ROBUST CONSENSUS AND SOFT CONTROL OF MULTI-AGENT SYSTEMS WITH NOISES

This paper considers the problem of robust consensus for a basic class of multi-agent systems with bounded disturbances and with directed information flow. A necessary and sufficient condition on the robust consensus is first presented, which is then applied to the analysis, control and decision making problems in the noise environments. In particular, the authors show how a soft control technique will synchronize a group of autonomous mobile agents without changing the existing local rule of interactions, and without assuming any kind of connectivity conditions on the system trajectories.

Robust consensus of multi-agent systems with time-varying delays in noisy environment

Over the last ten years, the consensus of multi-agent systems (MAS) has received increasing attention from mechanics, mathematics, physics, engineering sciences, social sciences, and so on. It is well known that the robustness of consensus of MAS is usually determined by several key factors, including noise, time-delays, and packet drop. In this paper, we introduce a general time-delayed MAS model with noise and also further investigate its robust consensus. In particular, we prove that the proposed algorithm is robust against the bounded time-varying delays and bounded noises. The effectiveness and robustness of the proposed consensus algorithm has been validated in the classical Vicsek model with time-varying delays. And two simulation examples are also given to justify the above theoretical results. These results may have some potential applications in various fields, including mechanics, biology, and engineering sciences.

Robust consensus of multi-agent systems with noise

The consensus problem of multi-agent systems has attracted wide attention from researchers in recent years, following the initial work of Jadbabaie et al. on the analysis of a simplified Vicsek model. While the original Vicsek model contains noise effects, almost all the existing theoretical results on consensus problem, however, do not take the noise effects into account. The purpose of this paper is to initiate a study of the consensus problems under noise disturbances. First, the class of multi-agent systems under study is transformed into a general time-varying system with noise. Then, for such a system, the equivalent relationships are established among （i） robust consensus, （ii） the positivity of the second smallest eigenvalue of a weighted Laplacian matrix, and （iii） the joint connectivity of the associated dynamical neighbor graphs. Finally, this basic equivalence result is shown to be applicable to several classes of concrete multi-agent models with noise.

Robust Consensus Tracking of Heterogeneous Multi-Agent Systems Under Switching Topologies

This paper considers a robust consensus tracking problem of heterogeneous multi-agent systems with time-varying interconnection topologies. Based on common Lyapunov function and internal model techniques, both state and output feedback control laws are derived to solve this problem.The proposed design is robust by admitting some parameter uncertainties in the multi-agent system.

Consensus based on learning game theory with a UAV rendezvous application

Multi-agent cooperation problems are becoming more and more attractive in both civilian and military applications. In multi-agent cooperation problems, different network topologies will decide different manners of cooperation between agents. A centralized system will directly control the operation of each agent with information flow from a single centre, while in a distributed system, agents operate separately under certain communication protocols. In this paper, a systematic distributed optimization approach will be established based on a learning game algorithm.The convergence of the algorithm will be proven under the game theory framework. Two typical consensus problems will be analyzed with the proposed algorithm. The contributions of this work are threefold. First, the designed algorithm inherits the properties in learning game theory for problem simplification and proof of convergence. Second, the behaviour of learning endows the algorithm with robustness and autonomy. Third, with the proposed algorithm, the consensus problems will be analyzed from a novel perspective.