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- W4313444002 abstract "It is well known that conservative mechanical systems exhibit local oscillatory behaviours due to their elastic and gravitational potentials, which completely characterise these periodic motions together with the inertial properties of the system. The classification of these periodic behaviours and their geometric characterisation are in an on-going secular debate, which recently led to the so-called eigenmanifold theory. The eigenmanifold characterises nonlinear oscillations as a generalisation of linear eigenspaces. With the motivation of performing periodic tasks efficiently, we use tools coming from this theory to construct an optimization problem aimed at inducing desired closed-loop oscillations through a state feedback law. We solve the constructed optimization problem via gradient-descent methods involving neural networks. Extensive simulations show the validity of the approach." @default.
- W4313444002 created "2023-01-06" @default.
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- W4313444002 date "2022-12-29" @default.
- W4313444002 modified "2023-10-18" @default.
- W4313444002 title "Discovering Efficient Periodic Behaviours in Mechanical Systems via Neural Approximators" @default.
- W4313444002 doi "https://doi.org/10.48550/arxiv.2212.14253" @default.
- W4313444002 hasPublicationYear "2022" @default.
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