Identification for robust control must deliver not only a nominal model, but also a reliable estimate of the uncertainty associated with the model. This paper addresses recent approaches to robust identification, that aim at dealing with contributions from the two main uncertainty sources: unmodeled dynamics and noise affecting the data. In particular, non-stationary Stochastic Embedding, Model Error Modeling based on prediction error methods and Set Membership Identification are considered. Moreover, we show how Set Membership Identification can be embedded into a Model Error Modeling framework. Model validation issues are easily addressed in the proposed framework. A discussion of asymptotic properties of all methods is presented. For all three methods, uncertainty is evaluated in terms of the frequency response, so that it can be handled by H-infinity control techniques. An example, where a nontrivial undermodeling is ensured by the presence of a nonlinearity in the system generating the data, is presented to compare these methods.

Reinelt, W., Garulli, A., & Ljung, L. (2002). Comparing different approaches to model error modeling in robust identification. AUTOMATICA, 38(5), 787-803 [10.1016/S0005-1098(01)00269-2].

Comparing different approaches to model error modeling in robust identification

GARULLI, ANDREA;
2002

Abstract

Identification for robust control must deliver not only a nominal model, but also a reliable estimate of the uncertainty associated with the model. This paper addresses recent approaches to robust identification, that aim at dealing with contributions from the two main uncertainty sources: unmodeled dynamics and noise affecting the data. In particular, non-stationary Stochastic Embedding, Model Error Modeling based on prediction error methods and Set Membership Identification are considered. Moreover, we show how Set Membership Identification can be embedded into a Model Error Modeling framework. Model validation issues are easily addressed in the proposed framework. A discussion of asymptotic properties of all methods is presented. For all three methods, uncertainty is evaluated in terms of the frequency response, so that it can be handled by H-infinity control techniques. An example, where a nontrivial undermodeling is ensured by the presence of a nonlinearity in the system generating the data, is presented to compare these methods.
Reinelt, W., Garulli, A., & Ljung, L. (2002). Comparing different approaches to model error modeling in robust identification. AUTOMATICA, 38(5), 787-803 [10.1016/S0005-1098(01)00269-2].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11365/26561
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