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arXiv · 2408.00688

Kernel-based multi-step predictors for data-driven analysis and control of nonlinear systems through the velocity form

Chris Verhoek·Roland Tóth
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Abstract

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.

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Chris Verhoek, Roland Tóth. 2024-08-01. Kernel-based multi-step predictors for data-driven analysis and control of nonlinear systems through the velocity form. https://arxiv.org/abs/2408.00688

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