一类MIMO非线性系统的自适应模糊输出反馈控制

针对一类MIMO非线性状态不可测系统,提出了一种稳定的基于观测器的自适应模糊控制方法.该方法不需要系统状态可测的条件,而是通过设计模糊观测器来估计系统的状态.证明了所提出的控制方法可保证闭环系统的稳定性和跟踪误差的收敛性.仿真结果进一步验证了该控制方法的实用性和有效性.     

基于预测模型切换的航发传感器主动容错控制

针对航空发动机传感器硬故障,基于模型预测控制(MPC)算法,提出了一种"隐性"判断传感器故障的主动容错控制(FTC)方法。首先,该方法建立包含正常模式以及传感器故障的预测模型库,然后,监控决策单元根据传感器测量值,选择与之最匹配的预测模型,并向切换单元下达切换指令,以此达到在线容错的目的。同时,对CMPASS-40K涡扇发动机进行了主动容错控制仿真,结果表明,监控决策机制能够在较短的时间内判断传感器故障,实现容错,而且该方法不仅能实现已知故障容错,还能实现未知故障容错。     

基于RBF神经网络的控制系统传感器故障诊断方法

针对现行研究中压铸机实时检测与控制系统中相关传感器的常见故障问题,通过对人工神经网络理论与方法的学习,建立了一种基于径向量基函数神经网络RBFNN的控制系统传感器故障诊断观测器模型.通过来自压铸机的实测参数进行模型训练,采用模糊K均值聚类算法选取聚类中心,利用该观测器确定传感器输出值与传感器实际输出值之间的残差,以此判断传感器是否发生故障.仿真结果表明,RBFNN观测器具有较强的非线性处理和任意函数逼近的能力,预测精度高,学习时间短,网络运算速度快,性能稳定,可满足传感器故障诊断的要求.     

扩张状态观测器下的自适应输出反馈控制

针对一类不确定非线性系统,研究了一种结合反步法和自抗扰控制的新的自适应输出反馈控制方法.通过引入扩张状态观测器(ESO)对被控系统的未知状态进行实时估计,同时利用扩张状态观测器实现对系统中的不确定项在线逼近及补偿.通过非线性滤波器对反步法设计过程中的虚拟控制信号进行求导,避免了传统反步法设计控制中复杂性爆炸的问题,并由此设计了自适应输出反馈控制器.通过李雅普诺夫函数证明了这种控制方法的稳定性,验证了闭环系统中所有信号均是有界的.数值仿真算例进一步验证了该方法的有效性.     

无人机分布式飞行控制计算机故障诊断研究

以分布式飞行控制计算机为对象,设计相应的故障诊断方法。提出了一种硬件余度和模型解析余度相结合的方法,对计算机外部传感器、外部执行机构以及计算机内部功能模块进行故障诊断。通过设计诊断体系结构,能够有效进行外部传感器、外部执行机构和飞行控制计算机内部数字控制器的故障检测和隔离,解决了传统单一故障观测器无法诊断计算机内部故障的不足,提高了飞行控制系统的故障诊断覆盖率和可靠性。     

空调系统传感器故障诊断方法

针对空调系统中常见的传感器故障问题,提出了基于小波神经网络(WNN)故障诊断策略.在分析空调系统中传感器主要故障的基础上,建立了传感器故障诊断系统.通过传感器的真实测量值与预测值的残差比较,验证了基于WNN的故障诊断能力,分析了基于WNN与BP神经网络故障诊断的残差比结果.仿真实验表明,基于WNN的故障诊断系统具有结构...     

