Abstract
The exchange of information is a crucial factor in achieving consensus among agents. However, in real-world scenarios, nonideal information sharing is prevalent due to complex environmental conditions. Consider the information distortions (data) and stochastic information flow (media) during state transmission both caused by physical constraints, a novel model of transmission-constrained consensus over random networks is proposed in this work. The transmission constraints are represented by heterogeneous functions that reflect the impact of environmental interference in multiagent systems or social networks. A directed random graph is applied to model the stochastic information flow where every edge is connected probabilistically. Using stochastic stability theory and the martingale convergence theorem, it is demonstrated that the agent states will converge to a consensus value with probability 1, despite information distortions and randomness in information flow. Numerical simulations are presented to validate the effectiveness of the proposed model.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
