Abstract
This paper investigates the adaptive distributed synchronization control problem of multi-agent systems, where the information transmission in the network is subject to unknown constant signal transmission delay. Firstly, an adaptive distributed control scheme is designed for a class of certain multi-agent systems to guarantee the synchronization among all agents is achieved. In the scheme, the unknown constant delay can be estimated online. Furthermore, compared with some existing works where some global information are required to design the controllers for the agents, the scheme proposed in our paper does not need the global information. By algebraic graph theory with Lyapunov stability theory, the whole closed-loop system stability analysis is provided. Then, the method is extended to a class of uncertain nonlinear MASs. Finally, genetic regulatory networks (GRNs) are used to show the effectiveness of the proposed design scheme.
Highlights
Extensive attention has been played to the control designing of networked dynamical systems (NDSs) or multi-agent systems (MASs)
The control problem considered in this paper is, for each agent in the network defined by (1) or (2), to take the time delay in signal transmission into account, to utilize local information and to propose a distributed control input such that it can asymptotically synchronizes to the other agents
Different from [24] and [25], this paper investigates the adaptive distributed synchronization control problem of multi-agent systems, where the information transmission in the network is subject to unknown constant signal transmission delay
Summary
Extensive attention has been played to the control designing of networked dynamical systems (NDSs) or multi-agent systems (MASs). Shen: Distributed Synchronization Control With Signal Transmission Delay and Its Applications of the singular values of graph adjacency and Laplacian matrices should be known for each agent in the graph that the results in [4]–[6], [8]–[10], [21] are obtained. (2) Different from [4]–[6], [8]–[10], [21] where some global information are assumed to be known for each agent in the graph and used in distributed control designing, in our work, the above assumption is not needed, and we can obtains the estimation value of the upper boundary by proper adaptive mechanism
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