多旋翼无人机执行机构故障重构技术研究

多旋翼无人机应用中一旦发生执行机构故障,将会危及无人机、地面人员与周围环境的安全。研究多旋翼无人机的执行机构故障重构技术有利于对其实施容错控制,提高运行的安全性和可靠性。首先对多旋翼无人机执行机构故障进行分类,建模分析了执行机构卡死和失效两类故障,建立了故障下六旋翼无人机的数学模型,然后分别设计基于自适应观测器的故障重构方法。通过选取合适的自适应律,自动调节非线性观测器的参数,实现对故障信息的精确重构。仿真结果证明了故障重构方法的准确性。     

基于自适应状态观测器的异步电机无速度传感器矢量控制系统

异步电机无速度传感器矢量控制系统是目前研究的热点,本文采用一种闭环磁链观测器,即自适应状态观测器对转子磁链进行观测,与传统开环电压、电流模型相比,观测效果更好。在转子磁链观测的基础上,采用PI型自适应律,对转速进行了辨识。最后,通过Matlab仿真验证了本文给出的异步电机无速度传感器矢量控制系统的可行性,仿真结果表明该系统具有较好的动、静态性能,并具有一定的抗干扰能力。     

基于CA-CMAC的快速传感器故障诊断方法

 针对动态系统的在线故障诊断问题,将信度分配小脑神经网络CA-CMAC(Credit Assigned Cerebellar Model Articulation Controller)应用于主元分析模型,实现多传感器在线故障检测与隔离.首先,应用传感器正常工作时测量的历史数据,由主元分析模型得到所有传感器的预测值;接着计算传感器系统的均方预期误差值SPE(Squared Prediction Error),由SPE值的变化,判定是否发生故障,根据重构单个传感器信号的SPE值来隔离故障传感器;最后应用一个多传感器故障诊断仿真实例说明了该算法的可行性,并通过与误差反传BP(Back Propagation)神经网络和常规小脑神经网络CMAC(Cerebellar Model Articulation Controller)进行比较,说明了基于CA-CMAC的主元分析模型的优越性.     

基于反步自适应技术的四旋翼无人机执行器故障问题研究

四旋翼无人机是一个具有多变量、强耦合、强非线性特性的欠驱动非稳定被控对象,快速准确地进行故障诊断对实现无人机安全飞行具有重要意义。基于Newton-Euler运动定理建立四旋翼无人机动力学方程,针对四旋翼无人机执行器故障基于反步法、Lyapunov理论结合自适应技术推导出自适应律,反解出滚转角、俯仰角的期望值,从而进行在线故障估计,取代用于故障重构的观测器,较好地实现执行器容错控制。通过仿真实验验证了本文所提的反步自适应容错控制方法的有效性。     

Multilayer control of an induction motor drive:A strategic step for automotive applications

Fault tolerance is a critical attribute in automotive electrical and propulsion systems. In this paper, a control scheme is presented that allows an induction motor drive system to operate in the event of multiple sensor failures. Automatic diagnosis of sensor fault and recovery is performed and used to reconfigure the drive system controls to achieve the best performance in lieu of component degradation. This approach couples a new digital delta-hysteresis regulation scheme with a model reference adaptive system scheme in order to provide fault tolerance for both phase-current and rotor position (speed) sensors. Simulation and experimental results are provided to show the effectiveness of the proposed scheme.     

一种新的多传感器故障分类诊断方法研究

本文在分析了现有故障诊断方法的特点及其不适于多传感器故障分类诊断的原因基础上,结合网络形式多输出模糊逻辑系统的单输出模糊逻辑系统传感器故障分类诊断方法,提出了一种新的适合于多传感器故障分类诊断的模糊逻辑系统方法、仿真实验难证了此方法的有效性。     

基于学习向量量化的传感器免疫网络诊断模型

为克服传感器免疫网络连接权值获取困难,提出了一种基于学习向量量化的传感器免疫网络诊断模型.该模型将学习向量量化(LVQ)的概念引入到传感器免疫网络模型中,在训练模式下,LVQ用于提取传感器正常工作下的相关性;在诊断模式下,根据LVQ获取的知识即可以确定传感器之间的检测结果,同时给出了诊断模型的性能优化算法.航空发动机传感器的仿真结果表明,所提出的方法能够准确获得网络节点之间的连接权值,保证免疫网络具有较高的检测灵敏度.     

