A neural network-based approach to hybrid systems identification for control Article Swipe
Filippo Fabiani
,
Bartolomeo Stellato
,
Daniele Masti
,
Paul J. Goulart
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2404.01814
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2404.01814
We consider the problem of designing a machine learning-based model of an unknown dynamical system from a finite number of (state-input)-successor state data points, such that the model obtained is also suitable for optimal control design. We adopt a neural network (NN) architecture that, once suitably trained, yields a hybrid system with continuous piecewise-affine (PWA) dynamics that is differentiable with respect to the network's parameters, thereby enabling the use of derivative-based training procedures. We show that a careful choice of our NN's weights produces a hybrid system model with structural properties that are highly favorable when used as part of a finite horizon optimal control problem (OCP). Specifically, we rely on available results to establish that optimal solutions with strong local optimality guarantees can be computed via nonlinear programming (NLP), in contrast to classical OCPs for general hybrid systems which typically require mixed-integer optimization. Besides being well-suited for optimal control design, numerical simulations illustrate that our NN-based technique enjoys very similar performance to state-of-the-art system identification methods for hybrid systems and it is competitive on nonlinear benchmarks.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2404.01814
- https://arxiv.org/pdf/2404.01814
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4393931802
All OpenAlex metadata
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https://openalex.org/W4393931802Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2404.01814Digital Object Identifier
- Title
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A neural network-based approach to hybrid systems identification for controlWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-04-02Full publication date if available
- Authors
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Filippo Fabiani, Bartolomeo Stellato, Daniele Masti, Paul J. GoulartList of authors in order
- Landing page
-
https://arxiv.org/abs/2404.01814Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2404.01814Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2404.01814Direct OA link when available
- Concepts
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Identification (biology), Artificial neural network, Control (management), Computer science, Artificial intelligence, Control engineering, Engineering, Biology, BotanyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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