Kernel-based multi-step predictors for data-driven analysis and control of nonlinear systems through the velocity form
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866914896109633536 |
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| author | Verhoek, Chris Tóth, Roland |
| author_facet | Verhoek, Chris Tóth, Roland |
| contents | We propose kernel-based approaches for the construction of a single-step and multi-step predictor of the velocity form of nonlinear (NL) systems, which describes the time-difference dynamics of the corresponding NL system and admits a highly structured representation. The predictors in turn allow to formulate completely data-driven representations of the velocity form. The kernel-based formulation that we derive, inherently respects the structured quasi-linear and specific time-dependent relationship of the velocity form. This results in an efficient multi-step predictor for the velocity form and hence for nonlinear systems. Moreover, by using the velocity form, our methods open the door for data-driven behavioral analysis and control of nonlinear systems with global stability and performance guarantees. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_00688 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Kernel-based multi-step predictors for data-driven analysis and control of nonlinear systems through the velocity form Verhoek, Chris Tóth, Roland Systems and Control We propose kernel-based approaches for the construction of a single-step and multi-step predictor of the velocity form of nonlinear (NL) systems, which describes the time-difference dynamics of the corresponding NL system and admits a highly structured representation. The predictors in turn allow to formulate completely data-driven representations of the velocity form. The kernel-based formulation that we derive, inherently respects the structured quasi-linear and specific time-dependent relationship of the velocity form. This results in an efficient multi-step predictor for the velocity form and hence for nonlinear systems. Moreover, by using the velocity form, our methods open the door for data-driven behavioral analysis and control of nonlinear systems with global stability and performance guarantees. |
| title | Kernel-based multi-step predictors for data-driven analysis and control of nonlinear systems through the velocity form |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2408.00688 |