共查询到18条相似文献,搜索用时 46 毫秒
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随着系统变得越发复杂和庞大,故障的出现会直接或间接影响系统的性能,因此,针对故障系统的研究越来越引起学者们的关注。针对故障系统的故障特点,将故障系统分为执行器故障系统和传感器故障系统,并进一步分别概括出现有执行器故障系统和传感器故障系统的状态估计方法。针对每种系统的基本特点,分别介绍了相应的状态估计研究进展,适用条件和应用范围。最后综述了故障系统状态估计方法目前诚待解决的问题。 相似文献
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针对一类不确定连续线性定常时滞系统,提出了一种执行器、传感器增益故障的鲁棒检测与估计策略。该类系统含有多状态与输出时滞,状态和输出方程上同时作用有非结构有界未知扰动。在Trunov和Polycarpou方法的基础上,设计了一种新的时滞系统自适应观测器用于检测并估计突变或缓变的增益故障。与Wang等针对线性无时滞不合输出扰动系统的工作相比,该结论更具一般性。理论分析表明,该方法对于未知扰动鲁棒,能够保证故障的估计,状态与输出估计偏差一致有界。数值仿真验证了方法的有效性。 相似文献
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引风机作为大型机械动力设备,它的安全性和可靠性非常重要,传统的故障检测方式是对设备进行故障诊断,这是一种事后检修方式,电站设备一旦因为故障停机,会带来财产损失,甚至会有人员的伤亡.因此如何用其他的方法在故障发生前发现设备出现问题——也就是实现故障预警,就显得尤为重要.本文以引风机为例,使用主成分分析法,小波降噪法优化数... 相似文献
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针对具有参数不确定和延迟环节的马尔科夫跳变系统,在状态转移概率矩阵(Transition probability matrix,TPM)不确定的情形下,讨论了其执行器和传感器故障同时估计的方法.通过扩展系统状态,将系统转换为一个具有马尔科夫跳变参数的广义描述系统,基于此广义描述系统设计马尔科夫跳变观测器实现对其状态和传感器故障的估计.与此同时,还设计了一组自适应律对执行器故障进行在线调节.通过求解一组线性矩阵不等式最优化问题,得到观测器存在的充分条件.最后,针对两个数值实例,验证了所设计方法的有效性. 相似文献
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本文讨论了一类具有不确定噪声的连续广义线性系统的鲁棒状态估计问题,文章提供了一种比较实用的状态估计方法.针对噪声不确定性,文章采用对策论的基本原理,导出了一种最小化不确定下最坏性能的极小极大鲁棒状态估计器. 相似文献
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作为自主式水下机器人(AUV)的重要组成部分,执行机构的可靠性对系统的安全运行具有重要意义.本文以AUV六自由度模型为基础,提出了一种基于自适应阈值与扩张状态滑模观测器相结合的故障检测与估计机制.首先,本文将模型中除去控制输入的部分扩张成新的系统状态,得到估计值和实际值之间的残差;其次,针对执行机构的未知扰动,文章设计了一种改进的自适应阈值以监测残差的变化,进一步降低了误诊率与漏诊率;随后,文章在扩张状态的结构基础上设计滑模观测器,将观测器的增益求解转化为线性矩阵不等式(LMI)约束优化问题;最后,通过动态滑模面的设计实现了抖振的抑制并论证了该动态滑模面的收敛性,同时引入等效控制输出误差注入原理,实现了AUV执行机构的故障重构.仿真结果表明,本文所提方法对AUV执行机构的故障具有较好的检测灵敏度和估计精度. 相似文献
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In this paper, we propose a simultaneous state estimation and fault estimation approach for a class of first‐order hyperbolic partial integral differential equation systems. Specifically, we consider the multiplicative boundary actuator and sensor faults, ie, unknown fault parameters multiplying by the boundary input or boundary state (ie, output). As a consequence, two difficulties arise immediately: (1) simultaneous estimation of both plant state and faults is a nonlinear problem due to the multiplication between fault parameters and plant signals; (2) no prior information is available to determine the type (actuator or sensor) of faults. To overcome these difficulties, this paper develops adaptive fault parameter update laws and embeds the resulting laws into the plant state observer design. First, we propose new approaches to estimate actuator fault and sensor fault, respectively. Next, we develop a novel method to simultaneously estimate actuator and sensor faults. The proposed observer and update laws, designed using only one boundary measurement, ensure both state estimation and fault parameter estimation. By choosing appropriate Lyapunov functions, we prove that the estimates of state and fault parameters converge to an arbitrarily small neighborhood of their true values. Numerical simulations are used to demonstrate the effectiveness of the proposed estimation approaches. 相似文献
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The problem of linear systems subject to actuator faults(outage,loss of efectiveness and stuck),parameter uncertainties and external disturbances is considered.An active fault compensation control law is designed which utilizes compensation in such a way that uncertainties,disturbances and the occurrence of actuator faults are account for.The main idea is designing a robust adaptive output feedback controller by automatically compensating the fault dynamics to render the close-loop stability.According to the information from the adaptive mechanism,the updating control law is derived such that all the parameters of the unknown input signal are bounded.Furthermore,a disturbance decoupled fault reconstruction scheme is presented to evaluate the severity of the fault and to indicate how fault accommodation should be implemented.The advantage of fault compensation is that the dynamics caused by faults can be accommodated online.The proposed design method is illustrated on a rocket fairing structural-acoustic model. 相似文献
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This paper addresses the problem of interval observer design for unknown input estimation in linear time-invariant systems. Although the problem of unknown input estimation has been widely studied in the literature, the design of joint state and unknown input observers has not been considered within a set-membership context. While conventional interval observers could be used to propagate with some additional conservatism, unknown inputs by considering them as disturbances, the proposed approach allows their estimation. Under the assumption that the measurement noise and the disturbances are bounded, lower and upper bounds for the unmeasured state and unknown inputs are computed. Numerical simulations are presented to show the efficiency of the proposed approach. 相似文献