1. SAE/IEEE Aerospace Control and Guidance Systems Committee Meeting 102 Grand Island, New York Oct. 15 – 17, 2008 Ron Hess Dept. of Mechanical and Aeronautical Engineering University of California Davis, CA

2. Outline University of California Davis Aero Program Analytical Approach to Assessing Flight Simulator Fidelity Modeling Pilot Adaptation to Sudden Changes in Vehicle Dynamics Modeling Human Pilot Controlling Rotorcraft with Time-Varying Dynamics Sponsor: NASA Subsonic Rotary Wing Project; Technical Manager: Dr. Barbara Sweet

3. UCD Aero Program 25 Year Celebration UC Davis Aeronautical Science and Engineering Program Celebrating 25 years since initial accreditation by ABET First accredited Aeronautical/Aerospace Program in the Nine Campus UC System UC Davis Aero Faculty Jean Jacques Chattot (Dept. Chair) Valeria LaSaponara Roger Davis Nesrin Sarigul-Klijn Mohamed Hafez Bruce White (new Dean of Eng.) Ron Hess Case van Dam Sanjay Joshi

4. Robert Mondavi Food and Wine Institute University of California Davis

5. Robert Mondavi Center for Performing Arts University of California Davis

6. Analytical Assessment of Flight Simulator Fidelity Pilot Model Developed That Includes –Visual feedback with degraded cues - Proprioceptive feedback –Vestibular feedback -Task interference –Variable skill levels Aimed Toward Assessing Training Simulator Fidelity “We suggest, then, that fidelity is the specific quality of a simulator that permits the skilled pilot to perform a given task in the same way that it is performed in the actual aircraft. Execution …is simply the closure of all loops made necessary by both the task requirements and the dynamics of the vehicle and subject to the information available.” - Heffley, R. K., et al, “Determination of Motion and Visual System Requirements for Flight Training Simulators,” U.S. Army Research for the Behavioral and Social Sciences, TR 546, Aug. 1981.

7. Fidelity Example: Small Rotorcraft – BO-105 Task: Reposition task (4 control axes) with atmospheric turbulence Flight Condition: near hover Simulator “limitations” – 4 scenarios - no motion - limited motion - limited motion + reduced visual cue quality - limited motion + reduced visual cue quality + time delay in sim

8. Fidelity Example: Small Rotorcraft – BO-105 pilot/vehicle computer simulation model pilot model for longitudinal control loops power in proprioceptive feedback signal

9. no-motion FM = pitch + roll + vertical position + heading = 1.36 + 2.39 + 0.36 + 0.837 = 4.95 limited-motion FM = pitch + roll + vertical position + heading = 0.4 + 0.7 +.05 + 0.15 =1.3 limited-motion + reduced visual quality FM = pitch + roll + vertical position + heading = 0.89 + 1.28 + 0.22 + 0.62 = 3.01 limited-motion + reduced visual quality + time delay FM = pitch + roll + vertical position + heading = 0.98 + 2.04 + 0.208 + 0.07 = 3.3 Fidelity Example: Small Rotorcraft – BO-105 Fidelity Metrics (larger values imply poorer fidelity)

10. Fidelity Example: Large Rotorcraft – CH-53D accel/decel task – time varying pilot model hover - 110 kts - hover FM = pitch-loop contribution + roll-loop contribution + vertical velocity- loop contribution + heading-rate loop contribution = 0.0148 + 0.02 + 0.107 + 0.0218 = 0.164 Fidelity metric calculation is independent of time-variant task demands power in proprioceptive feedback signal

11. Modeling Pilot Adaptation to Sudden Changes in Vehicle Dynamics Adaptive Pilot Model – Single Axis Tasks Four criteria for model adaptation signals must be easily sensed by pilot adaptation completed in 5 sec or less logic in adaptation must be predicated upon information available to pilot post-adapted pilot models must follow dictates of crossover model of human

12. Modeling Pilot Adaptation to Sudden Changes in Vehicle Dynamics (single-axis task) Pilot model adapting to suddenly changing vehicle dynamics with pulsive commands C(t)

13. Modeling Pilot Adaptation to Sudden Changes in Vehicle Dynamics (multi-axis task with control cross-coupling)

14. Pilot model adapting to suddenly changing vehicle dynamics with random-appearing commands C(t)

15. Modeling Human Pilot Controlling Rotorcraft with Time-Varying Dynamics Pilot Model with Y pf configured properly

16. Modeling Human Pilot Controlling Rotorcraft with Time-Varying Dynamics From Yc = 1/s to Yc = 25/(s 2 +6s +25) cue to pilot that dynamics have changed

17. Modeling Human Pilot Controlling Rotorcraft with Time-Varying Dynamics High –fidelity model of Army RASCAL Pilot model/vehicle open-loop transfer function Pilot/vehicle open-loop transfer function from laboratory tracking task

18. Modeling Human Pilot Controlling Rotorcraft with Time-Varying Dynamics pitch and roll SCASs changing from RC/ATTH to ATTC/ATTH over 10 sec with time-varying pilot model cue to pilot that SCAS is changingpilot/vehicle tracking performance with time-varying pilot model

19. Modeling Human Pilot Controlling Rotorcraft with Time-Varying Dynamics Predicting Handling Qualities Levels Laboratory tracking tasks UH-60 hover task – ATTC/ATTH SCAS

20. California Innovation Center The California Innovation Center provides a mechanism where industry and universities (UCD & CSU Sacramento) will come together to support the existing technology-focused missions at Beale Air Force Base. These collaborative efforts will support additional emerging technologies that will influence and embrace the future growth of autonomous and cyber systems.

21. Collision Avoidance with cooperative & non-cooperative aircraft Interoperability with manned / unmanned aircraft ATC Communications Compliance with 14 CFR 91.113 Take-off & Landing FAA Airspace Classification s Weather Avoidance? Safety & Reliability Issues Navigatio n Command & Control Link Operator Qualifications Aircraft Airworthiness FAR 91.113b When weather conditions permit, regardless of whether an operation is conducted under instrument flight rules or visual flight rules, vigilance shall be maintained by each person operating an aircraft so as to see and avoid other aircraft. California Innovation Center