Enhanced Fuzzy Logic Control Model and Sliding Mode Based on Field Oriented Control of Induction Motor

In the context of induction motor control, there are various control strategies used to separately control torque and flux. One common approach is known as Field-Oriented Control (FOC). This technique involves transforming the three-phase currents and voltages into a rotating reference frame, commonly referred to as the “dq” frame. In this frame, the torque/speed and flux components are decoupled, allowing for independent control, by doing so, the motor’s speed can be regulated accurately and maintain a constant flux which is crucial to ensure optimal motor performance and efficiency. The research focused on studying and simulating a field-oriented control system using fuzzy control techniques for an induction motor. The aim was to address the issue of parameter variations, particularly the change in rotor resistance during motor operation, which causes the control system to deviate from the desired direction. This deviation implies to an increase in the magnetic flux value, specifically the flux component on the q-axis. By employing fuzzy logic techniques to regulate flux vector’s components in the dq frame, this problem was successfully resolved, ensuring that the magnetic flux value remains within the nominal limits. To enhance the control system’s performance, response speed, and efficiency of the motor, sliding mode controllers were implemented to regulate the current in the inner loop. The simulation results demonstrated the proficiency of the proposed methodology.

Hybrid algorithm for accelerating the double series of Floquet vector modes

In this paper, a hybrid algorithm for accelerating the double series of Floquet vector modes arising in the analysis of frequency selective surfaces （FSS） is presented. The asymptotic terms with slow convergence in the double series are first accelerated by Poisson transformation and Ewald method, and then the remained series is accelerated by Shank transformation. It results in significant savings in memory and computing time. Numerical examples verify the validity of the hybrid acceleration algorithm.

Fiber transmission demonstrations in vector mode space division multiplexing

Much attention has been focused on the use of scalar modes for space division multiplexing （SDM）. Alternative vector mode bases offer another solution set for SDM, expanding the available trade-offs in system performance and complexity. We present two types of ring core fiber conceived and designed to explore SDM with fibers exhibiting low interactions between supported modes. We review demonstrations of fiber data transmis- sion tbr two separate vector mode bases： one for orbital angular momentum （OAM） modes and one for linearly polarized vector （LPV） modes. The OAM mode demon- strations include short transmissions using commercially available transceivers, as well as kilometer length transmission at extended data rates. The LPV demonstra- tions span kilometer length transmissions at high data rate with coherent detection, as well as a radio over fiber experiment with direct detection of narrowband signals.

Multi-mode process monitoring based on a novel weighted local standardization strategy and support vector data description

There are multiple operating modes in the real industrial process, and the collected data follow the complex multimodal distribution, so most traditional process monitoring methods are no longer applicable because their presumptions are that sampled-data should obey the single Gaussian distribution or non-Gaussian distribution. In order to solve these problems, a novel weighted local standardization(WLS) strategy is proposed to standardize the multimodal data, which can eliminate the multi-mode characteristics of the collected data, and normalize them into unimodal data distribution. After detailed analysis of the raised data preprocessing strategy, a new algorithm using WLS strategy with support vector data description(SVDD) is put forward to apply for multi-mode monitoring process. Unlike the strategy of building multiple local models, the developed method only contains a model without the prior knowledge of multi-mode process. To demonstrate the proposed method's validity, it is applied to a numerical example and a Tennessee Eastman(TE) process. Finally, the simulation results show that the WLS strategy is very effective to standardize multimodal data, and the WLS-SVDD monitoring method has great advantages over the traditional SVDD and PCA combined with a local standardization strategy(LNS-PCA) in multi-mode process monitoring.

Internal mode of incoherent photovoltaic vector solitons

The internal modes of incoherent vector solitons （IVSs） in photovoltaic photorefractive materials are investigated in the framework of coupled nonlinear Schrodinger equations. It is found that there is a pair of internal modes corresponding to a bright-bright IVS. The propagation dynamics of the bright-bright IVS perturbed by the internal modes is simulated by numerical method.

Automatic target recognition of moving target based on empirical mode decomposition and genetic algorithm support vector machine

In order to improve measurement accuracy of moving target signals, an automatic target recognition model of moving target signals was established based on empirical mode decomposition(EMD) and support vector machine(SVM). Automatic target recognition process on the nonlinear and non-stationary of Doppler signals of military target by using automatic target recognition model can be expressed as follows. Firstly, the nonlinearity and non-stationary of Doppler signals were decomposed into a set of intrinsic mode functions(IMFs) using EMD. After the Hilbert transform of IMF, the energy ratio of each IMF to the total IMFs can be extracted as the features of military target. Then, the SVM was trained through using the energy ratio to classify the military targets, and genetic algorithm(GA) was used to optimize SVM parameters in the solution space. The experimental results show that this algorithm can achieve the recognition accuracies of 86.15%, 87.93%, and 82.28% for tank, vehicle and soldier, respectively.

