Mutation detection and fast identification of switching system based on data-driven method

Abstract

In the engineering field, switching systems have been extensively studied, where sudden changes of parameter value and structural form have a significant impact on the operational performance of the system. Therefore, it is important to predict the behavior of the switching system, which includes the accurate detection of mutation points and rapid reidentification of the model. However, few efforts have been contributed to accurately locating the mutation points. In this paper, we propose a new measure of mutation detection — the threshold-based switching index by analogy with the Lyapunov exponent. We give the algorithm for selecting the optimal threshold, which greatly reduces the additional data collection and the relative error of mutation detection. In the system identification part, considering the small data amount available and noise in the data, the abrupt sparse Bayesian regression (abrupt-SBR) method is proposed. This method captures the model changes by updating the previously identified model, which requires less data and is more robust to noise than identifying the new model from scratch. With two representative dynamical systems, we illustrate the application and effectiveness of the proposed methods. Our research contributes to the accurate prediction and possible control of switching system behavior.