Mixed $H_2/H_\infty$ State Estimation for Discrete-Time Switched Complex Networks With Random Coupling Strengths Through Redundant Channels
Yun Ruo Chen, Zidong Wang, Licheng Wang, Weiguo Sheng · IEEE Transactions on Neural Networks and Learning Systems · 2019
This article investigates the mixed H2/H∞state estimation problem for a class of discrete-time switched complex networks with random coupling strengths through redundant communication channels. A sequence of random variables satisfying certain probability distributions is employed to describe the stochasticity of the coupling strengths. A redundant-channel-based data transmission mechanism is adopted to enhance the reliability of the transmission channel from the sensor to the estimator. The purpose of the addressed problem is to design a state estimator for each node, such that the error dynamics achieves both the stochastic stability (with probability 1) and the prespecified mixed H2/H∞performance requirement. By using the switched system theory, an extensive stochastic analysis is carried out to derive the sufficient conditions ensuring the stochastic stability as well as the mixed H2/H∞performance index. The desired state estimator is also parameterized by resorting to the solutions to certain convex optimization problems. A numerical example is provided to illustrate the validity of the proposed estimation scheme.