In this paper, we consider the problem of learning a nonlinear controller directly from experimental data. We assume that an existing, unknown controller, able to stabilize the plant, is available, and that input-output measurements can be collected during closed loop operation. A theoretical analysis shows that the error between the input issued by the existing controller and the input given by the learned one shall have low variability in order to achieve closed loop stability. This result is exploited to derive a $\ell_{1}$-norm regularized learning algorithm that achieves the stability condition as the number of data points tends to infinity. The approach is completely based on convex optimization.

Learning a nonlinear controller from data: theory and computation / Fagiano, Lorenzo; Novara, Carlo. - (2014). (Intervento presentato al convegno 53rd IEEE Conference on Decision and Control tenutosi a Los Angeles, California, USA).

Learning a nonlinear controller from data: theory and computation

FAGIANO, LORENZO;NOVARA, Carlo
2014

Abstract

In this paper, we consider the problem of learning a nonlinear controller directly from experimental data. We assume that an existing, unknown controller, able to stabilize the plant, is available, and that input-output measurements can be collected during closed loop operation. A theoretical analysis shows that the error between the input issued by the existing controller and the input given by the learned one shall have low variability in order to achieve closed loop stability. This result is exploited to derive a $\ell_{1}$-norm regularized learning algorithm that achieves the stability condition as the number of data points tends to infinity. The approach is completely based on convex optimization.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2588366
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