Identification of Electrostatically Actuated MEMS Models from Real Measurement Data

Abstract

This paper focuses on the identification of nonlinear dynamic models for physical systems such as Micro-Electro-Mechanical Systems (MEMS) from measurement data. The proposed approach consists in transforming, by means of suitable global operations, the specific input-output differential physical model of the system elaborated from physical analysis, in such a way that we get a new equivalent model formulation specifically adapted to the identification problem. Thanks to the equivalence of the dynamic model and the derived identification problem, the so-identified model remains of continuous-time type, with a clear physical meaning of any of its components, which is not the case when using, for example, black-boxes approaches. The method is implemented on real measurement data from a physical system.