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  Computed Torque Control with Nonparametric Regression Models

Nguyen-Tuong, D., Seeger, M., & Peters, J. (2008). Computed Torque Control with Nonparametric Regression Models. In 2008 American Control Conference (pp. 212-217). Piscataway, NJ, USA: IEEE.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-C8F7-8 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-4313-C
Genre: Conference Paper

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 Creators:
Nguyen-Tuong, D1, 2, Author              
Seeger, M1, 2, Author              
Peters, J1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: Computed torque control allows the design of considerably more precise, energy-efficient and compliant controls for robots. However, the major obstacle is the requirement of an accurate model for torque generation, which cannot be obtained in some cases using rigid-body formulations due to unmodeled nonlinearities, such as complex friction or actuator dynamics. In such cases, models approximated from robot data present an appealing alternative. In this paper, we compare two nonparametric regression methods for model approximation, i.e., locally weighted projection regression (LWPR) and Gaussian process regression (GPR). While locally weighted regression was employed for real-time model estimation in learning adaptive control, Gaussian process regression has not been used in control to-date due to high computational requirements. The comparison includes the assessment of model approximation for both regression methods using data originated from SARCOS robot arm, as well as an evaluation of the robot tracking p erformance in computed torque control employing the approximated models. Our results show that GPR can be applied for real-time control achieving higher accuracy. However, for the online learning LWPR is superior by reason of lower computational requirements.

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 Dates: 2008-06
 Publication Status: Published in print
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 Rev. Method: -
 Identifiers: DOI: 10.1109/ACC.2008.4586493
BibTex Citekey: 4976
 Degree: -

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Title: American Control Conference (ACC 2008)
Place of Event: Seattle, WA, USA
Start-/End Date: 2008-06-11 - 2008-06-13

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Title: 2008 American Control Conference
Source Genre: Proceedings
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Publ. Info: Piscataway, NJ, USA : IEEE
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 212 - 217 Identifier: ISBN: 978-1-4244-2079-7