Deriving Mechanical Structures in Physical Coordinates from Data-Driven State-Space Realizations

• Type: Article in International Conference Proceedings Book
• Date: 2015

Authors

P. Lopes dos Santos (Author) FEUP; Ramos, JA (Author) Other; Azevedo Perdicoulis, TP (Author) Other; Jorge Martins de Carvalho (Author) FEUP

Conference proceedings International

Title: Proceedings of the American Control Conference

Pages: 1107-1112

2015 American Control Conference, ACC 2015

1 July 2015 through 3 July 2015

Indexing

ISI Web of Knowledge - 3 Citations

Scopus - 4 Citations

Other information

Authenticus ID: P-00G-KXH

DOI: 10.1109/acc.2015.7170881

Abstract (EN): In this article, the problem of deriving a physical model of a mechanical structure from an arbitrary state-space realization is addressed. As an alternative to finite element formulations, the physical parameters of a model may be directly obtained from identified parametric models. However, these methods are limited by the number of available sensors and often lead to poor predictive models. Additionally, the most efficient identification algorithms retrieve models where the physical parameters are hidden. This last difficulty is known in the literature as the inverse vibration problem. In this work, an approach to the inverse vibration problem is proposed. It is based on a similarity transformation and the requirement that every degree of freedom should contain a sensor and an actuator (full instrumented system) is relaxed to a sensor or an actuator per degree of freedom, with at least one co-located pair (partially instrumented system). The physical parameters are extracted from a state-space realization of the former system. It is shown that this system has a symmetric transfer function and this symmetry is exploited to derive a state-space realization from an identified model of the partially instrumented system. A subspace continuous-time system identification algorithm previously proposed by the authors in [1] is used to estimate this model from the IO data.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6