Attack-resilient distributed formation control via online adaptation

This paper investigates a distributed formation control problem in an operator-vehicle network where each vehicle is remotely controlled by an operator. Each operator-vehicle pair is attacked by an adversary, who corrupts the commands sent from the operator to the vehicle following a partially unknown strategy. We propose a novel distributed control algorithm that allows operators to adapt their policies online by exploiting the latest collected information about adversaries. The algorithm enables vehicles to asymptotically achieve the desired formation from any initial configuration and initial estimate of the adversaries' strategies. It is shown that the sequence of the distances to the desired formation is summable. A numerical example is provided to illustrate the performance of the algorithm. In particular, we observe that the rate of convergence to the desired formation is exponential, outperforming our theoretical result.