Adaptive Neural Network Dynamic Surface Control: An Evaluation on the Musculoskeletal Robot Anthrob

Michael Jaentsch, Steffen Wittmeier, Konstantinos Dalamagkidis, Guido Herrmann, Alois Knoll

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

5 Citations (Scopus)

Abstract

The soft robotics approach is widely considered to enable robots in the near future to leave their cages and move freely in our modern homes and manufacturing sites. Musculoskeletal robots are such soft robots which feature passively compliant actuation, while leveraging the advantages of tendon-driven systems. Even though these robots have been intensively researched within the last decade, high-performance feedback control laws have only very recently been developed. In [1], a controller was developed utilizing Dynamic Surface Control (DSC), an extension to backstepping, with an adaptive neural network compensator for joint as well as muscle friction. We compare these novel control strategies to Computed Force Control (CFC), an existing technique from the field of tendon-driven control, yielding highly improved trajectory tracking. The musculoskeletal robot Anthrob [2] serves as a benchmark.
Original languageEnglish
Title of host publication2015 IEEE International Conference on Robotics and Automation (ICRA 2015)
Subtitle of host publicationProceedings of a meeting held 26-30 May 2015, Seattle, Washington, USA
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages4347-4352
Number of pages6
ISBN (Electronic)9781479969234
ISBN (Print)9781479969241
DOIs
Publication statusPublished - Aug 2015

Publication series

Name
ISSN (Print)1050-4729

Keywords

  • backstepping, Compliant actuation, musculoskeletal robots, non-linear control, adaptive control

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