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An intelligent thermal controller based on the deep deterministic policy gradient, called DRLTC, is proposed. Two types of reinforcement learning agents were designed in DRLTC, which can automatically adjust the control parameters of the thermal controllers and self-optimize online after training. The DRLTC has better universality, stronger robustness, and achieve more energy saving. In addition, the proposed intelligent thermal control algorithm is not only applicable to space telescopes, but also to all types of thermal controllers.

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This page is a summary of: Intelligent Thermal Control Algorithm Based on Deep Deterministic Policy Gradient for Spacecraft, Journal of Thermophysics and Heat Transfer, October 2020, American Institute of Aeronautics and Astronautics (AIAA), DOI: 10.2514/1.t5951.
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