Resilient Consensus of Higher Order Multiagent Networks: An Attack Isolation-Based Approach
Dan Zhao, Yuezu Lv, Xinghuo Yu, Guanghui Wen, Guanrong Chen
Abstract
Current resilient consensus algorithms deal with multiagent networks with lower order nodal dynamics where each agent excludes certain extreme values from its neighbors. This does not scale well in general networks. In this article, a new type of resilient consensus algorithm based on distributed attack isolation (DAI-RC) is proposed for general higher order networks. In the DAI-RC algorithm, the evolution of each normal agent can avoid the influence from those neighbors which are isolated as victims of attack. To characterize a feasible communication topology to isolate attacked agents, a notion of <i>graph isolability</i> is proposed based on which a sufficient condition to isolate the attacked agents is presented. Simulations are finally provided to illustrate the effectiveness of the proposed algorithm.