A Data-Driven Modeling Framework of Time-Dependent Switched Dynamical Systems via Extreme Learning Machine

TL;DR

This paper presents a data-driven framework using Extreme Learning Machine to model time-dependent switched dynamical systems, including detection of switching events, segmentation, and subsystem dynamics learning, demonstrated on a DC-DC converter.

Contribution

It introduces a novel approach combining switching detection and ELM-based modeling for time-dependent switched systems, enhancing accuracy and efficiency.

Findings

Effective detection of switching events in system data

Successful modeling of subsystem dynamics using ELM

Validated approach on a DC-DC converter example

Abstract

In this work, a data-driven modeling framework of switched dynamical systems under time-dependent switching is proposed. The learning technique utilized to model system dynamics is Extreme Learning Machine (ELM). First, a method is developed for the detection of the switching occurrence events in the training data extracted from system traces. The training data thus can be segmented by the detected switching instants. Then, ELM is used to learn the system dynamics of subsystems. The learning process includes segmented trace data merging and subsystem dynamics modeling. Due to the specific learning structure of ELM, the modeling process is formulated as an iterative Least-Squares (LS) optimization problem. Finally, the switching sequence can be reconstructed based on the switching detection and segmented trace merging results. An example of the data-driven modeling DC-DC converter is presented to show the effectiveness of the developed approach.