On-line learning of dynamic systems: sparse regression meets Kalman filtering

TL;DR

This paper introduces the Sindy Kalman Filter (SKF), a novel real-time method combining sparse regression and Kalman filtering to identify dynamic systems with time-varying parameters from data.

Contribution

The paper extends sparsity-based modeling to real-time scenarios by integrating Kalman filtering, enabling dynamic, nonlinear system identification with adaptive parameter estimation.

Findings

SKF successfully identifies chaotic Lorenz systems with drifting parameters.

SKF accurately models real aircraft dynamics from flight data.

The method simplifies sparsity and variance estimation in real-time.

Abstract

Learning governing equations from data is central to understanding the behavior of physical systems across diverse scientific disciplines, including physics, biology, and engineering. The Sindy algorithm has proven effective in leveraging sparsity to identify concise models of nonlinear dynamical systems. In this paper, we extend sparsity-driven approaches to real-time learning by integrating a cornerstone algorithm from control theory -- the Kalman filter (KF). The resulting Sindy Kalman Filter (SKF) unifies both frameworks by treating unknown system parameters as state variables, enabling real-time inference of complex, time-varying nonlinear models unattainable by either method alone. Furthermore, SKF enhances KF parameter identification strategies, particularly via look-ahead error, significantly simplifying the estimation of sparsity levels, variance parameters, and switching instants. We validate SKF on a chaotic Lorenz system with drifting or switching parameters and demonstrate its effectiveness in the real-time identification of a sparse nonlinear aircraft model built from real flight data.