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本文研究了离散不确定非线性时滞系统在网络传输不可靠情况下的状态估计问题.针对网络传输丢包问题,采用伯努利(Bernoulli)随机模型,建立了控制信号和输入信号的不可靠传输模型.本文通过状态扩展的方法处理不确定非线性项,得到了扩展状态系统.基于不可靠的控制和测量信息,设计了状态预测器和估计器,并给出相应的误差系统.通过设计最优估计器增益,本文给出了状态预测误差协方差的迭代公式.为了进一步提高状态估计器的精度,设计了一种新型的参数迭代优化方法.针对状态预测误差协方差,本文得到了其稳定性的判别准则.最后,通过一例数值仿真,验证了所得结论的有效性. 相似文献
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考虑到带宽有限网络环境下信号需经过量化处理才能进行发送,研究了一类带宽受限随机网络控制系统的L_2-L_∞滤波问题。采用对数量化器,将量化后的测量信号作为滤波器输入信号。首先将滤波误差系统建模成范数有界不确定随机时滞系统,进一步基于线性矩阵不等式方法推出了该随机网络控制系统的稳定性和滤波器设计的充分条件,并将滤波器的设计转化为一个凸优化的求解问题。所设计的滤波器能够保证相对于所有能量有界的外界扰动信号,随机网络控制系统的L_2-L_∞性能指标小于一定值γ。仿真实例证实了该设计方法的有效性。 相似文献
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考虑一类已知状态和测量时滞且范数有界参数不确定连续时间系统的鲁棒H∞状态估计问题.这个问题解的充分条件由二个代数Riccati不等式给出,它可以保证存在一个渐近稳定状态估计器使得对于所有不确定性从外界干扰到输出估计误差的传递函数满足指定的H∞指标.以上这些结果可以推广到一类未知状态和测量时滞且范数有界参数不确定连续系统的鲁棒H∞状态估计问题,对于已知状态和测量时滞系统,所得状态估计器与参数不确定性无关,而与时滞有关.对于未知状态和测量时滞系统,其状态估计器不仅与参数不确定性无关,而且与时滞也无关. 相似文献
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具有状态和测量时滞不确定系统的鲁棒H∞状态估计 总被引:1,自引:0,他引:1
考虑一类已知状态和测量时滞且范数有界参数不确定连续时间系统的鲁棒H∞状态估计问题. 这个问题解的充分条件由二个代数Riccati不等式给出, 它可以保证存在一个渐近稳定状态估计器使得对于所有不确定性从外界干扰到输出估计误差的传递函数满足指定的H∞指标. 以上这些结果可以推广到一类未知状态和测量时滞且范数有界参数不确定连续系统的鲁棒H∞状态估计问题, 对于已知状态和测量时滞系统, 所得状态估计器与参数不确定性无关, 而与时滞有关. 对于未知状态和测量时滞系统, 其状态估计器不仅与参数不确定性无关, 而且与时滞也无关. 相似文献
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本文研究总比特率给定下随机向量参数分布式量化估计及其最优比特分配问题.与现有文献大都假定每个传感器的量化比特率给定而不是最优分配下研究随机性参数的分布式量化估计问题不同的是,本文将综合考虑最优量化器、最优估计器算法以及给定总比特率下的最优比特分配问题.针对向量状态标量观测模型,首先借助现有文献给出基于量化观测的最优估计器及其误差协方差阵形式表达,其次得到各传感器的渐近最优量化器实际为著名的Lloyd-max量化器,且各传感器的渐近最优量化级数与信噪比成正比,同时引入一种次优的求解非负整数比特率的方法.考虑到当传感器数目比较大时,初始的最优估计器算法运算量很大,设计了一种渐近等价的迭代量化估计器算法,其计算负担大大减轻,且对于存在延迟或丢包的网络环境亦适用,增强了算法的鲁棒性.仿真结果表明,本文提出的最优比特分配方案估计性能明显优于一般的均匀比特分配方案. 相似文献
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This paper studies an optimal state estimation (Kalman filtering) problem under the assumption that output measurements are subject to random time delays caused by network transmissions without time stamping. We first propose a random time delay model which mimics many practical digital network systems. We then study the so‐called unbiased, uniformly bounded linear state estimators and show that the estimator structure is given based on the average of all received measurements at each time for different maximum time delays. The estimator gains can be derived by solving a set of recursive discrete‐time Riccati equations. The estimator is guaranteed to be optimal in the sense that it is unbiased with uniformly bounded estimation error covariance. A simulation example shows the effectiveness of the proposed algorithm. Copyright © 2013 John Wiley & Sons, Ltd. 相似文献
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A decision theoretic approach to estimation of unknown random and nonrandom parameters from a linear measurements model is proposed, when the a priori statistics are incomplete and only a small number of data points are available. The unknown statistics are partially characterized by considering two regions in the measurement space, namely, good and bad data regions and constraining the partial probability, the partial covariance, or the combination thereof of the measurements. The random parameter is assumed to be Gaussian variable with known mean and known covariance. Choosing the minimum covariance criterion, the min-max estimator is found to be soft-limiter or tangent type nonlinear function depending upon the a priori statistic available. The estimator for the unknown nonrandom parameter is obtained from the root of some function of the residuals, the function being obtained by minimizing the error covariance. The estimator obtained is similar to a random parameter case. 相似文献
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In this paper, a distributed extended Kalman filtering problem is studied for discrete‐time nonlinear systems with multiple fading measurements. To alleviate the network communication burden, the event‐triggered communication scheme is employed in both sensor‐to‐estimator channel and estimator‐to‐estimator channel. As such, the data transmission is executed only when the predefined event occurs. In addition, a set of independent random variables with known statistical properties is defined to represent the phenomenon of multiple fading measurements. The variance‐constrained approach is adopted to derive an upper bound for the estimation error covariance in consideration of the event‐triggered mechanism and truncated error by linearization. The filter gain for each node is then designed to minimize such an upper bound by recursively solving two Raccati‐like difference equations. By virtue of the stochastic stability theory, a sufficient condition is provided to guarantee the boundedness of the estimation error. Finally, a simulation example is presented to illustrate the feasibility and effectiveness of the proposed filtering algorithm. 相似文献
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Recursive filtering for state‐saturated systems with randomly occurring nonlinearities and missing measurements
