANFIS based multi-sector space vector PWM scheme for sensorless BLDC motor drive

This paper presents a design and development of Multi Sector Space Vector Pulse Width Modulation scheme (MS-SVPWM) for the speed control of brushless direct current (BLDC) motor drive. This control scheme is developed to enhance the performance of BLDC under wide range of speed and load variation. The hardware prototype is developed for 400 W, 30 V, 3000 rpm BLDC motor. The drive consists of uncontrolled rectifier unit for providing DC source to inverterunit. The proposed drive control has been done by implementing MS-SVPWM scheme using ANFIS control. The main function of ANFIS controller is to select the suitable sector for the drive and also predict the mismatching pulses by comparing conventional SVPWM and MS-SVPWM switching patterns. This new switching control technique helps to reduce switching losses of inverter and also improves an efficiency of BLDC system. This MS-SVPWM reduces the DC voltage ripple; Total Harmonic Distortion (THD) and torque ripple to the standard level. To verify and validate the practicality of the proposed system, the simulation is first performed using MATLAB Simulink tool. The hardware system is developed for the MS-SVPWM using DSPIC30F4011controller, the simulation and experimental results are presented.     

基于SVM的低压直流断路器故障诊断研究

低压直流断路器作为电力系统控制和保护的最重要的开关设备之一,它的可靠运行关系着电力系统的安全稳定性;因此实现低压直流断路故障诊断对于提高电力系统可靠性和稳定性方面具有重要意义;首先搭建了低压直流断路器信号监测与采集平台,然后提取断路器在分合闸过程中的机械振动信号和线圈电流信号的特征量作为训练和识别故障的特征,最后研究了SVM对电流、振动特征向量的故障诊断效果;结果表明:SVM能够实现低压直流断路器故障诊断,并且在融合振动信号和电流信号时的诊断效果更好。     

BLDC伺服系统控制系统建模与仿真

在分析了永磁无刷直流电机（BLDC）基本运行原理的基础上,建立了BLDC的数学模型,并在MATLAB7.1环境下搭建了伺服系统的双闭环控制系统仿真模型,实验结果表明,该模型在BLDC伺服系统的控制中性能稳定可靠,有较高的实用价值。     

基于自适应未知输入观测器的非线性动态系统故障诊断

针对以往故障诊断研究中要求故障或故障导数及系统干扰的上界是已知的不足,以及难以同时诊断执行器故障和传感器故障的问题,提出一种自适应未知输入故障诊断观测器,能够同时重构非线性动态系统的执行器故障和传感器故障.首先,利用H_∞性能指标抑制未知输入对故障重构的影响,采用Lyapunov泛函得到观测误差动态系统的稳定性;然后,通过线性矩阵不等式求解观测器增益阵,并实现故障重构;最后,通过直流电机系统的仿真验证了所提出方法的有效性.     

傅里叶变换光谱仪动镜运动控制仿真

在傅里叶变换光谱仪中,动镜按一定规律进行往复运动。一般控制方案采用直线电机直接推动,但在体积小而高分辨率的傅里叶变换光谱仪中,直线电机要做得很长,不是很合适。采用无刷直流电机进行动镜控制;分析了无刷直流电机(BLDC)数学模型和动镜速度均匀性对光谱仪的影响,采用自适应在线遗传算法整定的PID控制和传统PID控制方法分别进行仿真控制,着重分析了带负载无刷直流电机的控制效果,并人工加入负载扰动。仿真结果验证了采用合适的控制策略控制无刷直流电机可达到傅里叶变换光谱仪对速度均匀性的要求。     

基于深度度量学习的电机故障诊断

深度学习以其强大的自适应特征提取和分类能力在机械大数据处理方面取得了丰硕的成果，由于电机结构的复杂性，其信号表现出的非平稳、非线性和复杂多样等特点，使得传统分类方法中的Softmax分类器+交叉熵损失函数对电机故障诊断力不从心。根据电机信号非平稳、数据量大等特点，结合短时傅里叶变换(STFT)与深度学习中的卷积神经网络(CNN)算法和Triplet Loss三元组思想，提出了深度度量学习电机故障诊断方法。该方法能将电机故障信号转换成时频谱图，同时构建CNN，将预处理后的样本用于CNN的训练，采用Triplet Loss作为损失函数度量故障数据高维特征间的距离，并结合标签有监督地微调整个网络，从而实现准确的电机故障诊断。实验表明该方法在处理复杂数据时能够度量特征在高维空间中的距离，高效完成故障诊断任务，弥补了交叉熵函数的不足。     

Adaptive fuzzy APSO based inverse tracking-controller with an application to DC motors

This paper introduces the use of the adaptive particle swarm optimization (APSO) for adapting the weights of fuzzy neural networks (FNN) on line. The fuzzy neural network is used for identification of the dynamics of a DC motor with nonlinear load torque. Then the motor speed is controlled using an inverse controller to follow a required speed trajectory. The parameters of the DC motor are assumed unknown as well as the nonlinear load torque characteristics. In the first stage a nonlinear fuzzy neural network (FNN) is used to approximate the motor control voltage as a function of the motor speed samples. In the second stage, the above mentioned approximator is used to calculate the control signal (the motor voltage) as a function of the speed samples and the required reference trajectory. Unlike the conventional back-propagation technique, the adaptation of the weights of the FNN approximator is done on-line using adaptive particle swarm optimization (APSO). The APSO is based on the least squares error minimization with random initial condition and without any off-line pre-training. Simulation results are presented to prove the effectiveness of the proposed control technique in achieving the tracking performance.     

基于ML4428的无刷直流电机无传感器的控制

介绍了目前常用的无位置传感器无刷直流电动机的控制策略,分析了反电势检测换相原理以及ML4428的功能、参数设定及应用。ML4428无传感器平滑启动无刷直流电机PWM控制器是一种较理想的无刷直流电机驱动器件。