Recent contributions have investigated the use of regularization in linear system identification. In particular, regularizing high-order FIR models to enforce stability while controlling complexity and regularity of the impulse response provides state-of-the-art performance in linear system identification. An advantage of such techniques is that they also enjoy a Bayesian interpretation that yields confidence intervals around the nominal system.In this work it is shown that these features can be useful for the design of a controller in a linear setting. In particular, the posterior distribution of the impulse response available from the Bayesian framework is exploited to perform control design using three different approaches; one of these is the minimization of the expected (posterior) distance from the desired closed loop system. Numerical studies illustrate the good performance of the proposed approaches.
Bayesian Kernel-Based Linear Control Design
Chiuso A.;Formentin S.;
2019-01-01
Abstract
Recent contributions have investigated the use of regularization in linear system identification. In particular, regularizing high-order FIR models to enforce stability while controlling complexity and regularity of the impulse response provides state-of-the-art performance in linear system identification. An advantage of such techniques is that they also enjoy a Bayesian interpretation that yields confidence intervals around the nominal system.In this work it is shown that these features can be useful for the design of a controller in a linear setting. In particular, the posterior distribution of the impulse response available from the Bayesian framework is exploited to perform control design using three different approaches; one of these is the minimization of the expected (posterior) distance from the desired closed loop system. Numerical studies illustrate the good performance of the proposed approaches.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
