Distributed Output Consensus for Heterogeneous Multiagent Systems With Markov Packet Loss

This article investigates the mean-square output consensus problem for heterogeneous linear multiagent systems (MASs) over random packet loss channels. Agent heterogeneity is reflected in possibly different state dimensions and dynamic parameters. In addition to heterogeneity, a major challenge arises from relaxing the commonly adopted independent and identically distributed (i.i.d.) assumption on packet losses. To capture temporal correlations that are prevalent in practice, packet losses are modeled by a discrete-time Markov process. Since existing consensus controllers designed for i.i.d. losses may fail under Markovian packet losses, novel dedicated control schemes are developed. Two packet loss scenarios are considered: identical and nonidentical packet losses. For identical packet losses, where all channels drop packets simultaneously, both analytical and numerical consensus conditions are derived to guarantee consensus of the distributed observers. The analytical condition reveals the interplay among packet loss rate, communication topology, and system dynamics, while the numerical conditions are more computationally tractable. An output-regulation-based controller is then designed to achieve mean-square output consensus. For the more general case of nonidentical packet losses, edge Laplacian theory is employed to decouple packet loss processes from the communication topology, leading to consensus conditions for the distributed observers, as well as corresponding controllers that guarantee mean-square output consensus. Finally, numerical simulations are utilized to validate the results.