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arXiv · 2310.02583

Machine Learning-Enabled Precision Position Control and Thermal Regulation in Advanced Thermal Actuators

Seyed Mo Mirvakili·Ehsan Haghighat·Douglas Sim
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Abstract

With their unique combination of characteristics - an energy density almost 100 times that of human muscle, and a power density of 5.3 kW/kg, similar to a jet engine's output - Nylon artificial muscles stand out as particularly apt for robotics applications. However, the necessity of integrating sensors and controllers poses a limitation to their practical usage. Here we report a constant power open-loop controller based on machine learning. We show that we can control the position of a nylon artificial muscle without external sensors. To this end, we construct a mapping from a desired displacement trajectory to a required power using an ensemble encoder-style feed-forward neural network. The neural controller is carefully trained on a physics-based denoised dataset and can be fine-tuned to accommodate various types of thermal artificial muscles, irrespective of the presence or absence of hysteresis.

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Seyed Mo Mirvakili, Ehsan Haghighat, Douglas Sim. 2023-10-04. Machine Learning-Enabled Precision Position Control and Thermal Regulation in Advanced Thermal Actuators. https://arxiv.org/abs/2310.02583

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