Controller Design for Induction and Brushless Motors Using Matlab with Digital Signal Processor (DSP)

The automation process is a very important pillar for Industry 4.0.One of the first steps is the control of motors to improve production efficiency and generate energy savings.In mass production industries,techniques such as digital signal processing(DSP)systems are implemented to control motors.These systems are efficient but very expensive for certain applications.From this arises the need for a controller capable of handling AC and DC motors that improves efficiency and maintains low energy consumption.This project presents the design of an adaptive control system for brushless AC induction and DC motors,which is functional to any type of plant in the industry.The design was possible by implementing Matlab software and tools such as digital signal processor(DSP)and Simulink.Through an extensive investigation of the state of the art,three models needed to represent the control system have been specified.The first model for the AC motor,the second for the DC motor and the third for the DSP control;this is done in this way so that the probability of failure is lower.Subsequently,these models have been programmed in Simulink,integrating the three main models into one.In this way,the design of a controller for use in AC induction motors,specifically squirrel cage and brushless DC motors,has been achieved.The final model represents a response time of 0.25 seconds,which is optimal for this type of application,where response times of 2e-3 to 3 seconds are expected.

Regulation of specific abnormal calcium signals in the hippocampal CA1 and primary cortex M1 alleviates the progression of temporal lobe epilepsy

Temporal lobe epilepsy is a multifactorial neurological dysfunction syndrome that is refractory,resistant to antiepileptic drugs,and has a high recurrence rate.The pathogenesis of temporal lobe epilepsy is complex and is not fully understood.Intracellular calcium dynamics have been implicated in temporal lobe epilepsy.However,the effect of fluctuating calcium activity in CA1 pyramidal neurons on temporal lobe epilepsy is unknown,and no longitudinal studies have investigated calcium activity in pyramidal neurons in the hippocampal CA1 and primary motor cortex M1 of freely moving mice.In this study,we used a multichannel fiber photometry system to continuously record calcium signals in CA1 and M1 during the temporal lobe epilepsy process.We found that calcium signals varied according to the grade of temporal lobe epilepsy episodes.In particular,cortical spreading depression,which has recently been frequently used to represent the continuously and substantially increased calcium signals,was found to correspond to complex and severe behavioral characteristics of temporal lobe epilepsy ranging from gradeⅡto gradeⅤ.However,vigorous calcium oscillations and highly synchronized calcium signals in CA1 and M1 were strongly related to convulsive motor seizures.Chemogenetic inhibition of pyramidal neurons in CA1 significantly attenuated the amplitudes of the calcium signals corresponding to gradeⅠepisodes.In addition,the latency of cortical spreading depression was prolonged,and the above-mentioned abnormal calcium signals in CA1 and M1 were also significantly reduced.Intriguingly,it was possible to rescue the altered intracellular calcium dynamics.Via simultaneous analysis of calcium signals and epileptic behaviors,we found that the progression of temporal lobe epilepsy was alleviated when specific calcium signals were reduced,and that the end-point behaviors of temporal lobe epilepsy were improved.Our results indicate that the calcium dynamic between CA1 and M1 may reflect specific epileptic behaviors corresponding to different grades.Furthermore,the selective regulation of abnormal calcium signals in CA1 pyramidal neurons appears to effectively alleviate temporal lobe epilepsy,thereby providing a potential molecular mechanism for a new temporal lobe epilepsy diagnosis and treatment strategy.

Demonstration of the Source of Motor Program Signal: Study on the Correlation between Muscle Strength and sEMG Signal in Normal Children and Adults

To investigate the relationship between muscle strength and sEMG of biceps brachii during elbow flexion by measuring the maximum muscle strength and sEMG value of normal children and adults, and to analyze their sources, so as to lay a theoretical foundation for the method of motor program reconstruction to restore the function after brain injury, 30 healthy children aged 9 - 10 years and 30 adults aged 20 - 30 years were randomly selected. The muscle strength and sEMG of biceps brachii during elbow flexion were detected and recorded, and the data were statistically analyzed. The muscle strength of children was significantly lower than that of adults (P < 0.001), and the sEMG value of biceps brachii was significantly lower than that of adults (P < 0.001), but the sEMG value per kilogram force of children was significantly higher than that of adults (P < 0.01). The results show that there was a very significant difference in pull (efficiency) between adults and children when there was no significant difference in SEMG signal intensity. This is because although children’s central nervous system has matured, the muscle tissue has not been well trained, resulting in insufficient muscle strength. The muscle strength of adults is significantly higher than that of children, because they have been exercising for a long time after the development of the central nervous system. It is proved that sEMG signal is not produced by muscle contraction itself, but comes from the motor program signal of central nervous system which drives muscle contraction, and it is produced before muscle contraction.

