Ph.D. Defense: Olatunde Sanni

Thu Dec 15 2022 03:00 PM
Montgomery Knight Building 317 or Virtual via Zoom


Olatunde Sanni

(Advisor: Prof. Eric Feron)


"Microscopic Analysis of many Optimizing Air Vehicles
Using High-Performance Computing"


Thursday, December 15

3:00 p.m. (EST)
Montgomery Knight Building 317


The operational success of an air traffic system (ATS) depends on air traffic policies. These policies balance the trade-off between safety and performance. Stringent policies stifle rewards, and lenient policies can lead to loss of life and property. Air traffic management (ATM) research explores this trade-off. Unsurprisingly, this research area has been limited by the human-in-the-loop because human pilots and air traffic controllers (ATCs) are difficult to predict and expensive to model. However, advancements in autonomy algorithms and computational systems are changing this landscape. This dissertation leverages these advancements to explore consequences of air traffic policies.

A novel approach for studying air traffic policies is presented. This novel approach is implemented in the developed Massive Air Traffic Simulator (MATS). This approach uses a high-performance computing (HPC) cluster to conduct real-time simulations of many autonomous and independent air vehicles that communicate with ATM services, such as a weather service, map service, and traffic control or deconfliction service. Its simulated air vehicles use trajectory optimization to account for air traffic policies.

The optimizing air vehicles in this dissertation use the developed Extensible Trajectory Optimization Library (ETOL), which transcribes an optimal control problem into a problem that path-planning and optimization software can solve. These vehicles asynchronously use ETOL as part of a model predictive control (MPC) strategy, and ETOL is configured to use a mixed-integer linear programming (MILP) solver. Although trajectory optimization is notorious for taking a significant amount of time, this dissertation demonstrates the developed approach’s real-time worthiness.

This dissertation provides three primary contributions. It presents a novel framework for assessing large-scale air traffic operations at real-time speed. It presents a MILP formulation that an air vehicle’s onboard computer can rapidly solve as part of its MPC strategy. It presents simulation results, lessons learned, and challenges for large-scale high-density air traffic.


  • Prof. Eric Feron – School of Computer Science (advisor)
  • Prof. Brian German – School of Aerospace Engineering (co-advisor)
  • Prof. Graeme Kennedy – School of Aerospace Engineering
  • Prof. Mark Costello – School of Aerospace Engineering
  • Prof. John-Paul Clarke – Ernest Cockrell Jr. Memorial Chair in Engineering, Department of Aerospace Engineering and Engineering Mechanics, The University of Texas at Austin



Montgomery Knight Building 317 or Virtual via Zoom