Neuromechanical Control Architectures of Arthropod Locomotion
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Summary
A theoretical framework for the experimental study of neuromechanical control in animals is defined, based on mathematical concepts from dynamical systems theory, and the kinematic phase based models extended to the construction of a linearized approximation of animal dynamics based on Floquet theory.
- Type
- article
- Published
- 2009-01-01
- Cited by
- 24
- References
- 170
- Access
- Open access
- OpenAlex
- https://openalex.org/W55273051
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:107436714
Keywords
Floquet theory, Kinematics, Residual, Computer science, Dynamical systems theory
References
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- Mechanical models for insect locomotion: dynamics and stability in the horizontal plane – II. Application
- Event-Triggered Real-Time Scheduling of Stabilizing Control Tasks
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- Neurobiological bases of rhythmic motor acts in vertebrates.
- The Dynamics of Legged Locomotion: Models, Analyses, and Challenges
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- Finding the dimension of slow dynamics in a rhythmic system
- Instantaneous kinematic phase reflects neuromechanical response to lateral perturbations of running cockroaches
- Constructing predictive models of human running
- Neuromuscular and biomechanical compensation for wing asymmetry in insect hovering flight
- Stimulus predictability mediates a switch in locomotor smooth pursuit performance for Eigenmannia virescens
- Variability, Symmetry, and Dynamics in Human Rhythmic
- A Hybrid Dynamical Systems Theory for Legged Locomotion
- Neuromechanical Control of Paddle Juggling
- Walking like an ant: a quantitative and experimental approach to understanding locomotor mimicry in the jumping spider Myrmarachne formicaria
- Modular Hopping and Running via Parallel Composition
- A Data-Driven Approach to Connection Modeling
- Geometrically optimal gaits: a data-driven approach
- Gait modeling and optimization for the perturbed Stokes regime
- Data-driven geometric system identification for shape-underactuated dissipative systems
- Estimating Phase From Observed Trajectories Using the Temporal 1-Form
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