基于STM32的无位置传感器无刷直流电机矢量控制系统

针对无刷直流电机在方波控制时效率低、转矩脉动大,以及位置传感器降低系统稳定性等问题,基于32位MCU STM32F103C8T6设计了一套无位置传感器矢量控制系统。该系统利用滑模状态观测器对电机转子位置及转速进行精确估计,并采用电流与速度双闭环矢量进行控制。测试显示,与传统方波驱动相比,电机运行噪声明显变小,且电流波形具有较高的正弦度。适用于空调、助力自行车等对噪声比较敏感的电器,同时成本低廉,有较高的市场应用价值。

基于参数自适应SVR和VMD-TCN的水电机组劣化趋势预测

针对水电机组难以利用实时监测数据对机组劣化状态进行有效评估,以及水电机组不同运行工况对运行状态指标趋势预测模型参数影响显著的问题,提出一种基于参数自适应支持向量回归机(SVR)、变分模态分解(VMD)和时间卷积网络(TCN)的水电机组劣化趋势预测方法;首先按照功率和水头将机组运行工况细化为若干典型工况,在此基础上采用改进天鹰算法建立SVR模型,对各个工况下的预测参数进行寻优,建立起工况与最优参数的数据;再通过神经网络对工况和最优预测参数进行拟合,构建出映射两者复杂关系的非线性函数,然后将构建出的映射关系加入到传统的SVR中,实现适应于水电机组工况变化的自适应SVR健康模型;其次,根据健康模型输出的标准值和监测数据,计算出劣化趋势序列;最后,考虑到劣化趋势序列的非线性因素,建立了一个基于VMD-TCN的时间序列预测模型,以实现对劣化趋势的准确预测。并设计多组对比实验,验证所提出模型的精度更高,时间更快。

基于改进型SMO无位置传感器速度控制

针对传统SMO(Sliding Mode Observer)在开关函数切换过程中存在抖振现象,提出一种利用饱和函数代替开关函数的新型滑模观测器削弱抖振,并在位置信息提取过程中选用锁相环代替传统的反正切方法,从而提高对PMSM(Permanent-Magnet Synchronous Motor)转子位置的观测精度。在Matlab环境下,通过传统和新型SMO的对比,可观察到转子的转速误差提高了大约14 r/min,转子位置误差提高大约0.03 rad。

Distance Estimation and Material Classification of a Compliant Tactile Sensor Using Vibration Modes and Support Vector Machine

Many animals possess actively movable tactile sensors in their heads,to explore the near-range space.During locomotion,an antenna is used in near range orientation,for example,in detecting,localizing,probing,and negotiating obstacles.A bionic tactile sensor used in the present work was inspired by the antenna of the stick insects.The sensor is able to detect an obstacle and its location in 3 D(Three dimensional) space.The vibration signals are analyzed in the frequency domain using Fast Fourier Transform(FFT) to estimate the distances.Signal processing algorithms,Artificial Neural Network(ANN) and Support Vector Machine(SVM) are used for the analysis and prediction processes.These three prediction techniques are compared for both distance estimation and material classification processes.When estimating the distances,the accuracy of estimation is deteriorated towards the tip of the probe due to the change in the vibration modes.Since the vibration data within that region have high a variance,the accuracy in distance estimation and material classification are lower towards the tip.The change in vibration mode is mathematically analyzed and a solution is proposed to estimate the distance along the full range of the probe.

