Kernel-based multi-step predictors for data-driven analysis and control of nonlinear systems through the velocity form
<|reference_start|>Kernel-based multi-step predictors for data-driven analysis and control of nonlinear systems through the velocity form: 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.<|reference_end|>
@article{verhoek2024kernel-based,
    title={Kernel-based multi-step predictors for data-driven analysis and control
  of nonlinear systems through the velocity form},
    author={Chris Verhoek and Roland T'oth},
    journal={arXiv preprint arXiv:2408.00688},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.00688},
    primaryClass={eess.SY cs.SY}
}