一种基于模糊聚类的故障诊断方法

电子设备的多个传感器实时反映了设备运行状态,对一种基于模糊聚类的电子设备故障诊断方法进行讨论,针对电子装备多个传感器状态信息采用模糊聚类的方法进行融合,进而提出了对于观测数据运用模糊聚类方法进行故障诊断,推理故障模式的方法。实例证明该模糊聚类方法成功地完成了某电子装备故障诊断的自动推理。该方法可以不依赖于被诊断系统的数学模型进行自适应诊断,实现故障诊断的智能化、自动化。     

Intelligent sensors: a functional view

This paper presents the generic model of intelligent instruments and specifies it for intelligent sensors, from a functional point of view, i.e., from the point of view of the services they offer to the system designer. Basic services are concerned with data estimation and data characterization, both based on local transducers and on the availability of remote signals. Advanced services are concerned with data validation, through fault detection and isolation procedures, and with fault tolerance, by means of accommodation and reconfiguration strategies. Finally, the integration of intelligent sensors in distributed control systems is discussed.     

Simple and effective open switch fault diagnosis of single-phase PWM rectifier

In order to obtain lower harmonics distortion and higher power factors, single-phase pulse-width modulation (PWM) rectifiers are adopted in AC railway drive systems. Therefore, its reliability is of most importance with regard to the safe operation of the train. In this paper, a fault diagnosis method for open switch fault in single-phase PWM rectifier is proposed based on the switching system theory. It requires no additional sensor, nor extra operation states need to be set. Four observers which correspond to four kinds of open switch faults are utilized to detect and locate the faults. Real-time simulations are carried out to validate the effectiveness of this method.     

A heterogeneous fault diagnosis approach to enhance performance of connected vehicles

Evolution of wireless access technology, availability of smart sensors, and reduction in the size of the set up of the communication system have engrossed many researchers toward vehicular ad hoc network (VANET). Vehicle-to-vehicle and vehicle-to-access-point communication in a vehicular environment facilitates the deployment of VANET for many different purposes. The success of any application implemented in a VANET relies on timely and accurate data dissemination across the nodes of the network. Implementation of any application is not going to be fruitful if the communication unit transmits incorrect sensor data due to the presence of a fault. This article focuses on the automatic detection of hard and soft faults for vehicular sensors and the classification of faults into permanent, intermittent, and transient faults using cloud-based VANET. For the cloud service, ThingSpeak cloud is used. At the RSU of the VANET, hard fault detection is performed, and for this purpose, a time-out strategy is proposed. The observation center, after receiving sensor status data over a vehicular cloud, does soft failure detection. The soft fault is identified by utilizing a comparative-based technique during soft fault diagnosis. Soft faults are categorized using two machine learning algorithms: Support vector machine and logistic regression. The effectiveness of the suggested work is assessed using performance metrics like fault detection accuracy, false alarm rate, false positive rate, precision, accuracy, recall, and F1 score.     

综合表决的传感器故障检测方法及其应用

针对传感器不具有等精度的特征,提出了一种改进的均值一偏差积检测法,通过加权变换的方法将不等精度的传感器,根据其精度分别赋予不同的权值,组成了综合表决系统,进行多传感器冗余系统的故障检测。本方法不受传感器等精度和故障传感器输出不能相同等假设条件的限制,既减弱了使用的假设条件,扩大了应用范围;又提高了检测的正确率,有利于提高数据融合精度和降低设备成本。     

IRS: application reconfiguration scheme for wireless sensor networks

Application reconfiguration is essential to achieving flexibility and adaptability of wireless sensor networks (WSNs) used in environment monitoring. In this paper, we present an integrated reconfiguration scheme (IRS) for implementing environment adaptive application reconfiguration (EAAR) in WSNs. In our scheme, application reconfiguration is implemented with the push‐based paradigm for densely distributed nodes and the cluster‐based hybrid reconfiguration (CHR) paradigm for sparsely distributed nodes. We demonstrate the energy‐efficiency and scalability of our scheme by analyzing the energy consumption based on a randomly deployed sensor network. Moreover, we derive the density threshold of reconfiguration nodes (RNs) for determining if the nodes are densely or sparsely distributed, and choose the mode of operation for IRS. We use extensive simulation experiments to demonstrate the effectiveness of our scheme. Copyright © 2009 John Wiley & Sons, Ltd.