On distributed constrained formation control in operator-vehicle adversarial networks

We consider an operator-vehicle network where each vehicle is remotely maneuvered by an operator. The objective of the operators is to steer the vehicles to the desired formation subject to the given state and input constraints. Each operator-vehicle pair is attacked by an adversary who is able to maliciously replay the control commands sent from the operator. To play against attackers, we come up with a novel distributed resilient algorithm based on the receding-horizon control methodology, and show that the algorithm is able to allow vehicles, on the one hand, satisfy state and input constraints, and on the other hand, asymptotically achieve the desired formation despite replay attacks. With slight modifications, our proposed algorithm shows an analogous resilience to denial-of-service attacks.