Fast Convergence in Consensus Control of Leader-Follower Multi-Agent Systems

In this thesis, different distributed consensus control strategies are introduced for a multi-agent network with a leader-follower structure. The proposed strategies are based on the nearest neighbor rule, and are shown to reach consensus faster than conventional methods. Matrix equations are given to obtain equilibrium state of the network based on which the average-based control input is defined accordingly. Two network control rules are subsequently developed, where in one of them the control input is only applied to the leader, and in the other one it is applied to the leader and its neighbors. The results are then extended to the case of a time-varying network with switching topology and a relatively large number of agents. The convergence performance under the proposed strategies in the case of a time-invariant network with fixed topology is evaluated based on the location of the dominant eigenvalue of the closed-loop system. For the case of a time-varying network with switching topology, on the other hand, the state transition matrix of the system is investigated to analyze the stability of the proposed strategies. Finally, the input saturation in agents' dynamics is considered and the stability of the network under the proposed methods in the presence of saturation is studied

Multi-Agent Systems

This Special Issue ""Multi-Agent Systems"" gathers original research articles reporting results on the steadily growing area of agent-oriented computing and multi-agent systems technologies. After more than 20 years of academic research on multi-agent systems (MASs), in fact, agent-oriented models and technologies have been promoted as the most suitable candidates for the design and development of distributed and intelligent applications in complex and dynamic environments. With respect to both their quality and range, the papers in this Special Issue already represent a meaningful sample of the most recent advancements in the field of agent-oriented models and technologies. In particular, the 17 contributions cover agent-based modeling and simulation, situated multi-agent systems, socio-technical multi-agent systems, and semantic technologies applied to multi-agent systems. In fact, it is surprising to witness how such a limited portion of MAS research already highlights the most relevant usage of agent-based models and technologies, as well as their most appreciated characteristics. We are thus confident that the readers of Applied Sciences will be able to appreciate the growing role that MASs will play in the design and development of the next generation of complex intelligent systems. This Special Issue has been converted into a yearly series, for which a new call for papers is already available at the Applied Sciences journal’s website: https://www.mdpi.com/journal/applsci/special_issues/Multi-Agent_Systems_2019