trajectory optimization, high altitude long endurance (HALE), aircraft, solar powered aircraft, station keeping, nonlinear model predictive control, UAV, drone


This paper demonstrates the use of nonlinear dynamic optimization to calculate energy- optimal trajectories for a high-altitude, solar-powered Unmanned Aerial Vehicle (UAV). The objective is to maximize the total energy in the system while staying within a 3 km mission radius and meeting other system constraints. Solar energy capture is modeled using the vehicle orientation and solar position, and energy is stored both in batteries and in potential energy through elevation gain. Energy capture is maximized by optimally adjusting the angle of the aircraft surface relative to the sun. The UAV flight and energy system dynamics are optimized over a 24-hour period at an eight-second time resolution using Nonlinear Model Predictive Control (NMPC). Results of the simulated flights are presented for all four seasons, showing 8.2% increase in end-of-day battery energy for the most limiting flight condition of the winter solstice.

Original Publication Citation

Martin, R. A., Gates, N. S., Ning, A., and Hedengren, J. D., “Dynamic Optimization of High-Altitude Solar Aircraft Trajectories Under Station-Keeping Constraints,” Journal of Guidance, Control, and Dynamics, Nov. 2018. doi:10.2514/1.G003737

Document Type

Peer-Reviewed Article

Publication Date


Permanent URL






Ira A. Fulton College of Engineering and Technology


Mechanical Engineering

University Standing at Time of Publication

Assistant Professor