Shafting misalignment fault diagnosis by means of motor speed signal and SVD-HT method

Aiming at the deficiency of diagnosis method based on vibration signal,a novel method based on speed signal with singular value decomposition and Hilbert transform(SVD-HT)is proposed.The fault diagnosis mechanism based on the speed signal is obtained by constructing the shaft misalignment fault model firstly.Then the SVD-HT method is applied to the processing of the speed signal.The accuracy of the SVD-HT method is verified by comparing the diagnosis results of the order spectrum method and the SVD-HT method.After that,the diagnosis results based on vibration signal and speed signal under no-load and load patterns are compared.Under the no-load pattern,the amplitudes of the speed signal components f_(r),2f_(r) and 4f_(r) are linear with the misalignment.In addition,under the load pattern,the amplitudes of the speed signal components f_(r),2f_(r) and 4f_(r) have a linear relationship with the load.However,the diagnosis result of the vibration signal does not have the above characteristics.The comparison results verify the robustness and reliability of the speed signal and SVD-HT method.The method presented in this paper provides a novel way for misalignment fault diagnosis.

双三相同步磁阻电机单相开路故障下的容错控制策略

双三相同步磁阻电机(dual three-phase synchronous reluctance motor,DTP-SynRM)转子不含永磁体,没有高温退磁风险,成本较低,且由于其系统自由度高、冗余性强,因此具有较好的容错性能。开路故障是双三相同步磁阻电机系统中最常见的故障类型之一,该文针对一种两套三相绕组中性点相互独立的双三相同步磁阻电机单相开路故障,建立DTP-SynRM矢量空间解耦模型,实现对电压、电流和磁链方程的完全解耦。在开路故障状态下仍将其视为一个正常的双三相电机,分析故障状态对电机的电流约束和信号传递的影响,并指出在开路故障下z2轴电流与β轴电流不再独立,电机由正常运行时的四阶系统降阶为三阶系统,而由于开路故障导致的电压信号传递不准确则会在dq坐标系中引入二次谐波电流,并产生二次和四次转矩波动。该文提出一种同时考虑电流约束和信号传递误差的故障容错方法,电机处于三闭环运行状态,补偿电压信号传递误差,抑制开路故障时电机的转矩波动并降低电流谐波。搭建实验平台进行实验,验证所提容错方法的有效性和可行性。

基于长周期暂态信号分析的电机驱动变换器直流母线电容在线状态监测

直流母线电容作为电机驱动变换器中最薄弱的元件之一,其老化会导致系统故障的概率增大,因此对电容老化进行在线监测至关重要。针对现有监测方法存在经济性差、采样频率高、影响系统正常运行等问题,提出一种基于长周期暂态信号分析的电容在线监测方法,用于估计电机驱动变换器直流母线等值串联电容(equivalent series capacitance,ESC)。首先,根据系统负载切换过程建立共节点感-容等值暂态模型,分析长周期暂态信号特点。其次,推导基于长周期暂态信号的在线监测模型,确定监测程序启动判定条件。然后,提出一种基于多项式重构的电容电流基线校准方法,消除传感器零漂影响,提高监测精度。最后,仿真和实验表明所提出方法的监测精度满足电容监测的要求。

基于微分熵及卷积神经网络的脑电运动想象分类识别

针对基于运动想象的脑电信号多分类识别准确率不高的问题,提出一种基于微分熵及卷积神经网络对运动想象四分类的识别方法。首先,将脑电信号通过滤波器提取为Alpha、Beta、Theta、Gamma 4个频段,分别计算各个频段的微分熵特征,并按照脑电极空间特征对数据结构进行重构为三维脑电信号特征立方体。最后,将其输入卷积神经网络进行四分类,该方法基于BCI Competition IV-2a公开数据集,准确率达到95.88%,并在试验室建立四分类运动想象数据集进行相同的处理,准确率达到94.50%。测试结果表明本文所提方法具有更好的识别效果。

