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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