Model Structure Identification of Hybrid Dynamical Systems based on Unsupervised Clustering and Variable Selection

Abstract

This paper proposes a systematic identification process for the hybrid dynamical system (HDS) estimating not only the coefficients but also the structure of the model. Beneficially speaking, the proposed system identification is used for the HDS system that the model structure, the number of modes, and the explanatory variables of the model are unknown. In the proposed method, a quantitative index to evaluate the number of modes is deployed and the optimal number of modes is determined from the measurement. The variable selection method is also introduced to determine the explanatory variables in each mode in a systematic manner. Two of piece-wise linear models which are prepared for the proposed system to identify and the validity of the proposed method is then demonstrated. Finally, the result of the proposed in comparison with the conventional system identification method for HDS is discussed.