基于表面辐射声信号的柴油机进气及齿轮故障诊断

利用声振信号进行发动机故障诊断过程中,部分故障激励仅在发动机表面特定位置的振动中有较强响应,振动测点要求高,需要接触测量,部分场景难以实现.为此,提出了一种以表面辐射声为媒介、以自适应变分模态提取(adaptive variational mode extraction,AVME)进行预处理的柴油机进气故障和齿轮故障诊断方法.开展了某直列六缸重型柴油机的进气滤清器堵塞、气门间隙异常和正时齿轮损伤3类故障状态的台架试验,获取了不同故障程度下发动机表面辐射噪声.基于改进的AVME方法,实现噪声信号本征模函数(intrinsic mode function,IMF)的最优分解,通过计算IMF与原信号间的互相关系数,提取高相关IMF构成故障诊断输入.经预处理后,声信号故障特征得到有效增强,再输入到麻雀搜索算法优化支持向量机模型(support vector machine model optimized by sparrow search algorithm,SSA-SVM),进行特征参量和模型参数协同优化可以获得更好的诊断精度.试验验证表明,无需在半消声室测试,仅使用单通道声信号对3类11种程度的进气系统和齿轮故障进行诊断,前端噪声准确率最高(98.89%),顶部噪声准确率最低(88.78%);使用前、顶、后三通道噪声数据后,诊断精度可提升至99.57%.研究结论为基于声信号等非接触测量的发动机故障诊断提供了参考.

基于向量夹角余弦IPD模式参与方关系质量评价研究

IPD模式参与方的关系对IPD模式的发展至关重要。引入关系质量的概念,从参与方供求关系出发,建立IPD模式参与方关系质量评价指标体系;并借鉴供应链稳定性思想,基于向量夹角余弦法构建了IPD模式参与方关系质量评价模型;进而提出判定标准,通过对IPD模式参与方关系质量水平的分析,实现了IPD模式参与方关系的评价。研究表明,该模型不仅能够对两两参与方、三方及IPD参与方关系系统的关系质量进行简单有效的定量化,还能找到影响关系质量的短板进行改进和提高,从而为IPD模式参与方关系管理提供了科学的依据和合理的视角。

低电压穿越条件下并网逆变器多矢量模型预测功率控制研究

为了满足光伏电站的低电压穿越要求,需要保障在电网电压跌落时光伏电站依然能够维持并网,快速向电网提供一定的有功功率和无功功率,改善并网质量。为此提出一种多矢量模型预测功率控制方法,该方法避免了对六对基本电压矢量的重复调制,而是在每个周期中始终选择一对基本电压矢量和两个零矢量以对称方式执行调制,有效降低了网侧电流谐波含量,同时减少了计算时间,且具有固定的开关频率。此外,为了避免负责反馈信号的观测器因低电压穿越引起观测误差以及节约硬件成本,在此引入滑模观测器,并在滤波方法上加以改进,避免了对精确角频率的依赖。最后,通过仿真和试验验证了所提方法的正确性及有效性。

基于PSO-SVM的Φ-OTDR系统模式识别研究

针对相位敏感光时域反射仪(phase sensitive optical time domain reflectometer,Φ-OTDR)系统中误报率高的问题,提出一种多域特征提取与粒子群算法优化支持向量机(particle swarm optimization-support vector machine,PSO-SVM)相结合的模式识别算法。首先,对原始信号进行差分处理后提取时域特征,并利用小波包分解方法,通过验证不同分解层数下的事件分类准确率,设定最优分解层数为6层,提取差分信号的能量特征。然后以SVM分类器为基础,利用PSO算法优化SVM分类器参数,提高光纤振动信号识别准确率。最后利用Φ-OTDR事件数据集进行验证,实验结果表明,该模式识别算法达到了95.6%的振动事件分类准确率。

基于新闻情感分析和区间分解的汇率预测研究

汇率序列具有非线性和连续变化等特点,其细节波动是一系列事件和新闻综合影响的结果.然而,现有区间预测模型难以量化重大事件和公众情绪的影响,导致其缺乏广泛的适用性,且传统区间分解方法存在上下界混叠的缺陷.因此,论文从新冠疫情冲击出发,提出一种基于新闻情感分析和区间分解的汇率波动实时预测模型.首先,基于Snownlp情感词典对外汇新闻文本进行情感分析,获得相应的情感分数.另外,构建全球恐惧指数(the global fear index,简称GFI)以量化新冠疫情的影响,并将其与芝加哥期权交易所波动率(the Chicago board options exchange volatility index,简称VIX指数)相结合作为汇率的影响因素.然后,提出一种新的区间经验模态分解(interval empirical mode decomposition,简称IEMD)方法对区间汇率序列进行多尺度分解,并根据样本熵重构得到高、中、低频区间序列和残差项.其次,利用极限学习机(extreme learning machine,简称ELM)、多层感知机(multi-layer perceptron,简称MLP)、随机森林(random forest,简称RF)和二次曲面支持向量回归(quadric surface support vector regression,简称QSSVR)分别对不同特征的子序列进行组合预测,以提高预测结果的准确性和稳定性.最后,利用论文方法对美元兑人民币、澳元兑人民币和瑞士法郎兑人民币3种汇率进行实证预测分析,结果表明,论文模型适用于重大事件影响下的汇率区间波动预测,与现有方法相比具有较高的预测精度.

