Learning Control of Second-Order Systems via Nonlinearity Cancellation

Conference paper (2023)

Authors

M. Guo Team Meichen Guo - Mechanical, Maritime and Materials Engineering
Claudio De Persis Rijksuniversiteit Groningen
Pietro Tesi University of Florence
Research Group
Team Meichen Guo (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2023 M. Guo, Claudio De Persis, Pietro Tesi
DOI: https://doi.org/10.1109/CDC49753.2023.10383435
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Published Date

2023

Language

English

Reuse Rights

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Delft Center for Systems and Control

Research Group

Team Meichen Guo

Abstract

A technique to design controllers for nonlinear systems from data consists of letting the controllers learn the nonlinearities, cancel them out and stabilize the closed-loop dynamics. When control and nonlinearities are unmatched, the technique leads to an approximate cancellation and local stability results are obtained. In this paper, we show that, if the system has some structure that the designer can exploit, an iterative use of the data leads to a globally stabilizing controller even when control and nonlinearities are unmatched.

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