An Event-Triggering Approach to Recursive Filtering for Complex Networks With State Saturations and Random Coupling Strengths

Optimized Distributed Filtering for Saturated Systems With Amplify-and-Forward Relays Over Sensor Networks: A Dynamic Event-Triggered Approach

In this article, the optimized distributed filtering problem is studied for a class of saturated systems with amplify-and-forward (AF) relays via a dynamic event-triggered mechanism (DETM). The AF relays are located in the channels between sensors and filters to prolong the transmission distance of signals, where the transmission powers of sensors and relays can be described by a sequence of random variables with a known probability distribution. With the purpose of alleviating the communication burden and preventing data collision, the DETM is used to schedule the transmission cases of nodes by dynamically adjusting the triggered threshold according to the practical requirements. An upper bound matrix (UBM) of the filtering error (FE) covariance is first provided under the sense of variance constraint and the proper filter gain is further constructed via minimizing the proposed UBM. In addition, the boundedness evaluation regarding the trace of the UBM is provided. Finally, simulation experiments are used to illustrate the usefulness of the developed distributed recursive filtering scheme.

A refined adaptive event-based resilient filtering for complex networks over switching topology

The problem of the resilient filtering for a class of discrete-time complex networks over switching topology is investigated. Taking into account the limitation of channel bandwidth, a refined adaptive event-triggered scheme is derived, whose threshold is determined by the change rate of measurement. The large change rate of measurement results in a smaller threshold, which means that more data packets will be transmitted to guarantee the performance of filtering, and the smaller one leads to a bigger threshold to save the network energy. Under the adaptive event-triggered scheme, considering the switching topology and uncertain inner coupling, a resilient filtering with a variable filtering gain is proposed. Additionally, the minimal upper bound of the covariance of estimation error is developed and the sufficient conditions are also given to obtain the exponentially bounded in mean square of the estimation error system. Finally, a simulation is presented to certify the effectiveness of the derived resilient filtering.

Adaptive Exponential Synchronization of Complex Networks With Nondifferentiable Time-Varying Delay

In recent years, the adaptive exponential synchronization (AES) problem of delayed complex networks has been extensively studied. Existing results rely heavily on assuming the differentiability of the time-varying delay, which is not easy to verify in reality. Dealing with nondifferentiable delay in the field of AES is still a challenging problem. In this brief, the AES problem of complex networks with general time-varying delay is addressed, especially when the delay is nondifferentiable. A delay differential inequality is proposed to deal with the exponential stability of delayed nonlinear systems, which is more general than the widely used Halanay inequality. Next, the boundedness of the adaptive control gain is theoretically proved, which is neglected in much of the literature. Then, the AES criteria for networks with general delay are established for the first time by using the proposed inequality and the boundedness of the control gain. Finally, an example is given to demonstrate the effectiveness of the theoretical results.

Adaptive pinning cluster synchronization of a stochastic reaction-diffusion complex network

This work aims to achieve cluster synchronization of a complex network by some pinning control strategies. Firstly, the network not only is affected by the reaction-diffusion and the directed coupling phenomena, but also is disturbed by the stochastic noise and Markovian switching. Secondly, switched constant gain pinning, centralized and decentralized adaptive pinning are proposed respectively to realize the cluster synchronization of the considered network. In these adaptive pinning controllers, the control gain and coupling strength can been adjusted automatically while only a part of the nodes are controlled. Thirdly, the target state of cluster synchronization is taken as the average state related to the directed topology of all nodes in the same cluster, and does not need to be given separately as an isolated node. Finally, to verify the theoretical results, some simulations of directed coupled reaction-diffusion neural networks with stochastic noise and Markovian switching are given.

Recursive Minimum-Variance Filter Design for State-Saturated Complex Networks With Uncertain Coupling Strengths Subject to Deception Attacks

In this article, the recursive filtering problem is investigated for state-saturated complex networks (CNs) subject to uncertain coupling strengths (UCSs) and deception attacks. The measurement signals transmitted via the communication network may suffer from deception attacks, which are governed by Bernoulli-distributed random variables. The purpose of the problem under consideration is to design a minimum-variance filter for CNs with deception attacks, state saturations, and UCSs such that upper bounds on the resulting error covariances are guaranteed. Then, the expected filter gains are acquired via minimizing the traces of such upper bounds, and sufficient conditions are established to ensure the exponential mean-square boundedness of the filtering errors. Finally, two simulation examples (including a practical application) are exploited to validate the effectiveness of our designed approach.