loading page

Data-Driven State Observation for Nonlinear Systems based on Online Learning
  • Wentao Tang
Wentao Tang
NC State University

Corresponding Author:[email protected]

Author Profile


This paper considers the problem of state observation for nonlinear dynamics. While model-based observer synthesis is difficult due to the need of solving partial differential equations, this work proposes an efficient model-free, data-driven approach based on online learning. Specifically, by considering the observer dynamics as a Chen-Fliess series, the estimation of its coefficients has a least squares formulation. Since the series converges only locally, the coefficients are recursively updated, resulting in an online optimization scheme driven by instantaneous gradients. When the state trajectories are available, the online least squares guarantees an ultimate upper bound of average observation error proportional to the average variation of states. In the realistic situations where the states cannot be measured, the immersed linear dynamics based on the Kazantzis-Kravaris/Luenberger structure is assigned, followed by online kernel principal component analysis for dimensionality reduction. The proposed approach is demonstrated by a limit cycle dynamics and a chaotic system.
01 May 2023Submitted to AIChE Journal
10 May 2023Submission Checks Completed
10 May 2023Assigned to Editor
10 May 2023Review(s) Completed, Editorial Evaluation Pending
11 May 2023Reviewer(s) Assigned
04 Jun 2023Editorial Decision: Revise Major
04 Jul 20231st Revision Received
14 Jul 2023Submission Checks Completed
14 Jul 2023Assigned to Editor
14 Jul 2023Review(s) Completed, Editorial Evaluation Pending
15 Jul 2023Reviewer(s) Assigned
10 Aug 2023Editorial Decision: Accept