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    Area of Science:

    • Control Systems Engineering
    • Stochastic Systems
    • Machine Learning for Control

    Background:

    • Hidden Markov jump systems present challenges due to asynchronous controller and system modes.
    • Existing control designs often require precise temporal alignment and detailed system models.

    Purpose of the Study:

    • To design an asynchronous H-infinity control strategy for discrete-time hidden Markov jump systems.
    • To develop a model-free policy iteration algorithm that accommodates asynchronous phenomena.

    Main Methods:

    • A hidden Markov model was utilized to represent the asynchronous behavior.
    • A zero-sum control and disturbance strategy was formulated.
    • A model-free policy iteration algorithm was employed to solve the algebraic Riccati equation iteratively using collected data.

    Main Results:

    • The asynchronous policy iteration algorithm demonstrated flexibility by not requiring strict temporal alignment.
    • The iterative data-driven approach circumvented the need for system-internal and transfer probability information.
    • Monotonic convergence of the policy iteration algorithm to an optimal solution was verified.
    • The resulting system was proven to be stochastically stable in the mean-square sense.

    Conclusions:

    • The proposed model-free asynchronous control approach is effective for hidden Markov jump systems.
    • The method offers enhanced flexibility and robustness by relying on data rather than explicit system models.
    • Simulation results on a DC motor system validated the practical applicability of the control strategy.