Kernel-based multi-step predictors for data-driven analysis and control of nonlinear systems through the velocity form
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.
- Publication:
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arXiv e-prints
- Pub Date:
- August 2024
- DOI:
- 10.48550/arXiv.2408.00688
- arXiv:
- arXiv:2408.00688
- Bibcode:
- 2024arXiv240800688V
- Keywords:
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- Electrical Engineering and Systems Science - Systems and Control
- E-Print:
- 15 pages