基于轨迹数据的非机动车左转信号灯安全效应评估

【目的】对城市交叉口采用的左转非机动车信号灯设施进行交通安全性量化评估。【方法】提出一种基于拓展碰撞时间(extended time to collision,ETTC)指标的左转非机动车信号灯安全效应评估方法。针对现有的碰撞时间(time to collision,TTC)指标不适于评估交叉口左转非机动车冲突的问题,考虑非机动车车辆尺寸与加速度对交通冲突的影响,采用拓展碰撞时间指标,评估交叉口非机动车交通冲突。收集长沙市4个信号交叉口的视频大数据,利用视频软件Tracker提取车辆微观轨迹后,开展案例分析。【结果】左转非机动车信号灯在时间上明确了非机动车的通行权,其设置能显著降低非机动车冲突率,在平峰、高峰时段非机动车冲突率分别降低了40.11%、25.27%。在直行相位末期、左转相位即将启亮时,设置组的左转非机动车在待行区等待,冲突率降为0;而对比组近50%的非机动车违规左转,冲突严重。设置左转非机动车信号灯的改善效果随非机动车流量的增大呈先增加后降低趋势,而随机动车流量的增大呈逐步波动下降趋势。【结论】本研究揭示了非机动车左转信号灯的设置对减少交叉口交通冲突的影响,可为城市交叉口非机动车交通安全管控提供有益参考。

基于高频电流信号的电机故障特征提取方法

目前异步电机故障诊断主要依赖于振动、温度和噪声等参数,对于监测的电气信号,主要用于分析电机输出动力情况。电气信号中除了体现电机动力输出特征外,还包含丰富的机械故障、电气故障等信息特征。由于电气信号中的故障特征信号微弱,易被基频分量与噪声湮没而难以突出故障特征,不利于电机状态监测与故障诊断,因此提出了一种基于高频电流信号解调的电机故障特征提取方法,针对采样率不低于25.6 kHz的电机定子高频电流信号,综合运用高通滤波、Hilbert变换、快速傅里叶变换(FFT)3种分析方法,提取高频电流中电机故障的微弱特征信号,应用神经网络算法对电机正常、静态偏心、动态偏心、转子断条、轴承内圈、轴承外圈6种状态进行了诊断。该方法准确提取了电流信号故障特征,并成功识别了6种故障。

跨被试运动想象脑电信号的卷积神经网络识别方法

本文提出一种跨被试的深度神经网络识别方法,应对运动想象脑电信号的非线性、非平稳特性.该方法首先计算协方差矩阵均值,将不同被试者样本集的协方差对齐至单位矩阵,提升样本的被试间泛化性.然后,将对齐后的样本输入至卷积神经网络中,通过留一被试交叉验证法,构建跨被试的运动想象脑电信号识别方法.在BCI Competition IV dataset 2b公开数据集上进行实验,结果表明,新的方法在该数据集上取得了高的识别性能,且测试场景中的时间复杂度与现有方法相同.

基于sEMG的手指康复治疗的信号处理研究

手指功能在日常生活中特别重要,特别是在一些需要抓取和一些较为精细的动作中,对日常生活质量有着不可忽视的影响。而目前临床针对手指功能康复的治疗模式主要采用辅助设备康复,而这种模式又较为枯燥。提出了一种通过采集表面肌电信号(sEMG)的方式,使得手指功能受损的患者可以脱离现有比较枯燥的治疗方式,同时也更有利于患者其他功能例如神经系统功能的恢复。采用肌电信号公开数据集,对原始肌电信号做相关的预处理,同时采用matlab仿真的方式验证预处理的正确性;并通过临床实验采集患者肌电信号的方式验证使用目前的肌电传感器对运动意图分析的可行性。

强运动环境下抗基线漂移可穿戴运动体征监测系统设计

本文针对强运动环境设计了一种可穿戴运动体征监测系统。选用ADS1292型模数转换器芯片作为心电(ECG)信号采集模块,LMT70型温度传感器芯片作为体温信号采集模块,ICM20602型6轴陀螺仪作为加速度测量模块,可穿戴于人体实现动态心电监测、体表温度采集以及运动步数和运动距离测量。针对强运动导致的基线漂移对心电信号检测的影响,设计了归一化最小均方(NLMS)自适应滤波器进行滤除。测试结果表明,本设备测试精度较高,抗基线漂移能力较强。

