Motivated by the goal of having a building block in the design of direct data-driven controllers for nonlinear systems, we show how, for an unknown discrete-time bilinear system, the data collected in an offline open-loop experiment enable us to design a feedback controller and provide a guaranteed underapproximation of its basin of attraction. Both can be obtained by solving a linear matrix inequality for a fixed scalar parameter, and possibly iterating on different values of that parameter. The results of this data-based approach are compared with the ideal case when the model is known perfectly.

Data-based stabilization of unknown bilinear systems with guaranteed basin of attraction

Andrea Bisoffi;
2020-01-01

Abstract

Motivated by the goal of having a building block in the design of direct data-driven controllers for nonlinear systems, we show how, for an unknown discrete-time bilinear system, the data collected in an offline open-loop experiment enable us to design a feedback controller and provide a guaranteed underapproximation of its basin of attraction. Both can be obtained by solving a linear matrix inequality for a fixed scalar parameter, and possibly iterating on different values of that parameter. The results of this data-based approach are compared with the ideal case when the model is known perfectly.
2020
Direct data-driven control, Bilinear systems, Lyapunov functions, Basin of attraction, Linear matrix inequalities
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1226437
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