Identification of a Nonlinear Dynamic Model of MEMS with Unstable Switching Zone

Abstract

This paper focuses on the identification of nonlinear dynamic models for physical systems such as Micro-Electro-Mechanical Systems (MEMS). A first approach consists in transforming the specific inputoutput differential 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 between the dynamic model and the derived identification model, the latter remains in continuous-time, with a clear physical meaning of any of its components. In this paper, we propose to compare this method with two other ones: the first one is also based on global transformation of the model and application of the least-squares minimization and the second one is the well known Continuous-Discrete Extended Kalman Filter. The comparison is made first on simulated data and then on real measurement ones