nasa aeronautics research institute real-time onboard … · · 2014-10-17real-time onboard...

NASA Aeronautics Research Institute

Real-Time Onboard Global Nonlinear Aerodynamic

Modeling from Flight Data

NASA Aeronautics Research Mission Directorate (ARMD) 2014 Seedling Technical Seminar

February 19–27, 2014

Jay Brandon Eugene Morelli


Outline


NASA Aeronautics Research Mission Directorate 2014 Seedling Technical Seminar 2

• Background • Test Aircraft and Equipment • Flight Test Maneuvers • Aerodynamic Modeling • Example Results - Demo • Dissemination • Next Steps • Closing Remarks


Research Goal



• Ultimate Goal: Self-Learning and Autonomously-Adapting Vehicles

• Potential for High Impact In Three Primary Areas: – Safety – Reliability – Design/Development Efficiency

Technology Enablers Required: Computer Processing Capabilities System Identification Advances

• Non-linear aerodynamic modeling • Real-time system identification

Flight Control Theory Advances • Adaptive flight control algorithms

Short-circuit the current process…

…with a new paradigm


Phase I Progress



• Nonlinear Aerodynamic System Identification – No model structure specification

required – Large flight envelope with single model – Post-flight analysis

• Flight Test Techniques for Rich Data Content – Multi-axes inputs over large range of

flight conditions – Piloted adaptation of similar orthogonal

input techniques • Blending of Data from Different Sources

– Ship research data acquisition system – iPad internal sensors for inertial data

and GPS

0 50 100 150-0.25

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

Cm

Time, sec

FlightModel, R2 = 0.9649

-10 0 10 20 30 40-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

CN

orm

al

α, deg

FlightModel, R2 = 0.9963

-5 0 5 10 15 20 25 30 35-0.04

-0.02

0

Cmα, p

er d

eg

DF9C4

-5 0 5 10 15 20 25 30 35-0.03

-0.02

-0.01

0

Cmδe

, per

deg

-5 0 5 10 15 20 25 30 350

2

4x 10-3

Cmα-

dot, p

er d

eg/s

ec

-5 0 5 10 15 20 25 30 35-20

-10

0

Cmqh

at

α, deg

FuzzyLESQ


Test Aircraft



• Large flight envelope, aerobatic capability • Well instrumented:

α /β on nose boom, dynamic pressure, engine rpm, fuel flow accelerations, angular rates, and controls real-time data in aft cockpit and telemetered to the ground

Aermacchi Impala MB-326M


Flight Test Setup



• Standard Front Cockpit • Onboard Instrumentation • Real-time Analysis Computer

mounted in Aft Cockpit


Flight Test Maneuvers




Video of Typical Maneuver




In-Flight Aerodynamic Modeling



FLIGHT TEST MANEUVER

DATA CONDITIONING

NONLINEAR AERODYNAMIC MODELING ALGORITHMS: FUZZY LOGIC

MULTIVARIATE ORTHOGONAL FUNCTIONS

MODEL VALIDATION DATA FROM OTHER MANEUVERS

MEASURED DATA

FINAL MODEL

Explanatory variables: Response variables: α, β, p, q, r, δe , δa , δr , M, RPM, … CX , CY , CZ , Cl , Cm , Cn


Maneuver Example



-6 -5 -4 -3 -2 -1 0 1 2 3 46

8

10

12

14

16

18

20

22

24

β (deg)

α (

deg)

-15 -10 -5 0 5 10 15 20-8

-6

-4

-2

0

2

4

6

δa (deg)

δ r (de

g)

-20 -15 -10 -5 0 5 10 15-4

-3

-2

-1

0

1

2

3

p (deg/s)

r (d

eg/s

)

-6 -5 -4 -3 -2 -1 0 1 2 3 4-8

-6

-4

-2

0

2

4

6

β (deg)

δ r (de

g)


Maneuver Guidance Display




Modeling Display




Real-Time Demo




Dissemination of Research

• Publications – NASA TM-2012-217778 – Phase I Results

• Lectures / Presentations – SFTE Meeting September 2013 – NASA Dryden Controls & Dynamics Presentation

September 2013 – ICAST Presentation February 2014

• Upcoming Events – Invited Lecture SETP Symposium March 2014 – Invited Lecture ACGCS Meeting March 2014 – AIAA Paper June 2014




Next Steps

• Improve Modeling & Prediction Capabilities – Thrust vs aero effects – Full fuzzy model generation inflight

• Automate Data Collection – Real-time monitoring of flight data and

autonomous selection of data suitable for modeling and prediction

• Flight Controls – Use updated models for updated control

algorithms – Real-time checking of model adequacy




Closing Remarks

• Near Real-time Aero Modeling – Self-learning and monitoring vehicles – Rapid development – Robust flight control

• Demonstrated Successful Modeling Inflight – All axes, large envelope, nonlinear – Complete aero model ready before landing – Two methods giving similar results

• Great Project Model – Small closely-working team – Minimal administrative burdens



nasa aeronautics research institute real-time onboard … · · 2014-10-17real-time onboard...

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