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An Information-Based Learning Approach to Dual Control.

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    Summary
    This summary is machine-generated.

    This study introduces a receding horizon dual control method to balance learning and controlling unknown systems. It quantifies knowledge gain using information theory, demonstrating broad applicability in diverse settings.

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    Area of Science:

    • Control Theory
    • Information Theory
    • Machine Learning

    Background:

    • Dual control concurrently learns and controls unknown systems.
    • Active system learning conflicts with control objectives due to exploration disturbances.

    Purpose of the Study:

    • To develop a receding horizon approach for dual control.
    • To explicitly quantify knowledge gain during system learning.
    • To balance control objectives with information acquisition.

    Main Methods:

    • A multiobjective optimization problem solved iteratively within a receding horizon framework.
    • Defining a knowledge gain objective using information-theoretic measures (entropy, Fisher information, relative entropy).
    • Applying the framework to Markov decision processes and discrete-time nonlinear systems.

    Main Results:

    • Demonstrated a method to balance control performance with system identification.
    • Quantified knowledge gain from control actions using information theory.
    • Validated the approach across diverse problem settings with numerical examples.

    Conclusions:

    • The proposed receding horizon dual control framework effectively integrates learning and control.
    • Information-theoretic measures provide a robust way to quantify knowledge gain.
    • The approach shows broad applicability and usefulness in complex systems.