Model-based control can improve the performance of artificial cilia

Conference paper (2021)

Authors

Edoardo Milana Katholieke Universiteit Leuven, Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR)
Benjamin Gorissen Katholieke Universiteit Leuven, Harvard University
Dominiek Reynaerts Katholieke Universiteit Leuven
C. Della Santina Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering , Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR)
Research Group
Learning & Autonomous Control (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2021 Edoardo Milana, Francesco Stella, Benjamin Gorissen, Dominiek Reynaerts, C. Della Santina
DOI
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https://doi.org/10.1109/RoboSoft51838.2021.9479348
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Published Date

2021

Language

English

Reuse Rights

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Learning & Autonomous Control

Abstract

Artificial cilia are a prominent example of physical intelligence. Their mechanical properties are often designed so to achieve desired motions in response to very simple actuation patterns. Yet, variability in the mechanical properties are inherent in these systems. This may critically disrupt the input-output relation, resulting in a final behavior completely different from the desired one. In this Communication we investigate the possibility of designing a robotic brain that helps the cilium to maintain its physical intelligence. We achieve that by closing a model-based control loop which tracks the position of the end effector while compensating for drag forces. We propose experiments to characterize our model, and extensive simulations validating the results in different conditions. This work is intended as a proof of concept, which will be further expanded in future work.