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This research introduces "weak invariance," a generalized symmetry concept for control systems that relaxes rigid classical constraints while preserving key structural benefits like dimensionality reduction. By allowing non-symmetric dynamics to be captured by a separate control system, the framework offers greater flexibility for handling external forces and complex scenarios, such as aerial vehicles under gravity. This theoretical advance lays the groundwork for more robust and adaptable symmetry-informed control algorithms in robotics and automation.
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A Weak Notion of Symmetry for Control Systems

arXiv3 min read

arXiv:2609.34173v1 Announce Type: cross Abstract: Symmetry (or invariance) is a powerful structural property that enables efficient, effective solutions for estimation and control. However, the constraints imposed on a system's dynamics by classical invariance make symmetry a very rigid property, which may be broken by external forces or confined to only a portion of the overall system. Seeking greater flexibility, this work introduces a novel relaxed notion of symmetry, termed ``weak invariance'', in which the non-symmetric part of the dynamics (the ``residual'') can be captured entirely by another control system evolving on the symmetry group. Weakly invariant systems are strictly more general than classical invariant systems, but they nonetheless enjoy many similar favorable properties. In particular, we prove that any weakly invariant system admits a cascade decomposition in which the driven subsystem is group affine, showing that weak symmetry generalizes not only classical symmetry, but also the (thus far distinct) class of group affine systems. We also show that a weak symmetry with autonomous residual can be factored out of the system's error dynamics, enabling yet a greater reduction of dimensionality as compared to classical symmetries. Finally, we study the example of an aerial vehicle under the influence of gravity, for which we propose a nine-dimensional weak symmetry (strictly containing the system's familiar four-dimensional classical symmetry). Weak invariance thus generalizes classical symmetry while also preserving key structural properties, thereby laying a foundation for more flexible methods of symmetry-informed control.

Abstract:Symmetry (or invariance) is a powerful structural property that enables efficient, effective solutions for estimation and control. However, the constraints imposed on a system's dynamics by classical invariance make symmetry a very rigid property, which may be broken by external forces or confined to only a portion of the overall system. Seeking greater flexibility, this work introduces a novel relaxed notion of symmetry, termed ``weak invariance'', in which the non-symmetric part of the dynamics (the ``residual'') can be captured entirely by another control system evolving on the symmetry group. Weakly invariant systems are strictly more general than classical invariant systems, but they nonetheless enjoy many similar favorable properties. In particular, we prove that any weakly invariant system admits a cascade decomposition in which the driven subsystem is group affine, showing that weak symmetry generalizes not only classical symmetry, but also the (thus far distinct) class of group affine systems. We also show that a weak symmetry with autonomous residual can be factored out of the system's error dynamics, enabling yet a greater reduction of dimensionality as compared to classical symmetries. Finally, we study the example of an aerial vehicle under the influence of gravity, for which we propose a nine-dimensional weak symmetry (strictly containing the system's familiar four-dimensional classical symmetry). Weak invariance thus generalizes classical symmetry while also preserving key structural properties, thereby laying a foundation for more flexible methods of symmetry-informed control.

Systems and Control (eess.SY); Robotics (cs.RO)

arXiv:2609.34173 [eess.SY]

(or arXiv:2609.34173v1 [eess.SY] for this version)

https://doi.org/10.48550/arXiv.2609.34173 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 330 words

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