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Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
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Reinforcement learning based robust control algorithms for coherent pulse stacking.

Abulikemu Abuduweili, Jie Wang, Bowei Yang

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    We developed a new algorithm, soft actor-critic with stochastic parallel gradient descent with momentum (SAC-SPGDM), for precise control of delay lines. This method efficiently achieves coherent pulse stacking for 128 pulses in just 25 steps.

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    Area of Science:

    • Optical Engineering
    • Control Systems
    • Machine Learning

    Background:

    • Coherent pulse stacking is crucial for advanced optical systems.
    • Precise control of delay lines is essential for effective pulse stacking.
    • Existing control methods may lack speed and robustness.

    Purpose of the Study:

    • To develop a fast and robust control algorithm for delay lines.
    • To achieve efficient coherent pulse stacking using an optimized algorithm.
    • To demonstrate the algorithm's performance in a multi-stage stacking scenario.

    Main Methods:

    • Combined stochastic parallel gradient descent with momentum (SPGDM) and soft actor-critic (SAC).
    • Developed a novel hybrid algorithm named SAC-SPGDM.
    • Utilized simulation to test the algorithm's efficacy.

    Main Results:

    • The SAC-SPGDM algorithm successfully identified optimal delay-line positions.
    • Achieved coherent stacking of 128 pulses.
    • Demonstrated efficient 7-stage pulse stacking within 25 control steps.

    Conclusions:

    • The SAC-SPGDM algorithm offers a powerful solution for fast and robust delay-line control.
    • This method significantly enhances the efficiency of coherent pulse stacking.
    • The algorithm shows promise for applications requiring precise optical pulse manipulation.