Direct data-driven LPV control of nonlinear systems: An experimental result
arXiv:2211.17191 · doi:10.1016/j.ifacol.2023.10.1191
Abstract
We demonstrate that direct data-driven control of nonlinear systems can be successfully accomplished via a behavioral approach that builds on a Linear Parameter-Varying (LPV) system concept. An LPV data-driven representation is used as a surrogate LPV form of the data-driven representation of the original nonlinear system. The LPV data-driven control design that builds on this representation form uses only measurement data from the nonlinear system and a priori information on a scheduling map that can lead to an LPV embedding of the nonlinear system behavior. Efficiency of the proposed approach is demonstrated experimentally on a nonlinear unbalanced disc system showing for the first time in the literature that behavioral data-driven methods are capable to stabilize arbitrary forced equilibria of a real-world nonlinear system by the use of only 7 data points.
Accepted to the 22nd IFAC World Congress 2023 (IFAC2023)
References in corpus (1)
Cited by in corpus (6)
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