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  3. Data-driven Optimized Output Regulation For Markov Jump Linear Systems And Its Application.
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  3. Data-driven Optimized Output Regulation For Markov Jump Linear Systems And Its Application.

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Data-Driven Optimized Output Regulation for Markov Jump Linear Systems and Its Application.

Hao Shen, Zheng Huang, Jiacheng Wu

    IEEE Transactions on Cybernetics
    |June 10, 2026

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    This study addresses the linear optimized output regulation problem (LOORP) for Markov jump linear systems (MJLSs). A novel reinforcement learning (RL) approach solves LOORP even with unknown dynamics and unstable gains.

    Related Experiment Videos

    Area of Science:

    • Control Systems Engineering
    • Applied Mathematics
    • Machine Learning

    Background:

    • Markov jump linear systems (MJLSs) present complex dynamics requiring robust control strategies.
    • The linear optimized output regulation problem (LOORP) is crucial for achieving desired system outputs under varying conditions.
    • Existing methods for solving regulator equations have limitations in handling unknown system dynamics.

    Purpose of the Study:

    • To develop an effective model-based scheme for solving the LOORP in MJLSs.
    • To introduce a reinforcement learning (RL)-based iteration scheme for LOORP, enhancing robustness and applicability.
    • To validate the proposed RL scheme's performance in a practical distributed generation system.

    Main Methods:

    • Improvement of existing methods for solving regulator equations.
  • Development of a model-based control scheme for LOORP in MJLSs.
  • Application of a reinforcement learning (RL)-based iteration scheme for robust output regulation.

    • Main Results:

    • A novel model-based scheme effectively addresses the LOORP for MJLSs.
    • The RL-based iteration scheme successfully solves LOORP even with partially unknown system dynamics and unstable initial control gains.
    • The proposed RL scheme demonstrates superior performance in an LCL-coupled inverter-based distributed generation system.

    Conclusions:

    • The developed model-based and RL-based schemes offer advanced solutions for the LOORP in MJLSs.
    • The RL-based approach provides a robust and adaptable method for output regulation under uncertainty.
    • The practical application highlights the efficacy and potential of the proposed RL scheme in power systems.