The state estimation problem of heterogeneous multi-agent systems with random transport
protocol is investigated in this paper. Due to the dependency of the agent dynamics and the …

This article proposes a fault-tolerant adaptive multigradient recursive reinforcement learning
(RL) event-triggered tracking control scheme for strict-feedback discrete-time multiagent …

In order to provide intelligent services, the Internet of Things (IoT) facilitates millions of smart
cyber‐physical devices to be enabled with network connectivity to sense, collect, process …

In this paper, a distributed diffusion unscented Kalman filtering algorithm based on
covariance intersection strategy (DDUKF-CI) is proposed for target tracking with intermittent …

It is difficult to extract effective weak signals from complex noise environment in many
engineering applications. To address the problem, an improved unscented Kalman filter …

For a steam heating network (SHN), which is one of the most important parts of the
integrated energy systems in an industrial park, it is essential to provide accurate and …

Distributed adaptive learning algorithms have played a critical role in signal processing and
parameter estimation over networks. Most existing algorithms are based on the mean …

The problem of distributed constraint-coupled estimation for multi-subsystem is addressed,
in which there exist coupled linear equality constraints generated by cooperating neighbor …

Naturally complex-valued information or those presented in complex domain are effectively
processed by an augmented complex least-mean-square (ACLMS) algorithm. In some …

This paper provides the parallel distributed Kalman filter (PDKF) with non-aligned time
indexes, which uses four processing nodes to run the linear Kalman filter in parallel. To …