This paper proposes a comparative study on the predictive accuracy of some classical system identification techniques with respect to recent learning-based approaches. The comparison is made on three case studies in the paradigm of Cyber-Physical Systems: a bilinear model whose parameters are derived from a real building experimental setup; an F-16 aircraft benchmark based on ground vibration experimental test data (with unknown model); a climate control experimental setup in a real building of the University of L'Aquila (with unknown model). For each case study, we compare diverse approaches producing predictive models of heterogeneous classes, whose accuracy is tested over a fixed time horizon. A discussion on the trade-off between predictive accuracy and model complexity is proposed with control applications in mind.

A comparison of classical identification and learning-based techniques for cyber-physical systems

De Iuliis V.;Smarra F.;D'Innocenzo A.
2021

Abstract

This paper proposes a comparative study on the predictive accuracy of some classical system identification techniques with respect to recent learning-based approaches. The comparison is made on three case studies in the paradigm of Cyber-Physical Systems: a bilinear model whose parameters are derived from a real building experimental setup; an F-16 aircraft benchmark based on ground vibration experimental test data (with unknown model); a climate control experimental setup in a real building of the University of L'Aquila (with unknown model). For each case study, we compare diverse approaches producing predictive models of heterogeneous classes, whose accuracy is tested over a fixed time horizon. A discussion on the trade-off between predictive accuracy and model complexity is proposed with control applications in mind.
978-1-6654-2258-1
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11697/171610
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