SAE/IEEE Aerospace Control and Guidance Systems Committee
Meeting 102Grand Island, New York
Oct. 15 – 17, 2008
Ron HessDept. of Mechanical and Aeronautical Engineering
University of CaliforniaDavis, CA
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
UCD Aero Program25 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 LaSaponaraRoger Davis Nesrin Sarigul-KlijnMohamed Hafez Bruce White (new Dean of Eng.)
Ron Hess Case van DamSanjay Joshi
Robert Mondavi Food and Wine InstituteUniversity of California
Davis
Robert Mondavi Center for Performing ArtsUniversity of California
Davis
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.
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
Fidelity Example: Small Rotorcraft – BO-105
pilot/vehicle computer simulation model
pilot model for longitudinal control loops
power in proprioceptive feedback signal
• 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-105Fidelity Metrics
(larger values imply poorer fidelity)
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
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frequency rad/sec
P(
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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
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)
Modeling Pilot Adaptation to Sudden Changes in Vehicle Dynamics(multi-axis task with control cross-coupling)
4)(ss
e4)s(s
1(s)Y2
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Modeling Pilot Adaptation to Sudden Changes in Vehicle Dynamics
(multi-axis task with control cross-coupling)
4)5)(s.0s(se
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Pilot model adapting to suddenly changing vehicle dynamics with random-appearing commands C(t)
Modeling Human Pilot Controlling Rotorcraft with Time-Varying Dynamics
Pilot Model
MUM
with Ypf
configured properly
Modeling Human Pilot Controlling Rotorcraft with Time-Varying Dynamics
From Yc = 1/s to Yc = 25/(s2+6s +25)
cue to pilot that dynamics have changed
Modeling Human Pilot Controlling Rotorcraft with Time-Varying Dynamics
High –fidelity model of Army RASCAL
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Pilot model/vehicle open-loop transfer function
Pilot/vehicle open-loop transfer function from laboratory tracking task
Modeling Human Pilot Controlling Rotorcraft with Time-Varying Dynamics
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violation of normaloperating area
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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 changing pilot/vehicle tracking performance with time-varying pilot model
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Level 1
Modeling Human Pilot Controlling Rotorcraft with Time-Varying Dynamics
Predicting Handling Qualities Levels
Laboratory tracking tasks UH-60 hover task – ATTC/ATTHSCAS
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.
Collision Avoidance with cooperative & non-cooperative aircraft
Interoperability with manned / unmanned
aircraftATC
Communications
Compliance with 14 CFR 91.113
Take-off & Landing
FAA Airspace
Classifications
Weather Avoidance?
Safety & Reliability Issues Navigation
Command & Control Link
Operator Qualifications
Aircraft Airworthiness
FAR 91.113bWhen 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
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