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Model-free tracking control of complex dynamical trajectories with machine learning

Author

Listed:
  • Zheng-Meng Zhai

    (Arizona State University)

  • Mohammadamin Moradi

    (Arizona State University)

  • Ling-Wei Kong

    (Arizona State University)

  • Bryan Glaz

    (DEVCOM Army Research Laboratory)

  • Mulugeta Haile

    (DEVCOM Army Research Laboratory)

  • Ying-Cheng Lai

    (Arizona State University
    Arizona State University)

Abstract

Nonlinear tracking control enabling a dynamical system to track a desired trajectory is fundamental to robotics, serving a wide range of civil and defense applications. In control engineering, designing tracking control requires complete knowledge of the system model and equations. We develop a model-free, machine-learning framework to control a two-arm robotic manipulator using only partially observed states, where the controller is realized by reservoir computing. Stochastic input is exploited for training, which consists of the observed partial state vector as the first and its immediate future as the second component so that the neural machine regards the latter as the future state of the former. In the testing (deployment) phase, the immediate-future component is replaced by the desired observational vector from the reference trajectory. We demonstrate the effectiveness of the control framework using a variety of periodic and chaotic signals, and establish its robustness against measurement noise, disturbances, and uncertainties.

Suggested Citation

  • Zheng-Meng Zhai & Mohammadamin Moradi & Ling-Wei Kong & Bryan Glaz & Mulugeta Haile & Ying-Cheng Lai, 2023. "Model-free tracking control of complex dynamical trajectories with machine learning," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-41379-3
    DOI: 10.1038/s41467-023-41379-3
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    References listed on IDEAS

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    1. Daniel J. Gauthier & Erik Bollt & Aaron Griffith & Wendson A. S. Barbosa, 2021. "Next generation reservoir computing," Nature Communications, Nature, vol. 12(1), pages 1-8, December.
    2. Junjie Jiang & Ying-Cheng Lai, 2019. "Irrelevance of linear controllability to nonlinear dynamical networks," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    3. L. Appeltant & M.C. Soriano & G. Van der Sande & J. Danckaert & S. Massar & J. Dambre & B. Schrauwen & C.R. Mirasso & I. Fischer, 2011. "Information processing using a single dynamical node as complex system," Nature Communications, Nature, vol. 2(1), pages 1-6, September.
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    Cited by:

    1. Robert M. Kent & Wendson A. S. Barbosa & Daniel J. Gauthier, 2024. "Controlling chaos using edge computing hardware," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    2. Ling-Wei Kong & Gene A. Brewer & Ying-Cheng Lai, 2024. "Reservoir-computing based associative memory and itinerancy for complex dynamical attractors," Nature Communications, Nature, vol. 15(1), pages 1-14, December.

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