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Feedback control systems

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Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
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Information-State-Based Reinforcement Learning for the Control of Partially Observed Nonlinear Systems.

Raman Goyal, Mohamed Naveed Gul Mohamed, Ran Wang

    IEEE Transactions on Neural Networks and Learning Systems
    |August 19, 2025
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    Summary
    This summary is machine-generated.

    This study introduces a novel reinforcement learning (RL) method for controlling complex nonlinear systems with incomplete information. The approach transforms partially observed problems into fully observed ones, enabling precise control even with model uncertainties.

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    Area of Science:

    • Control Theory
    • Machine Learning
    • Dynamical Systems

    Background:

    • Controlling nonlinear dynamical systems with partial observations presents significant challenges.
    • Existing methods often struggle with model uncertainty and high dimensionality.

    Purpose of the Study:

    • To develop a model-based reinforcement learning (RL) approach for closed-loop control of nonlinear dynamical systems with partial nonlinear observation models.
    • To transform partially observed problems into fully observed ones using an information-state approach.

    Main Methods:

    • An information-state approach transforms the partially observed problem into a fully observed one.
    • A data-based generalization of the iterative linear quadratic regulator (ILQR) is developed for RL.
    • Local linear time-varying models are approximated using autoregressive-moving-average (ARMA) models from input-output data.

    Main Results:

    • Equivalence between the transformed and initial partially observed optimal control problems is established.
    • Conditions for solving the deterministic optimal solution are provided.
    • A local perturbation feedback control law is designed for optimal solutions.

    Conclusions:

    • The developed RL method effectively controls complex, high-dimensional nonlinear systems.
    • The approach demonstrates efficacy even with model and sensing uncertainties.
    • This work offers a robust solution for partially observed optimal control problems.