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This paper deals with the filtering problem for a class of discrete‐time state‐saturated systems subject to randomly occurring nonlinearities and missing measurements. A set of mutually independent Bernoulli random variables is used to describe the random occurrence of the missing measurements. Due to the simultaneous consideration of the state saturation, the randomly occurring nonlinearities, and the missing measurements, it is extremely hard to calculate the actual filtering error covariance in a closed form. As such, the objective of this paper is to construct an upper bound for the filtering error covariance and then design the filter parameters to minimize such an upper bound. The performance of the proposed filters is analyzed in terms of boundedness and monotonicity. Specially, we have shown that the minimum upper bound is always bounded under a mild assumption. Moreover, the relationship between the estimator performance and the arrival probability of the measurements is discussed. A numerical simulation is used to demonstrate the effectiveness of the filtering method. 相似文献
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This paper develops an adaptive state estimator design methodology for nonlinear systems with unknown nonlinearities and persistently bounded disturbances. In the proposed estimation scheme, the boundary layer strategy in variable structure techniques is utilized to design a continuous state estimator such that the undesirable chattering phenomenon is avoided; and the adaptive bounding technique is used for online estimation of the unknown bounding parameter. The existence condition of the adaptive estimators is provided in terms of linear matrix inequality (LMI). Since the orthogonal projection of the state estimation error onto the null space of the linear measurement distribution matrix is used in the derivation process, the update law of bounding parameter estimate is represented in terms of the available measurement error. The proposed estimator can ensure that the state estimation error is uniformly ultimately bounded (UUB) with an ultimate bound. Furthermore, using the existing LMI optimization technique, a suboptimal adaptive state estimator can be obtained in the sense of minimizing an upper bound of the peak gains in the ultimate bound. Finally, a simulation example is given to illustrate the effectiveness of the proposed design method. Copyright © 2010 John Wiley & Sons, Ltd. 相似文献
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针对带宽受限和网络拓扑随机切换约束下组网导航系统(NNSs)的分布式位姿状态估计问题,建立网络约束综合作用下的离散组网滤波增广系统模型,提出一种分布式有限时域FIR融合估计算法.目标节点从邻节点集合中接收经量化效应和饱和效应处理后的普通数据包和跟踪数据包,在给出无偏约束条件的前提下,以使得状态估计值的估计误差满足最小方差为准则,充分考虑有色噪声的影响,设计有限时域FIR估计器及其差分形式,通过普通数据包得到目标节点状态的区域估计值,建立系统本地状态估计的统一机制.同时,考虑网络约束,将跟踪数据包引入系统的融合过程,在以均方准则确定时变加权矩阵的前提下,给出最优权值所满足的线性代数方程以及融合误差协方差的差分形式,将目标节点状态的区域估计值与各邻节点随机发送的协作估计值加权融合,得到目标节点状态的全局融合估计值.最后通过算例仿真验证算法的有效性. 相似文献
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In this paper, the joint input and state estimation problem is considered for linear discrete-time stochastic systems. An event-based transmission scheme is proposed with which the current measurement is released to the estimator only when the difference from the previously transmitted one is greater than a prescribed threshold. The purpose of this paper is to design an event-based recursive input and state estimator such that the estimation error covariances have guaranteed upper bounds at all times. The estimator gains are calculated by solving two constrained optimisation problems and the upper bounds of the estimation error covariances are obtained in form of the solution to Riccati-like difference equations. Special efforts are made on the choices of appropriate scalar parameter sequences in order to reduce the upper bounds. In the special case of linear time-invariant system, sufficient conditions are acquired under which the upper bound of the error covariance of the state estimation is asymptomatically bounded. Numerical simulations are conducted to illustrate the effectiveness of the proposed estimation algorithm. 相似文献
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Mark M. Kogan 《International journal of control》2013,86(6):1216-1226
In this paper, we consider a minimax approach to the estimation and filtering problems in the stochastic framework, where covariances of the random factors are completely unknown. The term ‘random factors’ refers either to unknown parameters and measurement noise in the estimation problem or to disturbance process and the initial state of a linear discrete-time dynamic system in the filtering problem. We introduce a notion of the attenuation level of random factors as a performance measure for both a linear unbiased estimate and a filter. This is the worst-case variance of the estimation error normalised by the sum of variances of all random factors over all nonzero covariance matrices. It is shown that this performance measure is equal to the spectral norm of the ‘transfer matrix’ and therefore the minimax estimate and filter can be computed in terms of linear matrix inequalities (LMIs). Moreover, the explicit formulae for both the minimax estimate and the minimal value of the attenuation level are presented in the estimation problem. It turns out that the above attenuation level of random factors coincides with the attenuation level of deterministic factors that is the worst-case normalised squared Euclidian norm of the estimation error over all nonzero sample values of random factors. In addition, we demonstrate that the LMI technique can be applied to derive the optimal robust estimator and filter, when there is a priori information about convex polyhedral sets which unknown covariance matrices of random factors belong to. Two illustrative examples show advantages of the minimax approach proposed. 相似文献