基于优化滑模观测器的无刷直流电机变结构直接转矩控制

本文提出一种由一个优化滑模观测器及一个变结构控制器组成的直接转矩控制系统,应用于永磁无刷直流电机。滑模观测器用来获取绕组反电动势,为减小系统抖振,引入连续光滑的反正切函数,并且证明了观测器的收敛条件及稳定性。然后以转速误差为基准建立滑模切换面,构造出变结构控制器,得到转矩控制的参考值。最终通过转矩误差并参考霍尔位置信号选择合适的空间电压矢量,完成直接转矩控制。该方案的控制效果能够不受制于电机参数变化及干扰影响,具有很强的鲁棒性。实验结果证明了该方案的正确性及有效性。     

改进的车载空调压缩机无位置传感器控制起动方法

无位置传感器控制是空调压缩机控制系统的较优选择。针对基于滑模观测器(SMO)的永磁同步电机(PMSM)无位置传感器控制系统零速观测精度低、起动难度大等问题,提出了一种改进PMSM无位置传感器的零速低速起动及中高速域使用的SMO控制的切换方法。在电机低速和零速时,I/F控制使其工作在角度开环电流闭环的工况下,该算法的给定相位与观测相位误差达不到系统切换要求,需利用降低开环电路的方式来降低误差;在电机转速提高的同时补偿SMO的角度,使观测角度接近真实角度,2种算法切换时稳定性更高,抗干扰能力更强。根据MATLAB/Simulink仿真结果和实际3.5 kW车载空调PMSM调试,验证了该方法的可行性和改进算法的有效性。     

基于改进滑模观测器的PMSM控制系统

针对永磁同步电机(PMSM)控制系统中常用位置传感器来获取速度反馈信息造成的系统体积大、成本高的问题,采用一种自适应滑模观测器(SMO)代替传统位置传感器来估计PMSM的位置和速度信息.基于磁场定向控制(FOC)理论,在Matlab/Simulink平台搭建基于改进滑模观测器的PMSM双闭环控制系统.以TMS320F2...     

PMLSM动子位置估算与校正策略研究

由于无传感器技术无法直接得到永磁直线同步电机（PMLSM）动子绝对位置,目前的研究中主要是通过速度积分估算动子位置。但是速度是由无传感器技术估算的,与实际速度之间仍存在微小误差,随着电机连续运动,误差量不断积累,最终可能会导致估算位置与实际位置之间的误差发散,甚至可能会使得基于无传感器控制的位置伺服系统崩溃。因此,提出一种全新的低成本、高精度位置校正策略,并进行了仿真验证,仿真结果表明该策略提高了位置估算结果的精度,并且消除了由偏差累积而导致无传感器位置伺服系统崩溃的风险。     

基于滑模观测器的直驱PMSG机侧控制算法研究与应用

为适应平流水发电水流变化、转速不稳定等外部环境的变化,提高发电站的发电效率,在直驱式永磁同步发电机（PMSG）机侧控制算法的基础上,设计一种带反馈增益自调整算法的滑模观测器（SMO）,对PMSG的转子位置进行估算,从而改善低速的估计误差。通过设计一套37 kW的PMSG发电机组,并在现场进行了试验测试,验证了算法可以有效控制直驱PMSG在低速运行时的转子位置估算误差,在高速运行时算法能够快速收敛,满足PMSG控制要求。     

Semiconductor metal oxide compounds based gas sensors: A literature review

This paper gives a statistical view about important contributions and advances on semiconductor metal oxide (SMO) compounds based gas sensors developed to detect the air pollutants such as liquefied petroleum gas (LPG), H₂S, NH₃, CO₂, acetone, ethanol, other volatile compounds and hazardous gases. Moreover, it is revealed that the alloy/composite made up of SMO gas sensors show better gas response than their counterpart single component gas sensors, i.e., they are found to enhance the 4S characteristics namely speed, sensitivity, selectivity and stability. Improvement of such types of sensors used for detection of various air pollutants, which are reported in last two decades, is highlighted herein.     

Improving the Detection Rate of Rarely Appearing Intrusions in Network-Based Intrusion Detection Systems

In network-based intrusion detection practices, there are more regular instances than intrusion instances. Because there is always a statistical imbalance in the instances, it is difficult to train the intrusion detection system effectively. In this work, we compare intrusion detection performance by increasing the rarely appearing instances rather than by eliminating the frequently appearing duplicate instances. Our technique mitigates the statistical imbalance in these instances. We also carried out an experiment on the training model by increasing the instances, thereby increasing the attack instances step by step up to 13 levels. The experiments included not only known attacks, but also unknown new intrusions. The results are compared with the existing studies from the literature, and show an improvement in accuracy, sensitivity, and specificity over previous studies. The detection rates for the remote-to-user (R2L) and user-to-root (U2L) categories are improved significantly by adding fewer instances. The detection of many intrusions is increased from a very low to a very high detection rate. The detection of newer attacks that had not been used in training improved from 9% to 12%. This study has practical applications in network administration to protect from known and unknown attacks. If network administrators are running out of instances for some attacks, they can increase the number of instances with rarely appearing instances, thereby improving the detection of both known and unknown new attacks.     

基于支持向量机的人眼检测

人眼检测是计算机人脸识别的重要部分。复杂环境下人眼定位容易受到光照以及人不同姿态的影响。为了解决复杂环境下的人眼定位问题,采用基于支持向量机的人眼检测算法,首先对复杂环境下采集的不同人的人眼样本进行灰度化均衡以及小波变换,将变换结果表示成向量形式,运用序贯最小优化算法进行训练,得到一组支持向量,然后遍历待检测人脸图利用支持向量所构成的分类器进行人眼初检,最后根据先验知识完成信息融合,最终标定人眼。实验结果表明,该算法对各种复杂环境下的含人眼图像有普遍的适应性和有效性。     

回归支持向量机的改进序列最小优化学习算法

支持向量机(support vector machine,简称SVM)是一种基于结构风险最小化原理的学习技术,也是一种新的具有很好泛化性能的回归方法,提出了实现回归支持向量机的一种改进的SMO(sequential minimal optimization)算法,给出了两变量子优化问题的解析解,设计了新的工作集选择方法和停止条件,仿真实例说明,所提出的SMO算法比原始SMO算法具有更快的运算速度.     

An adaptive sliding-mode observer with a tangent function-based PLL structure for position sensorless PMSM drives

This paper proposes a position estimation strategy for the sensorless control of permanent-magnet synchronous motors (PMSMs) based on an improved sliding-mode observer (SMO) and a tangent function-based phase-locked loop (PLL) structure. The improved SMO adopts a rotor speed-related adaptive feedback gain, and is able to derive a flux model-based estimator that contains rotor position and speed direction information. To extract accurate rotor position from the proposed SMO and reduce position estimation errors in both forward and reverse rotation of the PMSM, a tangent function-based PLL structure is established. The proposed SMO together with the PLL structure realises a solution to position and speed estimation for sensorless PMSM drives. Compared with the conventional back electromotive force (EMF)-based position estimator, the proposed position estimation strategy has advantages of simple design and robust estimation performance at a wider speed range. Effectiveness of the proposed method has been validated with simulations on virtual platform.