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.
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105206
105206
Alessandro Luppi; Claudio De Persis; Pietro Tesi
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2158/1279962
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