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Cited by 671

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Geometric Control of Mechanical Systems

·2005·IEEE Transactions on Automatic Control

Abstract

This talk will outline a comprehensive set of modeling, analysis and design techniques for a class of mechanical systems. We concern ourselves with simple mechanical control systems, that is, systems whose Lagrangian is kinetic energy minus potential energy. Example devices include robotic manipulators, aerospace and underwater vehicles, and mechanisms that locomote exploiting nonholonomic constraints. Borrowing techniques from nonlinear control and geometric mechanics, we propose a coordinateinvariant control theory for this class of systems. First, we take a Riemannian geometric approach to modeling systems dened on smooth manifolds, subject to nonholonomic constraints, external forces and control forces. We also model mechanical systems on groups and symmetries. Second, we analyze some control-theoretic properties of this class of systems, including controllability, averaged response to oscillatory controls, and kinematic reductions. Finally, we exploit the modeling and analysis results to tackle control design problems. Starting from controllability and kinematic reduction assumptions we propose some algorithms for generating and tracking trajectories.

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Metadata

Venue
IEEE Transactions on Automatic Control
Published
2005-12
Volume
50
Issue
12
Pages
2111-2111
ISSN
0018-9286 (ISSN-L)
OpenAlex
W57077127
Cited by
671
References
1
FWCI
88.97
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Geometric Control of Mechanical Systems. https://app.cochat.ai/papers/10.1109/tac.2005.860277
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Geometric Control of Mechanical Systems. (2005). IEEE Transactions on Automatic Control. https://doi.org/10.1109/tac.2005.860277

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.