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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >$H_{infty}$ State Estimation for Complex Networks With Uncertain Inner Coupling and Incomplete Measurements
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$H_{infty}$ State Estimation for Complex Networks With Uncertain Inner Coupling and Incomplete Measurements

机译：具有不确定内部耦合和不完整测量的复杂网络的$ H_ {infty} $状态估计

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摘要

In this paper, the $H_{infty}$ state estimation problem is investigated for a class of complex networks with uncertain coupling strength and incomplete measurements. With the aid of the interval matrix approach, we make the first attempt to characterize the uncertainties entering into the inner coupling matrix. The incomplete measurements under consideration include sensor saturations, quantization, and missing measurements, all of which are assumed to occur randomly. By introducing a stochastic Kronecker delta function, these incomplete measurements are described in a unified way and a novel measurement model is proposed to account for these phenomena occurring with individual probability. With the measurement model, a set of $H_{infty}$ state estimators is designed such that, for all admissible incomplete measurements as well as the uncertain coupling strength, the estimation error dynamics is exponentially mean-square stable and the $H_{infty}$ performance requirement is satisfied. The characterization of the desired estimator gains is derived in terms of the solution to a convex optimization problem that can be easily solved using the semidefinite program method. Finally, a numerical simulation example is provided to demonstrate the effectiveness and applicability of the proposed design approach.
机译：在本文中，研究了具有不确定耦合强度和不完整测量的一类复杂网络的$ H_ {infty} $状态估计问题。借助间隔矩阵方法，我们首次尝试表征进入内部耦合矩阵的不确定性。考虑中的不完整测量包括传感器饱和度，量化和丢失的测量，所有这些都假定是随机发生的。通过引入随机的Kronecker增量函数，以统一的方式描述了这些不完整的度量，并提出了一种新颖的度量模型来解决这些以个别概率出现的现象。利用测量模型，设计了一组$ H_ {infty} $状态估计器，以便对于所有可接受的不完整测量以及不确定的耦合强度，估计误差动态指数均方根稳定，而$ H_ {infty } $个性能要求已得到满足。根据凸优化问题的解决方案得出所需估计器增益的特征，该问题可以使用半定程序方法轻松解决。最后，提供了一个数值仿真示例来证明所提出的设计方法的有效性和适用性。

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