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An Optimal Control Approach to Flight Management Systems for Unmanned Aerial Vehicles


An Optimal Control Approach to Flight Management Systems for Unmanned Aerial Vehicles

Di Perna, Michael (2017) An Optimal Control Approach to Flight Management Systems for Unmanned Aerial Vehicles. Masters thesis, Concordia University.

Text (application/pdf)
Diperna_MASc_F2017.pdf - Accepted Version


With unmanned aerial vehicles (UAVs) becoming increasingly present in military and commercial applications, the flight path efficiency and integration with current manned aircraft become important research topics to address in the coming years. This thesis considers three problems relating to UAVs: the optimal control of a single quadrotor UAV, a multi-agent coverage problem, and a software flight management system which can be used on UAVs.
The optimal control problem for a quadrotor UAV is considered with a tuning parameter, the cost index, used in flight management systems to trade-off between time and energy costs. A state-feedback control law is developed and simulation results are presented. A software flight management system (SFMS) using aerospace standard communication protocols is developed and validated with an industry flight simulator. The SFMS allows for the testing of algorithms which can be used on real aircraft (manned or unmanned) without requiring access to a costly commercial flight management system. An energy efficient coverage problem from previous work is considered and extended to include agents with second order dynamics using the backstepping technique. The extension to second order dynamics requires the analysis of the dynamics of Voronoi cells. A geometric interpretation is presented for the change in area and change in position of the center of mass for Voronoi cells. Simulation results are presented comparing the first order and second order agents.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering
Item Type:Thesis (Masters)
Authors:Di Perna, Michael
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Electrical and Computer Engineering
Date:27 April 2017
Thesis Supervisor(s):Rodrigues, Luis
ID Code:982497
Deposited On:09 Jun 2017 14:07
Last Modified:18 Jan 2018 17:55
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