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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >State Estimation for Discrete-Time Complex Networks With Randomly Occurring Sensor Saturations and Randomly Varying Sensor Delays
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State Estimation for Discrete-Time Complex Networks With Randomly Occurring Sensor Saturations and Randomly Varying Sensor Delays

机译：具有随机发生的传感器饱和和随机变化的传感器延迟的离散时间复杂网络的状态估计

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摘要

In this paper, the state estimation problem is investigated for a class of discrete time-delay nonlinear complex networks with randomly occurring phenomena from sensor measurements. The randomly occurring phenomena include randomly occurring sensor saturations (ROSSs) and randomly varying sensor delays (RVSDs) that result typically from networked environments. A novel sensor model is proposed to describe the ROSSs and the RVSDs within a unified framework via two sets of Bernoulli-distributed white sequences with known conditional probabilities. Rather than employing the commonly used Lipschitz-type function, a more general sector-like nonlinear function is used to describe the nonlinearities existing in the network. The purpose of the addressed problem is to design a state estimator to estimate the network states through available output measurements such that, for all probabilistic sensor saturations and sensor delays, the dynamics of the estimation error is guaranteed to be exponentially mean-square stable and the effect from the exogenous disturbances to the estimation accuracy is attenuated at a given level by means of an H∞-norm. In terms of a novel Lyapunov-Krasovskii functional and the Kronecker product, sufficient conditions are established under which the addressed state estimation problem is recast as solving a convex optimization problem via the semidefinite programming method. A simulation example is provided to show the usefulness of the proposed state estimation conditions.
机译：在本文中，研究了一类离散时滞非线性复杂网络的状态估计问题，该网络具有从传感器测量中随机出现的现象。随机发生的现象包括随机发生的传感器饱和度（ROSS）和随机变化的传感器延迟（RVSD），这些延迟通常是由联网环境引起的。提出了一种新颖的传感器模型，该模型通过两组具有已知条件概率的伯努利分布的白色序列在一个统一的框架内描述ROSS和RVSD。与其采用通常使用的Lipschitz型函数，不如使用更通用的扇形非线性函数来描述网络中存在的非线性。解决该问题的目的是设计一个状态估计器，以通过可用的输出测量值来估计网络状态，从而对于所有概率传感器饱和和传感器延迟，保证估计误差的动态指数均方根稳定，并且通过H ∞范数，在给定水平上减弱了外源性干扰对估计精度的影响。根据新颖的Lyapunov-Krasovskii泛函和Kronecker产品，建立了充分的条件，在该条件下，通过半定规划方法求解凸优化问题，可以重现寻址状态估计问题。提供了一个仿真示例，以显示所提出的状态估计条件的有用性。

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