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Hierarchical Cognitive Control for Unknown Dynamic Systems Tracking

Author

Listed:
  • Mircea-Bogdan Radac

    (Department of Automation and Applied Informatics, Politehnica University of Timisoara, 300223 Timisoara, Romania)

  • Timotei Lala

    (Department of Automation and Applied Informatics, Politehnica University of Timisoara, 300223 Timisoara, Romania)

Abstract

A general control system tracking learning framework is proposed, by which an optimal learned tracking behavior called ‘primitive’ is extrapolated to new unseen trajectories without requiring relearning. This is considered intelligent behavior and strongly related to the neuro-motor cognitive control of biological (human-like) systems that deliver suboptimal executions for tasks outside of their current knowledge base, by using previously memorized experience. However, biological systems do not solve explicit mathematical equations for solving learning and prediction tasks. This stimulates the proposed hierarchical cognitive-like learning framework, based on state-of-the-art model-free control: (1) at the low-level L1, an approximated iterative Value Iteration for linearizing the closed-loop system (CLS) behavior by a linear reference model output tracking is first employed; (2) an experiment-driven Iterative Learning Control (EDILC) applied to the CLS from the reference input to the controlled output learns simple tracking tasks called ‘primitives’ in the secondary L2 level, and (3) the tertiary level L3 extrapolates the primitives’ optimal tracking behavior to new tracking tasks without trial-based relearning. The learning framework relies only on input-output system data to build a virtual state space representation of the underlying controlled system that is assumed to be observable. It has been shown to be effective by experimental validation on a representative, coupled, nonlinear, multivariable real-world system. Able to cope with new unseen scenarios in an optimal fashion, the hierarchical learning framework is an advance toward cognitive control systems.

Suggested Citation

  • Mircea-Bogdan Radac & Timotei Lala, 2021. "Hierarchical Cognitive Control for Unknown Dynamic Systems Tracking," Mathematics, MDPI, vol. 9(21), pages 1-23, October.
  • Handle: RePEc:gam:jmathe:v:9:y:2021:i:21:p:2752-:d:668051
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    References listed on IDEAS

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    1. Mircea-Bogdan Radac & Anamaria-Ioana Borlea, 2021. "Virtual State Feedback Reference Tuning and Value Iteration Reinforcement Learning for Unknown Observable Systems Control," Energies, MDPI, vol. 14(4), pages 1-26, February.
    2. Josh Merel & Matthew Botvinick & Greg Wayne, 2019. "Hierarchical motor control in mammals and machines," Nature Communications, Nature, vol. 10(1), pages 1-12, December.
    3. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    4. Wei Wang & Xin Chen & Hao Fu & Min Wu, 2019. "Data-driven adaptive dynamic programming for partially observable nonzero-sum games via Q-learning method," International Journal of Systems Science, Taylor & Francis Journals, vol. 50(7), pages 1338-1352, May.
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