Contact

Davide Fabbroni

Address: Spemannstr. 38
72076 Tübingen
Room number: 111
Phone: +49 7071 601 609
Fax: +49 7071 601 616
E-Mail: davide.fabbroni

 

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Davide Fabbroni

Position: PhD Student  Unit: Bülthoff

I am a research scientist working with the Cybernetics Approach to Perception and Action research group.


I obtained my Master's Degree in Robotics and Automation Engineering from University of Pisa (Italy) in May 2017 and my Bachelor's Degree in Automation Engineering from University of Siena (Italy) in October 2013.

 

My research focuses on Stability Augmentation Systems for helicopters, helicopter simulators, pilot modeling and training of inexperienced pilots with simulators.

"Design and Evaluation of the MPI Helicopter Trainer for Flight Simulators"


Introduction

Nowadays, helicopters are widely used in many different sectors, for a wide variety of purposes. Their unique motion capabilities make them one of the most versatile aerial transportation vehicles. However, those very unique characteristics make them also intrinsically unstable and difficult to control. For these reasons, student pilots receive extensive and expensive flight training. Since the first studies on helicopter Transfer-of-Training (ToT) were carried out [1, 2], flight simulators were considered effective in reducing pilots’ training cost and time. Nevertheless, the extensive use of effective helicopter simulators is precluded to most of the civilian flight schools, due to their high cost. Moreover, there are still many open questions regarding the factors influencing the ToT from the simulator to the actual aircraft.

 

Goals

The final goal of this project is to make learning how to pilot a helicopter safer, quicker and less expensive. This can be achieved by exploiting the use of simulators combined with specifically-designed Synthetic Flight Training Systems (SFTS), such as the MPI Helicopter Trainer.

 

Methods

The MPI Helicopter Trainer (MPI HT) is a software trainer for flight simulators that shares the control of the helicopter with the student pilots. It is based on the Optimal Control pilot Model (OCM) [3, 4]. The OCM is a linear quadratic regulator that can be designed to control only specific helicopter control inputs, letting the student pilots focus on the others. Furthermore, its control action can be provided on the helicopter controls as haptic force-feedback and adapted to the student pilots’ performance.

The MPI HT guides the student pilots through the learning process by dividing the training lesson in phases. Each phase focuses on a specific maneuver, decided with the help of expert instructor pilots.

 

Initial results

In [5] the MPI HT was tested in a human-in-the-loop experiment in the fixed-based MPI PanoLab Simulator (PLS). The results showed that the training was effective in teaching the hover maneuver. A follow-up experiment demonstrated that student pilots trained in the simple, fixed-based PLS could also stabilize the highly-realistic MPI CyberMotion Simulator: the Transfer-of-Training (ToT) happened [6]. To further test the possibility of achieving ToT to the aircraft, a preliminary experiment was performed with a limited number of participants with the actual helicopter. The instructor’s evaluation of the student pilots trained with the simulator was very positive.

 

Initial conclusion

The results of the experiments and tests carried out up to date are very promising. The use of the MPI Helicopter Trainer can lead to major savings in terms of time needed to learn how to fly a helicopter, even in simple, fixed-based flight simulators. However, there is still work to do. The simulated environment in which the student pilots are trained should be simple, yet realistic. In this way, the Transfer-of-Training can be maximized.

 

References

  1. Caro PW, Isley RN (1966), Helicopter Trainee Performance Following Sinthetic Flight Training, Journal of American Helicopter Society, 11(3), 38-44.
  2. Valverde HH (1973), A Review of Flight Simulator Transfer of Training Studies, Human Factors, 15(6), 510-523.
  3. Kleinman DL, Baron S, Levison WH (May-1970), An Optimal Control Model of Human Response Part I: Theory and Validation, Automatica, 6(3), 357-369.
  4. Davidson JB, Schmidt DK (Dec-1992), Modified Optimal Control Pilot Model for Computer-Aided Design and Analysis, Technical Report, NASA Langley Research Center, Hampton (VA), USA.
  5. Fabbroni D, Geluardi S, Gerboni CA, Olivari M, D'Intino G, Pollini L and Bülthoff HH (May-2017), Design of a Haptic Helicopter Trainer for Inexperienced Pilots, 73nd American Helicopter Society International Annual Forum (AHS 2017), 1-12.
  6. Fabbroni D (May-2017), Design and Evaluation of an Adaptive Helicopter Trainer with Haptic Force-Feedback for Inexperienced Pilots, Master Thesis, University of Pisa.

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Conference papers (2):

Fabbroni D, Geluardi S, Gerboni CA, Olivari M, Pollini L and Bülthoff HH (September-2017) Quasi-Transfer-of-Training of Helicopter Trainer from Fixed-Base to Motion-Base Simulator, 43rd European Rotorcraft Forum (ERF 2017), -. accepted
Fabbroni D, Geluardi S, Gerboni CA, Olivari M, D'Intino G, Pollini L and Bülthoff HH (May-2017) Design of a Haptic Helicopter Trainer for Inexperienced Pilots In: The Future of Vertical Flight, , 73rd American Helicopter Society International Annual Forum (AHS 2017), Curran, Red Hook, NY, USA, 2097-2108.

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Last updated: Monday, 22.05.2017