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Optimal Output Regulation for General Linear Systems via Adaptive Dynamic Programming.

Yanzhi Wu, Qingpeng Liang, Jiangping Hu

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    Summary
    This summary is machine-generated.

    This study introduces an adaptive dynamic programming approach for optimal output regulation in linear systems. It achieves closed-loop stability and disturbance rejection by optimizing control gains.

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    Area of Science:

    • Control Systems Engineering
    • Adaptive Control Theory
    • Linear System Analysis

    Background:

    • Optimal output regulation aims to ensure closed-loop stability and disturbance rejection.
    • Minimizing performance indices is crucial for effective control system design.
    • Traditional methods often require complete knowledge of system parameters.

    Purpose of the Study:

    • To develop an adaptive optimal output regulation strategy for general linear systems.
    • To guarantee system stability and achieve effective disturbance rejection.
    • To minimize predefined performance indices using optimal control gains.

    Main Methods:

    • Adaptive dynamic programming (ADP) is employed to determine the optimal feedback control gain.
    • Unknown system matrices are explicitly computed for controller design.
    • Linear vector space optimization methods are utilized to find the optimal feedforward control gain.

    Main Results:

    • The study successfully computes unknown system matrices and disturbance coefficients.
    • An optimal controller, including feedback and feedforward gains, is derived.
    • The proposed method provides approximately optimal solutions for the linear optimal output regulation problem.

    Conclusions:

    • The presented adaptive dynamic programming approach effectively solves the linear optimal output regulation problem.
    • The method ensures closed-loop stability and disturbance rejection while optimizing performance.
    • This work offers a robust framework for adaptive control of linear systems with unknown parameters.