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

TL;DR

This paper introduces a machine learning-based open-loop controller for nylon artificial muscles that enables precise position control without external sensors, using a neural network trained on physics-based data.

Contribution

It presents a novel sensorless control method employing neural networks to map desired displacements to power inputs for thermal artificial muscles.

Findings

Successful position control without external sensors

Neural network accurately maps displacement to power

Controller adapts to different muscle types

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.