Forward-Backward Extended DMD with an Asymptotic Stability Constraint
- Resource Type
- Working Paper
- Authors
- Lortie, Louis; Dahdah, Steven; Forbes, James Richard
- Source
- Subject
- Electrical Engineering and Systems Science - Systems and Control
- Language
This paper presents a data-driven method to identify an asymptotically stable Koopman system from noisy data. In particular, the proposed approach combines approximations of the system's forward- and backward-in-time dynamics to reduce bias caused by noisy data while enforcing asymptotic stability. A Koopman model of an inherently asymptotically stable system can be unstable due to noisy data and a poor choice of lifting functions. To prevent identifying an unstable model, the proposed approach imposes an asymptotic stability constraint on the Koopman model. The proposed method is formulated as a semidefinite program and its performance is compared to state-of-the-art methods with a simulated Duffing oscillator dataset and experimental soft robot dataset.
Comment: 20 pages, 7 figures