In this paper, we directly design a state feedback controller that stabilizes a class of uncertain nonlinear systems solely based on input-state data collected from a finite-length experiment. Necessary and sufficient conditions are derived to guarantee that the system is absolutely stabilizable and a controller is designed. Results derived under some relaxed prior information about the system, strengthened data assumptions and perturbed data are also discussed. All the results are based on semi-definite programs that depend on input-state data only, which – once solved – directly return controllers. As such they represent end-to-end solutions to the problem of learning control from data for an important class of nonlinear systems. Numerical examples illustrate the method with different levels of prior information.

On data-driven stabilization of systems with nonlinearities satisfying quadratic constraints / Alessandro Luppi; Claudio De Persis; Pietro Tesi. - In: SYSTEMS & CONTROL LETTERS. - ISSN 0167-6911. - STAMPA. - 163:(2022), pp. 105206-105206. [10.1016/j.sysconle.2022.105206]

On data-driven stabilization of systems with nonlinearities satisfying quadratic constraints

Pietro Tesi
2022

Abstract

In this paper, we directly design a state feedback controller that stabilizes a class of uncertain nonlinear systems solely based on input-state data collected from a finite-length experiment. Necessary and sufficient conditions are derived to guarantee that the system is absolutely stabilizable and a controller is designed. Results derived under some relaxed prior information about the system, strengthened data assumptions and perturbed data are also discussed. All the results are based on semi-definite programs that depend on input-state data only, which – once solved – directly return controllers. As such they represent end-to-end solutions to the problem of learning control from data for an important class of nonlinear systems. Numerical examples illustrate the method with different levels of prior information.
2022
163
105206
105206
Alessandro Luppi; Claudio De Persis; Pietro Tesi
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1279962
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 20
  • ???jsp.display-item.citation.isi??? 17
social impact