State Estimation for Nonlinear Complex Dynamical Networks With Random Coupling Strengths: A Decode-and-Forward Relay-Based Strategy
研究了有限时域内一类具有随机耦合强度和丢包的非线性复杂动态网络的H∞状态估计问题，提出基于解码转发中继的策略来提升通信质量，并给出了估计器增益的求解方法。
This article is concerned with the finite-horizon <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty}$</tex-math> </inline-formula> state estimation problem for a specific class of nonlinear complex dynamical networks (CDNs) which are subject to random couplings and packet dropouts. The random coupling strengths among network nodes are characterized by a set of random variables with known statistical information. Three sequences of Bernoulli distributed random variables are utilized to model the packet dropouts over different communication channels. A decode-and-forward relay-based strategy is implemented to enhance the quality of communication by controlling the signal transmission in each sensor-to-estimator channel. The primary goal of this investigation is to create an appropriate state estimator for each node of the CDN, enabling the fulfillment of a specific <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty}$</tex-math> </inline-formula> performance requirement for the estimation error dynamics over a finite horizon. Through the use of stochastic analysis techniques and matrix operations, a preliminary sufficient condition is given to meet the finite-horizon <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty}$</tex-math> </inline-formula> performance requirement. The expected estimator gains are subsequently determined, which are defined in terms of the solutions to a series of recursive matrix inequalities. The effectiveness of the proposed relay-based estimation scheme is ultimately demonstrated through a numerical example.