Conference
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ME – PhD Thesis Colloquium Continual Koopman Learning for Data-Driven Control of Nonlinear Systems
A physics-informed Koopman representation based on generalized momenta is introduced, yielding a linear control-affine model in lifted coordinates with known input structure that avoids the bilinear state – input coupling inherent in standard Koopman approaches, enabling improved prediction accuracy and tractable contr...
PhD student Mr. Rajpal Singh
· 0 citations
Jul 2026
Revisiting Certainty Equivalence: The Structural Coupling Between Estimation and Control in Underactuated Nonlinear Systems
The certainty equivalence (CE) principle underpins a wide range of control architectures by enabling the separation of estimation and control design. While this property holds for linear systems, its validity in nonlinear settings remains limited and often implicitly assumed. This paper revisits CE from a nonlinear per...
Daniel Engelsman, Itzik Klein
· arXiv.org · 0 citations
Preprint
Aug 2026
KOOPMAN-Luenberger Observer Design for Nonlinear Systems with Application to the Monitoring of a Latent Thermal Energy Storage
State estimation for nonlinear dynamical systems remains a fundamental challenge, particularly when measurements are sparse and internal states are inaccessible. This work presents a KOOPMAN-based Linear State Observer (KOOPMAN-LSO) design framework that enables linear observer synthesis for nonlinear systems through K...
Mustapha Habib, Dario Aguiar, Esther Kieseritzky et al.
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Preprint
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Bilinear Koopman-Based Robust Model Predictive Control for Unknown Nonlinear Systems via Contraction Metrics
A RMPC framework for unknown nonlinear systems with general nonlinear constraints based on data-driven bilinear Koopman realizations is proposed and robust satisfaction of the original nonlinear constraints is proved by the true closed-loop trajectory, recursive feasibility, and convergence to a neighborhood of the tar...
Yuki Higuchi, Kazuhiro Sato
· 1 citation
Preprint
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Spectral Distillation: From Nonlinear Dynamics to Linear State-Space Models
This work first learns an implicit spectral predictor using Observation Spectral Filtering using Observation Spectral Filtering, a convex method that competes with the best linear observer for the system, and applies spectral-to-LDS distillation to convert this predictor into an explicit recurrent linear dynamical syst...
Liane Galanti, Devan Shah, Shlomo Fortgang et al.
· 0 citations
Preprint
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Joint Identifiability and Conditioning in Finite-Horizon Continuous-Time Inverse LQR with Unknown Dynamics
Inverse Optimal Control (IOC) aims to infer the underlying cost functional of an agent from observations of its expert behavior. This paper studies the finite-horizon continuous-time inverse LQR problem from closed-loop state--input trajectories, where both the system matrices and the quadratic cost are unknown. The fi...
Meiling Yu, Yuan-Hua Ni, Lei Jiang
· 0 citations