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www.cranfield.ac.uk

Autonomy and AI for Advanced Air Mobility

November 10th, 2020

Gokhan Inalhan

BAE Systems Chair

Professor of Autonomous Systems and Artificial Intelligence

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Advanced Air Mobility Vision and Framework

Airspace, CNS and System Design & Implementation

Air Traffic Operations Vehicle Design and Operations & Fleet Management

Community Integration

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Key Technological Milestones for Advanced Air Mobility

Airspace, CNS and System Design & Implementation

Air Traffic Operations Vehicle Design and Operations & Fleet Management

Community Integration

• Fleet Management

• Safe Urban Flight

Management

• Scalable Vehicle

Operations

• Ground Operations and

Maintenance

• Autonomy at Fleet and

Vehicle Level

• Local Regulatory

Environment

• Certification and

Operational Approval

• Public Acceptance

• Supporting

Infrastructure

• Intermodal

transportation

Integration

• Airspace design, and

defining operational

rules, roles and

procedures

• CNS and Control

Service Infrastructure

• Urban Air/Cargo

Mobility Port Design

• Safe, efficient and

scalable ATM/UTM

Operation

• Urban Traffic and

Weather Prediction

• Urban Demand and

Capacity Balancing

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• Europe’s main AAM demonstration project with CORUS XUAM (2020-2022)

Air Mobility Urban - Large Experimental Demonstrations (AMU-LED)

demonstrate the safe integration of UAM as additional airspace user

Safe UAM flightSafe interaction

with other AS Users

Navigation Communications

Urban canyons

GNSS outages

BVLOS Lossof C2

Routes/Structure Separation

Flight Plan Deconfliction

Cameras/etc. 4G/5GU-spaceservices

DAASmart City

UAM

Flyability

UAM Platform

Architecture / Deployment

Objectives

Topics

Problems

Solutions

Architecture

Experiments

VLDsSource : E-HangSESAR AMU-LED

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Cranfield Global Research Airport

Cranfield Global Research Airport

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Airspace of the Future:1. Oxford-Cambridge arc

Unsegregated Airspace Populated Areas

NBEC : National BVLOS Experimentation Corridor

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Major Challenges in Advanced Air Mobility Concept and Our Autonomy Focus

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• Challenge #1 : Building explainable AI ?

• Challenge #2 : Building trustworthy AI ?

• Challenge #3 : Building computationally/physically tractable AI ?

Three major challenges for Learning Enabled Autonomous Systems

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• Background• Trip fuel is required to be calculated precisely. Fuel is expensive.

• Fuel flow is tail-specific and is a function of several parameters.

• Aircraft performance models calculate fuel flow.

• The most reliable are manufacturer models

• (e.g., Boeing Performance Software, Airbus’ Performance Engineer’s Program)

• The most available is BADA (under licensing terms), which is based on BPS and PEP.

• Problem• These models are generic to aircraft types and designed at ”zero” conditions.

• Aircraft performance change over time, due to maintenance, and operations at various conditions .

• How to monitor the performance and “update” the model?

• Solution• Monitoring: flight data (Quick Access Recorder, Flight Data Recorder)

• Update: Re-construct input-output relationship using flight data.

Digital Twin Aircraft Performance Model

Model discrepancies

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3k

$

30M$+

6.150

M$

60

0k

g

Saving per flight

Saving per year

(1 flight/day)

Saving per year

1 a/c type

Saving per year

All fleet

50 a/c

~300 a/c

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• Data (10M+ flight points) + statistics + function app. = ML

• The fundamental parameters affecting fuel flow are: altitude, temperature, mass, engine states, speed.

• A regression problem. And the relation is highly nonlinear.

• Deep artificial neural networks are the state-of-the-art solutions.

• What can be customized?

• Feature engineering.

• Architecture search.

• Hyper parameter optimization.

• Issue: Flight dynamics could be complex, but the model learns only what has been flown.

