1. 1 (Im)proving the Effectiveness of Simulator-Based Pilot Training Human Factors in Aviation Seminar Research eXchange, April 6, 2016 dr. ir. Daan M. Pool, TU Delft, Aerospace Engineering, Control & Simulation

2. 2 Full Flight Simulators for Pilot Training Antoinette trainer (1910)Today [1] www.cae.com [2] www.link.com [3] www.lmcft.com [1] [2] [3]

3. 3 Simulator-Based Training Effectiveness Change in pilots’ role in the cockpit (automation, unless…) Crucial role of flight simulators in flying skill acquisition and maintenance, especially for critical scenarios Manual flying skill erosion?

4. 4 Research Goal Issues in evaluating training effectiveness: Complex skills, with considerable allowed variability Inadequacy of output-based proficiency metrics Cost/effort required for formal evaluation Effects of specific simulator hardware/tuning? Our goal: “(Im)proving simulator-based pilot training effectiveness ”, through: 1.Quantitative model-based measurement of pilot control characteristics 2.Evaluating training effectiveness explicitly in experimental transfer studies ?

5. 5 Training at Different Levels [1] Anderson, J.R., The Architecture of Cognition, 1983 Rule-Based Behavior Procedural Knowledge Declarative Knowledge Knowledge-Based Behavior Skill-Based Behavior

6. 6 Transfer-of-Training Studies [1] Martin, E.A., “The Influence of Tactual Seat-Motion Cues on Training and performance in a Roll-Axis Compensatory Tracking Task Setting”, 1985 True transfer-of-training Quasi transfer-of-training “Training” “Test”

7. 7 Is Motion Needed for Simulator Training? NO EFFECT EFFECT NO EFFECT EFFECT NO EFFECT

8. 8 Variables that Affect Manual Control [1] McRuer and Jex, “A Review of Quasi-Linear Pilot Models”, 1967

9. 9 Approach: Skill-Based Tracking Tasks visual stimuli external disturbance control action motion stimuli target

10. 10 Approach: Multimodal Operator Models motion stimuli visual stimuli control action sensorsprocessingactuation

11. 11 Approach: Multimodal Operator Models motion stimuli visual stimuli sensorsprocessingactuation control action +

12. 12 Approach: Parameter Estimation Measured time tracesMultimodal operator model Parameter estimation algorithm Operator behavior quantitatively and objectively defined by:

13. 13 Example results: pilot control dynamics

14. 14 Experiment SIMONA Research Simulator at TU Delft Compensatory display shown on PFD Control with side stick (pitch axis only) Pitch motion feedback from 6-DOF hydraulic hexapod motion system (1-to-1) Other cueing systems (e.g., OTW view) switched off

15. 15 Experiment Naive subjects (selected for no prior experience) Two groups: NM and M 12 participants per group (total 24 participants) 7 sessions per participant (Training: 4, Test: 3) Sessions on consecutive (working) days 25 runs per session (90 second runs)

16. 16 Results: Task Proficiency 0255075100125150175 0.2 0.4 0.6 0.8 1 1.2 TrainingTest Trial Average Group NM Average Group M Curve fit Group NM Curve fit Group M

17. 17 Results: Lead Constant 0255075100125150175 0.2 0.3 0.4 0.5 0.6 0.7 TrainingTest Trial Average Group NM Average Group M Curve fit Group NM Curve fit Group M

18. 18 Results: Motion Gain 0255075100125150175 0 1 2 3 4 TrainingTest Trial Average Group NM Average Group M Curve fit Group NM Curve fit Group M

19. 19 Conclusions Model-based analysis of manual control skills enables explicit and quantitative insight into training effectiveness Initial acquisition of skill-based manual control skills is slow, both in moving-base and fixed- base simulators Our results “prove” that very little positive transfer results from initial fixed-base training of manual control skills ? [1] Pool, Harder and Van Paassen, Effects of Simulator Motion Feedback on Training of Skill-Based Control Behavior, 2016 [1]

20. 20 Challenges Model-based analysis of more operationally relevant control tasks (e.g., stall/upset recovery, manual landing flare & decrab): –Discrete maneuvering –Multi-axis control –Time-varying task variables Structural consideration of effects of simulator hardware and cueing: –Aircraft dynamics model quality (e.g., stall/upset recovery) –Visual cueing quality (e.g., delays, hardware characteristics) –Motion cueing quality (e.g., motion filters, delays) –Augmented cues (e.g., visual/haptic) to enhance learning? Practical feasibility of the transfer-of-training paradigm

21. 21 (Im)proving the Effectiveness of Simulator-Based Pilot Training Human Factors in Aviation Seminar Research eXchange, April 6, 2016 dr. ir. Daan M. Pool, TU Delft, Aerospace Engineering, Control & Simulation