.png){kind=icon}
.png){kind=icon}
Return
Consensus With a Linear Constraint
.png){kind=icon}
PRE
AI
DOI：10.1109/TAC.2023.3315688.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this article, we consider a network of agents running a linear or nonlinear consensus algorithm with the following goal: in addition to requiring that the outputs of all agents converge to the same value, we also require that a specified linear function of the agent states remains constant during the evolution of the consensus algorithm. To achieve this goal requires the construction of a matrix of weighting parameters with specific properties. In this article, we present a noniterative centralized algorithm and an iterative decentralized algorithm for determining the weighting parameters. In the decentralized algorithm, the weighting parameters are specified by the agents and each agent only specifies the weighting parameters associated with the agents to which it communicates. The results do not require that the communication graph of the network be bidirectional. The results of this article can also be applied to consensus problems where one wants to achieve consensus to a specified weighting of the initials states of the network.
Keywords:
Resource management
Topology
Null space
Network topology
Behavioral sciences
Laplace equations
Consensus algorithm
Consensus
Journal
IF:
7
Papers:
1.3W
Citations:
6.7W
