Abstract
Unknown nonlinear dynamics often limit the tracking performance of feedforward control. The aim of this paper is to develop a feedforward control framework that can compensate these unknown nonlinear dynamics using universal function approximators. The feedforward controller is parametrized as a parallel combination of a physics-based model and a neural network, where both share the same linear autoregressive (AR) dynamics. This parametrization allows for efficient output-error optimization through Sanathanan-Koerner (SK) iterations. Within each SK-iteration, the output of the neural network is penalized in the subspace of the physicsbased model through orthogonal projection-based regularization, such that the neural network captures only the unmodelled dynamics, resulting in interpretable models.
Original language | English |
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Title of host publication | Proceedings of the IEEE 61st Conference on Decision and Control (CDC 2022) |
Publisher | IEEE |
Pages | 2475-2480 |
ISBN (Print) | 978-1-6654-6761-2 |
DOIs | |
Publication status | Published - 2022 |
Event | IEEE 61st Conference on Decision and Control (CDC 2022) - Cancún, Mexico Duration: 6 Dec 2022 → 9 Dec 2022 |
Conference
Conference | IEEE 61st Conference on Decision and Control (CDC 2022) |
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Country/Territory | Mexico |
City | Cancún |
Period | 6/12/22 → 9/12/22 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Keywords
- Friction
- Feedforward neural networks
- Nonlinear dynamical systems
- Feedforward systems
- Task analysis
- Optimization