Smart Autoflight Control System


Current research envisions shifting the role of flight crews towards mission supervisors who make decisions at a very high level
of abstraction – decisions that guide complex systems automatically towards a defined goal. The applicable aircraft condition and contextual information towards the development of smarter Automatic Flight Control Systems (AFCSs) supporting this vision are being studied. These include the aircraft’s systems and capabilities state, the airspace structure, weather and traffic situation, the surrounding terrain and its population density, facilities, as well as human factors and operational aspects.

The visioned concept particularly aims at integrating Air Traffic Control (ATC) and the operational environment into the automatic decision making process. Suitable Artificial Intelligence (AI) methods and algorithms shall be studied and evaluated on a small commercially available Unmanned Air Vehicle (UAV). The Unmanned Aircraft System (UAS) will be extended to support simulated interactions with
ATC and mission control. The resulting system shall be able to perform missions on the basis of abstract goal descriptions that may change during the flight and require revised online flight planning and an adapted aircraft systems configuration in a hard real-time environment constrained by bounded rationality and bounded reactivity.

Such an UAS will enable higher-level command and control as well as increasingly flexible airborne missions.
Research Plan
This project comprises the following milestones:

  • Study of applicable models and algorithms
  • Implementation of prototypes and flight tests
  • Evaluation and comparison of different models and algorithms
  • Problem definition and formulation of research questions
  • Review of relevant literature and existing work
  • Application for Transport Canada Special Flight Operating Certificate and organization of flight test facilities
  • Review of current autopilot hardware and software stacks
  • Definition of relevant context to establish appropriate situational awareness
  • Study of applicable models and algorithms


  • S. Heinemann, H.A. Müller, A. Suleman: Smart Autoflight Control Systems. Proceedings Conference of the Centre for Advanced Studies on Collaborative Research (CASCON 2014), ACM, pp. 343-346. In press.