复杂环境新能源汽车永磁电机传感器故障诊断

新能源汽车电机驱动过程中由于电机系统本身噪声、传感器运行特性和各个部件接触不良等因素影响下,永磁电机传感器易处于异常工作状态和故障状态。电动汽车电机运行环境的复杂性导致故障诊断难度也相对较大。于是提出新能源汽车永磁电机传感器故障诊断方法。通过分析永磁电机机械运动方式构建新能源汽车永磁电机数学模型,将上述模型与滑模观测器结合,捕捉任意时刻永磁电机传感器故障状态。基于此,提取传感器故障状态下的特征量,并输入支持向量机中,根据支持向量机输出的分类结果实现新能源汽车永磁电机传感器故障诊断。实验结果表明,针对新能源汽车恒速形式和新能源汽车变速形式,研究方法的诊断结构均与实际情况相符。

永磁同步电机低共模电压和低复杂度无差拍预测控制

针对永磁同步电机无差拍预测控制共模电压较大的问题,在一个控制周期内采用相位相反的基本电压矢量来生成虚拟零电压矢量抑制共模电压,并采用动态选择虚拟电压矢量生成方式来减小开关频率。为了提升无差拍预测控制性能,构造12个虚拟非零电压矢量来增加备选电压矢量。仿真结果表明:与7个基本备选电压矢量相比,基于19个备选电压矢量的无差拍预测控制降低转矩脉动6.89%,降低磁链脉动13.33%,同时共模电压也得到有效抑制。相较固定生成方式,动态选择虚拟电压矢量生成方式可降低开关频率25.89%。虽然该方法可有效提升系统性能,但增加遍历次数,极大增大计算量。为提高算法实时性,提出一种通过判断理想电压矢量所在区域来确定最优电压矢量简化确定方法,无需遍历计算,减小运算负担。基于STM32H743IIT6单片机实时性试验表明:相较于7个基本电压矢量,采用19个电压矢量算法执行时间增大273.40%,最优电压矢量简化确定方法执行时间减小88.09%。

基于滑模抗扰动观测器的PMSM无差拍预测控制

受温度及其它非线性因素影响,永磁同步电机(PMSM)运行过程中电阻、电感及磁链参数会发生变化,直接导致预测电流模型中电机参数与实际参数不符,影响系统控制性能。为提高系统参数的鲁棒性,针对一种三电平驱动的PMSM进行基于滑模观测器的参数抗扰动研究,电流内环采用等速趋近律滑模观测器来估计系统所受到的总参数扰动,并对模型电流预测控制器进行实时补偿;转速外环采用基于指数趋近律的滑模控制器实现;此外,针对传统预测电流控制中定子电流谐波分量大的问题,提出一种基于注入虚拟矢量的近似两电平SVPWM无差拍算法;最后,搭建Simulink仿真平台验证了所提控制理论的正确性与鲁棒性。

永磁同步电机模型预测转矩控制共模电压抑制研究

针对永磁同步电机模型预测转矩控制共模电压较大的问题,对比分析了磁链、转矩和共模电压共同控制、直接去除零电压矢量和虚拟零电压矢量三种抑制共模电压的方法。仿真结果表明,三种方法均可有效抑制共模电压。前两种方法均未采用零电压矢量,导致磁链脉动和转矩脉动增大;虚拟零电压矢量方法既保留了零电压矢量,又有效抑制了共模电压,但开关频率有所增大;动态虚拟零电压矢量选择开关次数最小的零电压矢量生成方式,可在控制性能基本相当的前提下,有效减小开关频率,综合性能最优。

飞机地面振动试验激振方案优化与工程验证

地面振动试验是飞机型号研制与强度验证中的重要环节,在试验设计阶段一个合理的激振方案能够显著缩短后续试验周期,提升模态辨识精度。基于飞机有限元模型采用QR分解对激振位置进行考虑试验目标的全局规划,进而引入模态参与对激振点布置方案的激振效率进行定量评估,同时针对密集模态测试问题,采用多变量模态指示函数对激励力矢量进行优化,并将上述方法应用于小型通用飞机地面振动试验中,验证优化后激振位置规划方案及密集模态识别方法的工程应用效果。结果表明:本文采用的激振方案优化方法能够显著提升试验效率,降低了密集模态辨识难度,具有较高的工程应用价值。