Bemporad, Alberto and Rocchi, Claudio Decentralized linear time-varying model predictive control of a formation of unmanned aerial vehicles. In: Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC). IEEE, 7488 -7493. ISBN 978-1-61284-800-6 (2011)Full text not available from this repository.
This paper proposes a hierarchical MPC approach to stabilization and autonomous navigation of a formation of unmanned aerial vehicles (UAVs), under constraints on motor thrusts, angles and positions, and under collision avoidance constraints. Each vehicle is of quadcopter type and is stabilized by a local linear time-invariant (LTI) MPC controller at the lower level of the control hierarchy around commanded desired set-points. These are generated at the higher level and at a slower sampling rate by a linear time-varying (LTV) MPC controller per vehicle, based on an a simplified dynamical model of the stabilized UAV and a novel algorithm for convex under-approximation of the feasible space. Formation flying is obtained by running the above decentralized scheme in accordance with a leader-follower approach. The performance of the hierarchical control scheme is assessed through simulations, and compared to previous work in which a hybrid MPC scheme is used for planning paths on-line.
|Item Type:||Book Section|
|Additional Information:||This work was partially supported by the European Space Agency through project “ROBMPC – Robust Model Predictive Control for Space Constrained Systems"|
|Uncontrolled Keywords:||autonomous aerial vehicles , collision avoidance , decentralised control, linear systems, motion control, predictive control, sampling methods, stability, time-varying systems|
|Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TL Motor vehicles. Aeronautics. Astronautics
|Research Area:||Computer Science and Applications|
|Depositing User:||Ms T. Iannizzi|
|Date Deposited:||04 Apr 2012 08:41|
|Last Modified:||04 Apr 2012 08:41|
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