• Cruise at 10,000 ft is rarely seen compared to cruise at 30k ft above.

• Above app. 29,000 ft, the Mach speed is usually constant at every cruise level.

• Question: How can the model learn not only what is in the data but also the physical laws governing a flight?

• Answer: Embedding a physical intuition into the loss function.

Digital Twin Aircraft Performance Model

Architecture 1 Architecture 2

Regression is good Physical explainability isquestionable

M. Uzun, M. U. Demirezen, E. Koyuncu, and G. Inalhan, “Design of a hybrid digital-twin flight performance model through machine learning,” in 2019 IEEE Aerospace Conference. IEEE, 2019, pp. 1–14.

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• The labeled data do not cover the complete envelope.

• Include a physics based constraint to the optimizationproblem, so that the model also learns that physicalintuition. It needs to be implementable to the lossfunction [1].

• In our case, the physical guidance for cruise flight is the following equation:

𝐹 ∝𝑀

θ𝑎1𝑀

2 + 𝑎2𝑚2

𝑀2δ2

• Which stands for that fuel flow is proportional to thethrust required multiplied by the Mach number. Thrustrequired is approximated through this equation.

𝐽𝑝ℎ𝑦 =1

𝑁𝑅𝐸𝐿𝑈 𝑦𝑝𝑟𝑒𝑑 1:𝑁 − 1 − 𝑦𝑝𝑟𝑒𝑑 2:𝑁

• Any negative prediction of fuel flow is penalized.

𝐽𝑠𝑖𝑔𝑛 =1

𝑁𝑅𝐸𝐿𝑈 −𝑦𝑝𝑟𝑒𝑑

• Final loss function is:

𝐽 = 𝜆1𝑀𝑆𝐸 𝑦𝑎𝑐𝑡𝑢𝑎𝑙 , 𝑦𝑝𝑟𝑒𝑑 + 𝜆2𝐽𝑝ℎ𝑦 + 𝜆3𝐽𝑠𝑖𝑔𝑛

Physics-Informed Learning

[1] Abu-Mostafa, Y. S. (1990). Learning from hints in neural networks. Journal of complexity, 6(2), 192-198.

Accuracyis maintained

The model now captures the physics.

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Major Challenges in Advanced Air Mobility Concept and Our Autonomy Focus

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• Model Reference Adaptive Control (MRAC) (No observer gain)

• Convergent, yet oscillatory adaptationbehavior in the presence of modelingerrors.

• Closed-loop Reference Model AdaptiveSystems with Fixed Observer Gain

• Small observer gain => High frequency oscillation

• Large observer gain => Slow dynamics

• Why do not we use Variable Observer Gain?

• Large amplitude observer gain is used in the initial phase of the adaptation process => to improve the transient dynamics

• Small amplitude observer gain is used after the adaptation process is completed => to speed up the system response

• Can we learn the adaptation policy of the observer gain magnitude by using Reinforcement Learning?

• RL-CRM Adaptive Control Systems

Autonomy : Adaptive Flight Controls

Yuksek B, Inalhan G. Reinforcement Learning Based Closed-loop Reference Model Adaptive Flight Control System Design. International Journal of Adaptive Control and Signal Processing. 2020;1–21. https://doi.org/10.1002/acs. 3181

Ref. Model

Observer Gain

Adaptive Law

PlantControllerCommand

+

-

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Reinforcement Learning - CRM Adaptive Control System

Stabiliziedmodel is required if theopen-loopdynamics is unstable

Time-varying 𝑘 𝑡provides scalingpolicy of theobserver gainparameter 𝑣𝑜𝑝𝑡

Actor-CriticStructureTrained byutilizing DDPG Algorithm

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Reliable performance under large parametric uncertainities

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Major Challenges in Advanced Air Mobility Concept and Our Autonomy Focus

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Centre for Autonomous and Cyber-Physical Systems