基于小波包能量分析和信号融合的异步电机转子故障诊断

为提高异步电机转子故障诊断的可靠性,文中介绍了一种基于小波包能量分析和信号融合的异步电机转子故障诊断方法。采用定子电流信号和振动信号的频谱特征融合作为转子断条以及气隙偏心故障的诊断依据,首先对信号进行小波包分解,获得不同小波包频带节点下对应的能量分布,并与正常电机信号进行比较,进而对能量异常的信号频段进行小波包节点重构,最后通过快速傅里叶变换识别故障特征频率,诊断电机故障是否发生。通过仿真分析,验证了该方法的有效性和实用性,对于电机运行状态的准确监测具有重要意义。

嵌入式系统中运动想象脑-机接口编解码算法综述

脑-机接口技术通过在大脑与外部设备之间建立信息传输通路,使用户能够对外部设备进行直接控制。近年来,基于运动想象范式的脑-机接口编解码算法研究在医疗健康、教育娱乐及日常生活设备中的应用范围越来越广,这些算法通常需要嵌入到硬件设备中来满足实际应用的需求。介绍了近年来嵌入式系统中运动想象脑-机接口编解码算法研究现状,从传统机器学习算法和深度学习算法两个角度指出其对应的优缺点。重点介绍四类常用嵌入式平台的代表性设备及其优缺点,并针对不同的应用场景给出相应的硬件选型建议。归纳了更适用于嵌入式脑-机接口系统的评价指标并最终总结了领域内现存的挑战与未来发展方向。

中药及间充质干细胞调控免疫反应治疗肌萎缩侧索硬化症的作用机制

背景:肌萎缩侧索硬化为一种进行性神经退行性疾病,常导致大脑和脊髓神经元死亡。肌萎缩侧索硬化发病机制极为复杂,难治率、死亡率高且目前其治疗药物仅有2种,因此开发新治疗方法以改善患者预后迫在眉睫。目的:综述中药及间充质干细胞调控免疫反应治疗肌萎缩侧索硬化的作用机制。方法:以“traditional Chinese medicine,mesenchymal stem cells,ALS,immune response”为英文检索词,以“中药,间充质干细胞,肌萎缩侧索硬化,免疫反应”为中文检索词,检索万方、中国知网、PubMed及Web of Science数据库2010-2023年的相关文献,最终纳入69篇文献进行综述分析。结果与结论:①中药调控免疫反应治疗肌萎缩侧索硬化可总结为5个机制:主要包括冰片和黄芪甲苷等中药促进闭锁小带蛋白1、闭合蛋白5表达重建血液中枢神经系统屏障完整性;复方扶芳藤合剂调节自然杀伤细胞表面受体分子抑制其自身毒性;半枝莲和广藿香等作用补体系统因子抑制其异常激活;雷公藤和钩藤等介导细胞外信号调节激酶1/2衰减诱导型一氧化氮合酶产生而抑制小胶质细胞活化;左归丸、栝蒌根等促进白细胞介素10表达调控T细胞改善免疫环境。②通过现有研究总结了间充质干细胞调控免疫反应治疗肌萎缩侧索硬化可总结为5个机制:减少水通道蛋白4表达和降低内皮型一氧化氮合酶信号传导等方面修复免疫屏障完整性;释放吲哚胺2,3-双加氧酶和前列腺素E2等因子抗自然杀伤细胞毒性;分泌因子H干扰转化酶活性抑制补体系统异常激活;调控CX3CL1/CX3CR1系统轴或分泌转化生长因子β等途径改变小胶质细胞表型抑制其活性;增加白细胞介素10表达或激活STATS磷酸化通路来恢复T细胞功能。③目前中药联合间充质干细胞治疗肌萎缩侧索硬化研究较少,已知的相关研究报道显示,肌萎灵注射液可促进干细胞增殖分化以及补阳还五汤联合骨髓间充质干细胞显著提高血脑屏障完整性,未来还需进一步探讨两者对难治性肌萎缩侧索硬化的协同治疗效果。

