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AI Pontryagin or how artificial neural networks learn to control dynamical systems

Author

Listed:
  • Lucas Böttcher

    (Frankfurt School of Finance and Management
    University of California, Los Angeles)

  • Nino Antulov-Fantulin

    (ETH Zurich)

  • Thomas Asikis

    (ETH Zurich)

Abstract

The efficient control of complex dynamical systems has many applications in the natural and applied sciences. In most real-world control problems, both control energy and cost constraints play a significant role. Although such optimal control problems can be formulated within the framework of variational calculus, their solution for complex systems is often analytically and computationally intractable. To overcome this outstanding challenge, we present AI Pontryagin, a versatile control framework based on neural ordinary differential equations that automatically learns control signals that steer high-dimensional dynamical systems towards a desired target state within a specified time interval. We demonstrate the ability of AI Pontryagin to learn control signals that closely resemble those found by corresponding optimal control frameworks in terms of control energy and deviation from the desired target state. Our results suggest that AI Pontryagin is capable of solving a wide range of control and optimization problems, including those that are analytically intractable.

Suggested Citation

  • Lucas Böttcher & Nino Antulov-Fantulin & Thomas Asikis, 2022. "AI Pontryagin or how artificial neural networks learn to control dynamical systems," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-021-27590-0
    DOI: 10.1038/s41467-021-27590-0
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    References listed on IDEAS

    as
    1. Benjamin Schäfer & Dirk Witthaut & Marc Timme & Vito Latora, 2018. "Dynamically induced cascading failures in power grids," Nature Communications, Nature, vol. 9(1), pages 1-13, December.
    2. Benjamin Schäfer & Dirk Witthaut & Marc Timme & Vito Latora, 2018. "Author Correction: Dynamically induced cascading failures in power grids," Nature Communications, Nature, vol. 9(1), pages 1-1, December.
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    Cited by:

    1. Lucas Böttcher & Thomas Asikis & Ioannis Fragkos, 2023. "Control of Dual-Sourcing Inventory Systems Using Recurrent Neural Networks," INFORMS Journal on Computing, INFORMS, vol. 35(6), pages 1308-1328, November.

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