Matching Sensing to Actuation and Dynamics in Distributed Sensorimotor Architectures
In this article we explore the benefits of matching sensing characteristics to actuation and dynamics in the context of spatially distributed sensorimotor architectures, motivated by recently discovered connections in blowfly flight physics and visual physiology. Within the proposed framework, we pr...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10836695/ |
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| author | Zoe Turin Graham K. Taylor Holger G. Krapp Emily Jensen J. Sean Humbert |
| author_facet | Zoe Turin Graham K. Taylor Holger G. Krapp Emily Jensen J. Sean Humbert |
| author_sort | Zoe Turin |
| collection | DOAJ |
| description | In this article we explore the benefits of matching sensing characteristics to actuation and dynamics in the context of spatially distributed sensorimotor architectures, motivated by recently discovered connections in blowfly flight physics and visual physiology. Within the proposed framework, we present novel semidefinite programs with linear matrix inequality constraints which yield directions encoded in the sensory output that maximize the smallest unstable Hankel singular value of the system. This is a coordinate-invariant metric that minimizes the control energy required to stabilize an unstable system and maximizes the achievable robustness to unstructured additive uncertainty over all possible controllers. We also reformulate the problem to achieve a prescribed speed of response, which can be applied to stable and unstable systems. We adapt a maximally robust controller synthesis method from previous work which provides a tool for validation. We additionally present an <inline-formula> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> controller formulation which allows for a trade-off between minimization of actuator effort and robustness versus disturbance rejection and tracking capability, providing design flexibility over the maximally robust controller. |
| format | Article |
| id | doaj-art-39ae03ad748547a6b525eb4498e5712b |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-39ae03ad748547a6b525eb4498e5712b2025-01-25T00:02:00ZengIEEEIEEE Access2169-35362025-01-0113135841360510.1109/ACCESS.2025.352834310836695Matching Sensing to Actuation and Dynamics in Distributed Sensorimotor ArchitecturesZoe Turin0https://orcid.org/0009-0000-6548-0250Graham K. Taylor1Holger G. Krapp2Emily Jensen3https://orcid.org/0000-0002-7373-1539J. Sean Humbert4https://orcid.org/0000-0002-0863-875XDepartment of Mechanical Engineering, University of Colorado Boulder (UCB), Boulder, CO, USADepartment of Biology, University of Oxford, Oxford, U.K.Department of Bioengineering, Imperial College London, South Kensington Campus, London, U.K.Department of Electrical, Computer and Energy Engineering, University of Colorado Boulder (UCB), Boulder, CO, USADepartment of Mechanical Engineering, University of Colorado Boulder (UCB), Boulder, CO, USAIn this article we explore the benefits of matching sensing characteristics to actuation and dynamics in the context of spatially distributed sensorimotor architectures, motivated by recently discovered connections in blowfly flight physics and visual physiology. Within the proposed framework, we present novel semidefinite programs with linear matrix inequality constraints which yield directions encoded in the sensory output that maximize the smallest unstable Hankel singular value of the system. This is a coordinate-invariant metric that minimizes the control energy required to stabilize an unstable system and maximizes the achievable robustness to unstructured additive uncertainty over all possible controllers. We also reformulate the problem to achieve a prescribed speed of response, which can be applied to stable and unstable systems. We adapt a maximally robust controller synthesis method from previous work which provides a tool for validation. We additionally present an <inline-formula> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> controller formulation which allows for a trade-off between minimization of actuator effort and robustness versus disturbance rejection and tracking capability, providing design flexibility over the maximally robust controller.https://ieeexplore.ieee.org/document/10836695/Bio-inspired roboticsH infinity controlmatched filterssemidefinite programmingsensor arraysrobust control |
| spellingShingle | Zoe Turin Graham K. Taylor Holger G. Krapp Emily Jensen J. Sean Humbert Matching Sensing to Actuation and Dynamics in Distributed Sensorimotor Architectures IEEE Access Bio-inspired robotics H infinity control matched filters semidefinite programming sensor arrays robust control |
| title | Matching Sensing to Actuation and Dynamics in Distributed Sensorimotor Architectures |
| title_full | Matching Sensing to Actuation and Dynamics in Distributed Sensorimotor Architectures |
| title_fullStr | Matching Sensing to Actuation and Dynamics in Distributed Sensorimotor Architectures |
| title_full_unstemmed | Matching Sensing to Actuation and Dynamics in Distributed Sensorimotor Architectures |
| title_short | Matching Sensing to Actuation and Dynamics in Distributed Sensorimotor Architectures |
| title_sort | matching sensing to actuation and dynamics in distributed sensorimotor architectures |
| topic | Bio-inspired robotics H infinity control matched filters semidefinite programming sensor arrays robust control |
| url | https://ieeexplore.ieee.org/document/10836695/ |
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