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Output Feedback Q-Learning for Linear-Quadratic Discrete-Time Finite-Horizon Control Problems.

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    Summary

    This study introduces an algorithm for output feedback control in finite-horizon linear-quadratic (LQ) problems. It enables optimal control without needing system matrices, using only input-output data for estimation.

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    Area of Science:

    • Control Theory
    • Systems Engineering
    • Optimization

    Background:

    • Finite-horizon linear-quadratic (LQ) optimal control problems typically require full knowledge of system dynamics.
    • Output feedback control is desirable for practical applications where full state information is unavailable.
    • Existing methods often necessitate explicit system matrices for controller design.

    Purpose of the Study:

    • To develop an algorithm for determining output feedback policies for finite-horizon LQ optimal control.
    • To enable optimal control without prior knowledge of the system's dynamical matrices.
    • To utilize input-output data for controller synthesis.

    Main Methods:

    • Characterizing Q-factors in the state feedback case for finite-horizon LQ problems.
    • Parameterizing Q-factors as functions of input-output vectors.
    • Developing a procedure for estimating these Q-factor functions from measured input-output data.

    Main Results:

    • The proposed algorithm successfully determines output feedback policies for finite-horizon LQ problems.
    • The method effectively bypasses the need for explicit system dynamical matrices.
    • Optimal control can be computed using estimated Q-factor functions derived from input-output data.

    Conclusions:

    • The developed algorithm provides a data-driven approach to output feedback control for finite-horizon LQ problems.
    • This method enhances the practicality of optimal control in scenarios with limited system knowledge.
    • The findings contribute to advancing robust and adaptive control strategies.