Learning How to Control
Dept. of Electrical Engineering
University of Notre Dame
National Science Foundation
June 15, 2023- June 14, 2026
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Description:
Internet-of-Things (IoT) enabled manufacturing systems
(a.k.a. Manufacturing 4.0) form a particularly important class of cyberphysical system
(CPS). IoT-enabled manufacturing systems have a physical fabric woven
from a heterogeneous mix of machines carrying and processing materials
across the factory floor. The cyber fabric for these systems is a heterogeneous
mix of wired and wireless digital communication networks enabling the global visibility of
numerous data streams used by cloud-based planners and edge devices to manage the physical
fabric's workflows. These IoT-enabled systems are complex CPS with a great deal of modeling
uncertainty. The physical and cyber fabrics are open to an external environment that can
shift in an abrupt and unpredictable manner. Such shifts may be due to changes in customer
work orders or due to environmental changes that increase interference in the cyber fabric's
wireless (RF) networks. The dynamics of both fabrics are coupled since congestion in th physical fabric may create
congestion in the cyber fabric and vice versa. This complexity and uncertainty stand as
major obstacles to the broader acceptance of IoT technologies by U.S. manufacturers.
To lower the risk in adopting IoT technolo- gies, this project proposes developing methods
that learn how to control complex CPS in Manufacturing 4.0 applications.
This project addresses that need by developing methods that learn how to control
complex CPS found in IoT-enabled manufacturing. This project proposes doing that by
developing a meta-learning framework for control based on a novel machine learning
model that appears to address robustness issues found in competing deep reinforcement
learning for control. This project will develop algorithms and software implementations
of this novel approach to meta-learning. The project will benchmark the method's
performance on a testbed capturing the complex interaction's between an IoT-manufacturing system’' physical and cyber fabrics.
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Products:
- X. Guo, S. Han, X.S. Hu, X. Jiao, Y. Jin, F. Kong, and M.D. Lemmon,
Towards Scalable, Secure, and Smart Mission-Critical IoT Systems: Review and Vision, Proceedings of the 2021 Interntational Conference on Embedded Software, September 2021.
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M.D. Lemmon, P.M. Wensing, V. Kurtz, and H. Lin,
Learning to Control Robot Hopping over Uneven Terrain, American Control Conference, Atlanta GA, June 2022
- M.D. Lemmon,
Learning How to Control: A Meta-Learning Approach for the Adaptive Control of Cyber-Physical Systems, NSF-CPS-551, June 2022
- Nolan Fey,
Adapting the planning and control of legged robots to extreme environments
,B.S. Thesis, Dept. of Electrical Engineering, May 2023
- Y. Duan and M.D. Lemmon,
Post-Fair Federated Learning: Achieving Group and Community Fairness in Federated Learning via Post-processing
, submitted to ICML (international conference on machine learning) 2024, July 21-27 2024, Vienna Austria.
- N.Fey, H. Li, N. Adrian, P. Wensing, and M.D. Lemmon,
A learning-based framework to adapt legged robots on-the-fly to unexpected disturbances,
to appear in 6th Annual Learning for Dynamics & Control Conference (L4DC), 15-17 July, 2024, University of Oxford.
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Intellectual Merit:
The intellectual merit of this proposal centers on meta-learning
algorithms for the control of complex and uncertain CPS performing a range of tasks.
The first novel aspect of this work is its adoption of a new type of machine learning
model called a behaviorally ordered abstraction (BOA). This model has greater cross-task
generalization capacity, better sample efficiency, and greater interpretability
than other deep learning methods. The project's second novel aspect is its potential
to address issues regarding the robust stability of deep reinforcement learning.
The third novel aspect of this work is its embedding of meta-learning in a
generalized regulator in which the framework learns “how” to configure controller
synthesis across all tasks. The project's fourth novel aspect of is its
benchmarking of meta-learning methods in IoT-enabled manufacturing.
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Broader Impacts:
The proposed research, if successful, would address critiques regarding the robustness of
deep reinforcement learning. The proposed research, if successful, would transform the use
of IoT technologies in complex CPS. The proposed research would also
lower the barriers that US industries face in adopting IoT technologies,
thereby improving U.S.
industrial global competitiveness. Regarding curriculum development,
this project will be integrated into a professional MS program with a concentration in
CPS and IoT systems engineering. Educational and training opportunities will be provided
for women through a joint program between Notre Dame and Saint Mary's College.
The project will leverage existing programs such as EngRET@ND for K-12 outreach.
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