Space-Based Antenna Morphing using Reinforcement Learning
نویسنده
چکیده
Shape Memory Alloys (SMA’s) have been employed to enhance structural properties and increase the ability of structures to adapt and conform as desired. Morphing technology has also proven beneficial to space hardware deployment, in addition to satellite antenna design. In this research, Reinforcement Learning is utilized with an antenna model to demonstrate that antenna elements equipped with SMA actuators and subject to simulated SMA dynamics are capable of independently learning the optimal concavity. Appropriate applications of Q-Learning and -greedy methods in this manner would allow space-based radar and communication systems would be able to decrease the quantity of antennas currently mounted on spacecraft. The implication of a single antenna capable of altering its geometry is a revolutionary world-wide compatibility of receivers and transmitters.
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