Using Spectral Submanifolds for Nonlinear Periodic Control

Conference Paper (2022)
Author(s)

Florian Mahlknecht (Stanford University)

John Irvin Alora (Stanford University)

Shobhit Jain (Institute of Mechanical Systems)

Edward Schmerling (Stanford University)

Riccardo Bonalli (CNRS - Guyancourt)

George Haller (Institute of Mechanical Systems)

Marco Pavone (Stanford University)

Affiliation
External organisation
DOI related publication
https://doi.org/10.1109/CDC51059.2022.9992400 Final published version
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Publication Year
2022
Language
English
Affiliation
External organisation
Pages (from-to)
6548-6555
ISBN (electronic)
9781665467612
Event
IEEE 61st Conference on Decision and Control (CDC 2022) (2022-12-06 - 2022-12-09), Cancún, Mexico
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180

Abstract

Very high dimensional nonlinear systems arise in many engineering problems due to semi-discretization of the governing partial differential equations, e.g. through finite element methods. The complexity of these systems present computational challenges for direct application to automatic control. While model reduction has seen ubiquitous applications in control, the use of nonlinear model reduction methods in this setting remains difficult. The problem lies in preserving the structure of the nonlinear dynamics in the reduced order model for high-fidelity control. In this work, we leverage recent advances in Spectral Submanifold (SSM) theory to enable model reduction under well-defined assumptions for the purpose of efficiently synthesizing feedback controllers.