Machine Learning-Enabled Precision Position Control and Thermal
  Regulation in Advanced Thermal Actuators
Seyed Mo Mirvakili Ehsan Haghighat Douglas Sim
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.