柱塞泵马达高压油路压力脉动耦合特性

柱塞泵马达系统高压油路存在固有的压力脉动现象,其激发振动和噪音,恶化动力传递的平稳性,严重影响系统的可靠性和寿命。以往的研究主要聚焦于高压柱塞泵的压力脉动,而对柱塞泵马达系统高压油路的压力脉动耦合特性缺少研究。针对以上问题,通过揭示柱塞泵马达系统高压油路压力脉动的耦合机理及耦合特征,提出柱塞泵马达系统高压油路压力脉动解耦新方法。利用解耦新方法研究发现,泵源激励是贡献柱塞泵马达系统高压油路压力脉动的主要根源。对于所研究的联体设计的泵马达,在泵满排量状态下,泵源激励对高压脉动的平均贡献占比高达72%,而马达贡献占比仅为28%。新方法及所得到的新结论加深了对柱塞泵马达系统高压油路压力脉动耦合特性的认识,为进一步抑制和利用柱塞泵马达系统高压油路的压力脉动现象提供了理论支撑。

基于高频注入法的同步磁阻电机无位置传感器控制

同步磁阻电机是一种结构简单耐用、造价较低的特殊同步电机,近年来逐渐受到关注。将旋转高频电压注入法无位置传感器控制技术应用于同步磁阻电机,研究无带通滤波器同步磁阻电机位置辨识策略,以简化控制算法,降低位置辨识误差,减轻处理器的运算压力。同时在实验中,分奇偶周期分别注入基波电压和高频电压,以充分利用实验电源容量。在Matlab/Simulink环境下搭建仿真模型,同时搭建1.2 kW样机的硬件测试平台,仿真和实验结果都验证了简化的无位置传感器矢量控制算法的有效性和准确性。

陀螺电机轴承小样本非等间隔的寿命预测研究

现有轴承寿命预测的研究成果直接应用于陀螺电机轴承时,存在轴承振动信号难采集、模型使用小样本、非等间隔数据建模精度低等问题。选取陀螺电机电流信号作为可测信号并制定执行标准,截取有效电信号;提取初值化均方根值和Renyi熵作为退化特征以描述轴承寿命。设计的非等间隔、融合经验模态分解(Empirical Mode Decomposition,EMD)和生物地理学算法(Biogeography-based Optimization,BBO)的灰色模型(Grey Models,GM),即非等间隔EMD-BBO-GM(1,1),其包括间隔变化、数据分解、模型构建和参数优化共4个模块,可实现轴承寿命预测功能。选取小微挠性陀螺电机进行轴承寿命预测试验。研究结果表明,新模型使用两种退化特征获得的预测寿命与实际寿命相当,拟合精度不低于98%,预测精度不低于95%;与标准GM(1,1)相比,预测精度提升量为24.975%,间隔变化、数据分解和参数优化3个模块的贡献度分别为90.94%、3.64%、5.42%。

基于并行多尺度时间卷积网络的运动想象信号分类方法

运动想象信号的非线性和低时空域分辨率会导致基于深度学习分类方法的准确性较低。因此,提出一种并行多尺度时间卷积网络分类方法对运动想象信号进行分类。所提方法可提取运动想象信号在时间和空间上的信息,并在时间维度上使用并行卷积计算方法,更好地提取信号的时间特征。此外,在不影响分类准确率的前提下,使用一种简化的预处理方法,简化复杂的预处理过程。实验结果表明,所提方法在BCI Competition IV数据集上的分类准确率为74.4%,待训练的参数量减少到35663;对比FBCSP和DeepNet等经典分类方法,分类准确率分别提高了2.2%和6.6%,参数量分别降低了86.4%和87.5%,验证了所提方法的有效性。

改进FBCSP和CNN的运动想象脑电信号分类

为提高运动想象脑机接口识别准确率,结合共空间模式(common spatial pattern,CSP)和卷积神经网络(convolutional neural network,CNN)方法,提出一种改进滤波器组共空间模式(filter bank common spatial pattern,FBCSP)和CNN的算法,用于多分类运动想象脑电信号识别任务。信号预处理后,使用包含重叠频带的FBCSP计算空间投影矩阵,数据经过投影得到更有区分度的特征序列。然后将特征序列以二维排列方式输入搭建的CNN模型中进行分类。所提出方法在脑机接口竞赛数据集2a和Ⅲa上验证,并和其他文献方法对比。结果表明,本文方法一定程度上提高了运动想象脑电信号的分类准确率,为运动想象研究提供了一个有效办法。