This paper describes an algorithm for the control of a swarm of UAVs based on decentralized MPC. For each UAV, our algorithm first determines the trajectory taking into account the obstacles and the constraints on the aircraft performance. Then basing on a robust MPC algorithm, optimal guidance laws are calculated and tracked by the UAVs by means of local PIDs controllers. Our approach also allows us to take into account moving obstacles and constraints on the minimum distance between the vehicles. Validation of the approach is obtained by means of simulations where for each UAV a 6-DOF model is used.
Model predictive control for a swarm of fixed wing UAVs / Ariola, M.; Mattei, M.; D'Amato, E.; Notaro, I.; Tartaglione, G.. - (2016). ( 30th Congress of the International Council of the Aeronautical Sciences, ICAS 2016 Daejeon Convention Center (DCC), kor 2016).
Model predictive control for a swarm of fixed wing UAVs
Mattei M.;
2016
Abstract
This paper describes an algorithm for the control of a swarm of UAVs based on decentralized MPC. For each UAV, our algorithm first determines the trajectory taking into account the obstacles and the constraints on the aircraft performance. Then basing on a robust MPC algorithm, optimal guidance laws are calculated and tracked by the UAVs by means of local PIDs controllers. Our approach also allows us to take into account moving obstacles and constraints on the minimum distance between the vehicles. Validation of the approach is obtained by means of simulations where for each UAV a 6-DOF model is used.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
