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    This study introduces inverse reinforcement learning (IRL) control for nonlinear networked control systems (NCSs). The algorithms effectively mimic target trajectories despite data dropouts and disturbances, enhancing system performance.

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    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Networked control systems (NCSs) face challenges from data dropouts and external disturbances.
    • Mimicking target system trajectories is crucial for NCS performance but difficult with unknown dynamics.

    Purpose of the Study:

    • To develop inverse reinforcement learning (IRL) control algorithms for nonlinear NCSs.
    • To address challenges of data dropouts and external disturbances in trajectory imitation.
    • To enable effective control under partial model knowledge.

    Main Methods:

    • Developed a model-based IRL algorithm integrating $H_{\infty}$ control for disturbance rejection and uncertainty management.
    • Proposed a neural-network-based, data-driven IRL algorithm to infer cost functions and control policies from available data.
    • Addressed data dropouts in trajectory, state feedback, and control input data transmission.

    Main Results:

    • Demonstrated effective trajectory imitation capabilities of the proposed IRL algorithms.
    • Showcased robustness against random data dropouts and external disturbances.
    • Validated the performance through comprehensive simulation studies.

    Conclusions:

    • The developed IRL algorithms enable robust trajectory imitation in nonlinear NCSs.
    • The methods reduce reliance on complete system models, offering practical advantages.
    • Successful trajectory mimicry is achieved despite significant operational uncertainties.