Distance-Driven Consensus Quantification

Distributed cooperative control requires that every participant shares a consistent view of objectives and the world. Information is periodically disseminated over a noisy time-varying network topology so that all the agents asymptotically converge to a common value. However, the strict global consensus is of excessive resource consumption and not mandatory for the majority of coordination tasks. To better satisfy such quantitative requirements of consensus in the practical multi-agent systems, this paper proposes a real time and distance-driven consensus quantification model especially for C-ITS applications. This model encodes agents' spatial location distribution into their mutual consensus quantification through introducing their inter-distance into consensus calculation. Accordingly, this paper proposes a distance-driven-consensus-based power adaptive control method as a practical use case of the quantitative framework of consensus, by which agents can autonomously optimize the transmit power through balancing the desired consensus benefit and power cost according to the real timely predicted local consensus. We perform extensive numerical calculations to investigate the effectiveness and the applicability of the consensus quantification framework and the power adaptive control method. The results show that the model can effectively capture the real time consensus fluctuation as the multi-agent systems evolve and can provide reliable decision basis to cooperative control, in such way to restrict the consensus extent to a target value and to tradeoff between the anticipated consensus level and the paid cost accordingly.