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Unmanned Aerial Vehicles Formation Using Learning Based Model Predictive Control
Author(s) -
Hafez Ahmed T.,
Givigi Sidney N.,
Yousefi Shahram
Publication year - 2018
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.1774
Subject(s) - flocking (texture) , model predictive control , computer science , control engineering , control (management) , aerodynamics , control theory (sociology) , artificial intelligence , engineering , aerospace engineering , materials science , composite material
This paper presents a solution for the formation flight problem for multiple unmanned aerial vehicles (UAVs) cooperating to execute a required mission. Learning Based Model Predictive Control (LBMPC) is implemented on the team of UAVs in order to accomplish the required formation. All flight simulations respect Reynold's rules of flocking to avoid UAV collisions with nearby flockmates, match the team members velocity and stay close to each other during the formation. The main contribution of this paper lies in the application of LBMPC to solve the problem of formation for an autonomous team of UAVs. The proposed solution is theoretically, by the application of analysis to the problem, demonstrated to be stable. Moreover, simulations support the findings of the paper. The main contributions of this paper are the proposed LBMPC formulation for formation of vehicles with uncertainty in their models, and the theoretical analysis of the solution.