Learning-Based Control Strategies for Soft Robots: Theory, Achievements, and Future Challenges-Reference-Cited by-同舟云学术

Learning-Based Control Strategies for Soft Robots: Theory, Achievements, and Future Challenges

Published:2023-06 Issue:3 Volume:43 Page:100-113
ISSN:1066-033X
Container-title:IEEE Control Systems
language:
Short-container-title:IEEE Control Syst.

Author:

Laschi Cecilia¹^ORCID,Thuruthel Thomas George²^ORCID,Lida Fumiya³^ORCID,Merzouki Rochdi⁴^ORCID,Falotico Egidio⁵^ORCID

Affiliation:

1. National University of Singapore, Singapore, Singapore

2. Department of Computer Science, UCL, London, U.K.

3. Department of Engineering, University of Cambridge, Cambridge, U.K.

4. Polytech Lille, University of Lille, Lille, Villeneuve d’Ascq, France

5. BioRobotics Institute of Scuola Superiore Sant’Anna, Pisa, Italy

Funder

Future and Emerging Technologies program of the European Commission

PROBOSCIS

GrowBot

Marie Skłodowska Action of the European Commission

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Modeling and Simulation,Control and Systems Engineering,Electrical and Electronic Engineering,Modeling and Simulation,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/5488303/10136404/10136428.pdf?arnumber=10136428

Reference82 articles.

1. Feedback-error-learning neural network for trajectory control of a robotic manipulator

2. On the use of empirical gramians for controllability and observability analysis

3. Forward Models for Physiological Motor Control

4. Data-Based Controllability and Observability Analysis of Linear Discrete-Time Systems

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