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Real-Time Safety-Assured Autonomous Aircraft

(AIAA-2016-4129) AIAA Aviation 2016, Washington D.C., 16 June 2016

Raghu Cowlagi Worcester Polytechnic Institute

Jeff Chambers and Nikola Baltadjiev Aurora Flight Sciences

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Self-Aware Aerospace Vehicle: A definition

A self-aware aerospace vehicle can dynamically adapt

the way it performs missions by gathering information

about itself and its surroundings and responding

intelligently.

Sense

Environment Vehicle Response Vehicle Health

Plan

Vehicle State Maneuver Capability Remaining Useful Life

Act

Beliefs Goals Plans

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Self-Awareness in other industries

“Robot passes self-awareness test for

first time” – indiatoday.in July 27, 2015

Self driving cars must be aware of

surrounding environment and react to a

set of rules to navigate the roads safely,

but “self-awareness” is not a priority

Aircraft are designed with minimum margins and operate in complex environments –

self-awareness increases safety

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RTS3A Objective

Real-time Monitoring: constantly monitor self-assessment data from subsystems on the

aircraft and integrate with broadcast information about surrounding environment. Current

effort focuses on airframe and propulsion subsystems.

State Prediction: using real-time sensor data and prediction algorithms developed by

Aurora and WPI, software predicts current state of two subsystems (airframe and propulsion)

and updates avionics with a new ‘vehicle level’ state.

Action Determination: real-time motion-planning and control system makes an informed

decision about updated operating envelope of vehicle, provides informative alerts indicating

the responsible sub-system(s), and takes autonomous actions to optimize mission

performance within the new operating envelope. Information updated to ground control to

allow for human-on-the-loop supervision and maintenance crew to make informed decisions.

Develop standalone RTS3A device that provides an air vehicle with condition aware capabilities and that can interface with standard flight

control systems

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Benefit of RTS3A:

Maximize Aircraft Lifetime and Utilization

• Tailor or restructure everyday flight to minimize

wear, fatigue, or environmental degradation

– Adds years to life

– Reduces maintenance required to maintain

airworthiness

• Aircraft fly to maximum capability, performing

missions beyond traditional design envelop

• Autonomously adapt vehicle maneuvers to rely

more heavily on healthy systems in order to

complete missions

– Combine in-situ sensors with on-board models to make informed decisions

– Reasoning agents to determine optimal actions to accomplish mission

– In-Situ flight maneuver adjustments

• Future Benefits

– Prioritized maintenance based on fleet capability/requirements

– Flight optimization based on structure and engine capability

– Utilize a vehicle with “just enough in the tank”

Residual

Strength

Aircraft Lifetime

Baseline

Design

(point)

Maximum

Benefit

(point)

Damage-Aware

Algorithm (line)

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RTS3A Data Management

• RTS3A handles various types of data flows: – Sensor signals

– Performance data

– Health state information

– RUL information

– Mission planer data

– FCS signals

• Requires efficient data management in real time through an open

standard such as DDS or ROS

• Open architecture allows the RTS3A system to be adapted to various

aircraft and missions

Modified from: Ferrell, Barry L. "Air vehicle prognostics

and health management.“ Aerospace Conference

Proceedings, 2000 IEEE. Vol. 6. IEEE, 2000.

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RTS3A Open Architecture

• Open architecture layer enables

efficient data exchange through

publish/subscribe mechanism

• Platform/Mission-specific modules are

easily replaced in such architectures

• Allows for different sensor suites and

PHM modules to be integrated into the

system

• Open architecture facilitates

component re-use, enables

‘plug-n-play’ services

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Benefits of Open Architecture

• Portability: RTS3A is designed as a retrofit system which increases the

level of autonomy in an existing and future UAS. Our OA approach is

necessary to enable this retrofit architecture.

• Decreased Development Times: Within the RTS3A software, our approach

decreases development time through enabling re-use of existing modules

and streamlining the development and integration of new modules.

• Lower Upgrade Cost: Over the longer term, our approach to lower the cost

and time needed of future upgrades through implementation of scalable,

extensible, and interoperable service oriented modules.

• System Security: OA offers solutions to meet current and future DoD

operational needs with faster fielding and lower ownership costs through

modular, scalable, portable, extensible, and interoperable system attributes

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RTS3A Subsystem Development

• Focus:

– Structural subsystem development

– Propulsion subsystem development

– Overall integration

• Motion planner development

discussed in Prof. Cowlagi’s

presentation

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MIT DDDAS

Structural Subsystem:

Safety-Assured Maneuvering Design

Size wing using traditional

analysis (FEA)

Damaged design

allowables

Aerodynamic stability Structural stability Structural strength

Baseline Design Point

Loads Allowables

Measured Damage Information

Environment Information (e.g., temp)

Material Damage Model

Airframe Capability

Model

Local Capability

Vehicle State

Model

Maneuver (from VMS)

Maximum Maneuver

Airframe Response

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(2000)

(1500)

(1000)

(500)

0

500

1000

1500

2000

2500

(100000) (50000) 0 50000 100000 150000 200000

To

rsio

n (M

s)

(lb

-ft)

Vertical Bending Moment (Mc) (lb-ft)

BL= 0, Torsion Moment Vs Vertical Bending Moment

MQ-X BL=0

Pull Up

Push Over

Aileron

Aileron + Sideslip

1G

4G

3G

2G0G

-0.5G

-1G

Structural Subsystem:

Vehicle State Model • Translate maneuvers into airframe structural responses

Stress Contour (4g Pull-Up)

Stress Contour (3g Pull-Up)

High Compressive Stress

Low Compressive Stress

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Structural Subsystem:

Material Damage Model

• Determine local capability based on state

• Open Hole Damage Model

– Whitney-Nuismer Average Stress Criterion

• Stress distribution around an open hole

• Notched strength ratio

• Design Allowables

– Baseline Design: Open Hole Compression

(OHC) Strength

• Typical design allowable

• 0.25” diameter hole

– Peak Condition: Unnotched Compression

Strength Source: Callus, P., The Effects of Hole-size and Environment on the Mechanical Behaviour of a Quasi-isotropic AS4/3501-6 Laminate in Tension, Compression and Bending DSTO-TR-2077, 2007

Notched

Tension

Unnotched

Tension

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RTS3A Subsystem Development

• Focus:

– Structural subsystem development

– Propulsion subsystem development

– Overall integration

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Flight

Conditions

Throttle

command Truth Model EKF Estimator Fuel Flow

Noise

Sensor

signals

Degradation

factors

Controller

Corrected

fan speed

+

State

Estimation

Propagation

Correction

Propulsion Subsystem:

Performance Self-Awareness • Low-fidelity physics-based model using thermodynamic cycle analysis

– capable of executing onboard and in real time

– models performance of various engine subsystems, allowing for degradation of

turbomachinery components and flow passages

• Health estimator based on Extended Kalman filter theory

– Estimates degradation factors by comparing sensor signals to model predictions

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• State estimation tracks overall engine performance parameters, as well as

individual subsystems

• Accuracy depends on model fidelity and available signals

• Data can be used to alter mission parameters and also predict remaining useful life

Propulsion Subsystem:

State Estimation

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• LEAP-Frog method uses linear regression to predict long-term degradation

• If error between linear regression and PHM degradation estimates larger than 3

standard deviations, a smaller subset of degradation data is used to redefine the

linear regression trend

• RUL data can be used by motion planner, as well as for maintenance and repair

scheduling

Propulsion Subsystem:

RUL Estimation

Adapted from: F. Greitzer and T. Ferryman,

"Predicting Remaining Life of Mechanical

Systems," in Intelligent Ship Symposium IV, 2001.

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RTS3A Subsystem Development

• Focus of this presentation:

– Structural subsystem development

– Propulsion subsystem development

– Overall integration

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RTS3A Framework Implementation

• Focus is on minimizing the impact of degradation

– How to utilize available data from multi-fidelity models to improve mission performance?

• Fast incremental replanning algorithm (Zhang & Cowlagi, 2015)

• Low-level control adjustments explored next

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Low –Level Control in Presence of Degradation

• Goal is to find small control adjustments (Δ𝑉, Δ𝜎, Δ𝛼) to minimize excess fuel

consumption

• Using linear approximations around the original trim conditions, the equations

of motion for the degraded system can be written as

Airspeed

Fuel flow rate

Angle of attack

Roll angle

(r denotes reference trim state, d denotes degraded state)

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Low –Level Control in Presence of Degradation

• Evaluating the partial derivatives requires knowledge of

– aerodynamic capability (vehicle state model)

– thrust as a function of fuel flow and air speed in both pristine (engine model) and

degraded state (health estimator), and partial derivatives at the reference trim state

• Solving the equations of motion for the degraded system also requires a cost

function of the form

– first-order necessary condition for optimality yields

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Case Study: Setup

• Simulated cruise segment

only for a conceptual

vehicle with induced

moderate fan degradation

• Compared total fuel

consumption for the given

range for two cases

– Maintaining same trim state

– Using mission planner to

update trim state

• PHM logic offline

– Assumed degradation is

detected

Mission Planner

Aircraft Model

Engine Model

Lift (L) Drag (D)

Thrust (Tp)

Sen

sor

sign

als

PHM Model

Propagation

Correction

Pitch (α), Flight speed (V)

Degraded thrust (Td)

Fuel flow (σ), Flight speed (V)

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Case Study: Results

• New trim state at slightly

lower speed

• RTS3A system recovers

~7.3% of the excess fuel

due to degradation

• New trim state yields lower

temperature, decreasing the

probability of engine failure

Warning limit

NTE limit

22 lb.

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Conclusions

• Self-awareness enables air vehicles to intelligently react to in-situ changes

to on-board subsystems and live changes to the surrounding environment

– Permits air vehicle to fly at current maximum capability – even if the capability is

a reduction of ‘normal’ operation

• RTS3A framework developed around open architecture to allow future

integration of third-party modules that may contain proprietary capabilities

• Development of two vehicle subsystem modules demonstrates integration

of vehicle data with motion planner:

– Structures Subsystem Module

– Propulsion Subsystem Module

– Motion Planner Module

• Motion planner allows for fast incremental re-plan or low-level control

adjustment to optimize mission performance

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Future Work

• Integrate RTS3A in Aurora’s 6DOF

simulation environment

• Explore various use cases

– Airframe degradation that limits

turn radius

– Moderate propulsion system

degradation that increases fuel

consumption and decreases range

– Severe propulsion system

degradation that limits thrust output

• Integrate awareness of the

surrounding environment

– Weather, terrain, keep-out airspace

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Questions/Discussion