Learned Lifted Linearization Applied to Unstable Dynamic Systems Enabled by Koopman Direct Encoding

Abstract

This paper presents a Koopman lifting linearization method that is applicable to nonlinear dynamical systems having both stable and unstable regions. It is known that DMD and other standard data-driven methods face a fundamental difficulty in constructing a Koopman model when applied to unstable systems. Here we solve the problem by incorporating knowledge about a nonlinear state equation with a learning method for finding an effective set of observables. In a lifted space, stable and unstable regions are separated into independent subspaces. Based on this property, we propose to find effective observables through neural net training where training data are separated into stable and unstable trajectories. The resultant learned observables are used for constructing a linear state transition matrix using method known as Direct Encoding, which transforms the nonlinear state equation to a state transition matrix through inner product computations with the observables. The proposed method shows a dramatic improvement over existing DMD and data-driven methods.

arXiv comment6 pages, 5 figures, Accepted to LCSS, https://ieeexplore.ieee.org/document/9998050

References in corpus (7)

• A Data-Driven Approximation of the Koopman Operator: Extending Dynamic Mode Decomposition
• Deep learning for universal linear embeddings of nonlinear dynamics
• Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control
• Modern Koopman Theory for Dynamical Systems
• Learning Deep Neural Network Representations for Koopman Operators of Nonlinear Dynamical Systems
• Learning Stable Models for Prediction and Control
• Lie group valued Koopman eigenfunctions