ON ATTACK-RESILIENT DISTRIBUTED FORMATION CONTROL IN OPERATOR-VEHICLE NETWORKS

This paper tackles a distributed formation control problem where a group of vehicles is remotely controlled by a network of operators. Each operator-vehicle pair is attacked by an adversary, who corrupts the commands sent from the operator to the vehicle. From the point of view of operators, each adversary follows an attacking strategy linearly parameterized by some (potentially time-varying) matrix which is unknown a priori. In particular, we consider two scenarios depending upon whether adversaries can adapt their attacking tactics online. To assure mission completion in such a hostile environment, we propose two novel attack-resilient distributed control algorithms that allow operators to adjust their policies on the fly by exploiting the latest collected information about adversaries. Both algorithms enable vehicles to asymptotically achieve the desired formation from any initial configuration and initial estimate of the adversaries' strategies. It is further shown that the sequence of the distances to the desired formation is square summable for each proposed algorithm. In numerical examples, the convergence rates of our algorithms are exponential, outperforming the theoretic results.