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[2602.22630] HyperKKL: Enabling Non-Autonomous State Estimation through Dynamic Weight Conditioning
Summary
The paper presents HyperKKL, a novel approach for designing KKL observers for non-autonomous nonlinear systems, leveraging hypernetwork architecture to enhance state estimation.
Why It Matters
This research addresses a significant gap in existing methodologies for state estimation in non-autonomous systems, which are often inadequately managed by traditional KKL observers. By introducing HyperKKL, the authors provide a more efficient method that could lead to advancements in control systems and machine learning applications.
Key Takeaways
- HyperKKL utilizes a hypernetwork architecture for dynamic weight conditioning.
- The approach improves state estimation for non-autonomous nonlinear systems.
- It outperforms traditional KKL observers by eliminating the need for retraining.
- The method is validated through simulations on benchmark systems like Lorenz and Rössler.
- HyperKKL represents a significant advancement in the application of machine learning to control systems.
Electrical Engineering and Systems Science > Systems and Control arXiv:2602.22630 (eess) [Submitted on 26 Feb 2026] Title:HyperKKL: Enabling Non-Autonomous State Estimation through Dynamic Weight Conditioning Authors:Yahia Salaheldin Shaaban, Salem Lahlou, Abdelrahman Sayed Sayed View a PDF of the paper titled HyperKKL: Enabling Non-Autonomous State Estimation through Dynamic Weight Conditioning, by Yahia Salaheldin Shaaban and 2 other authors View PDF Abstract:This paper proposes HyperKKL, a novel learning approach for designing Kazantzis-Kravaris/Luenberger (KKL) observers for non-autonomous nonlinear systems. While KKL observers offer a rigorous theoretical framework by immersing nonlinear dynamics into a stable linear latent space, its practical realization relies on solving Partial Differential Equations (PDE) that are analytically intractable. Current existing learning-based approximations of the KKL observer are mostly designed for autonomous systems, failing to generalize to driven dynamics without expensive retraining or online gradient updates. HyperKKL addresses this by employing a hypernetwork architecture that encodes the exogenous input signal to instantaneously generate the parameters of the KKL observer, effectively learning a family of immersion maps parameterized by the external drive. We rigorously evaluate this approach against a curriculum learning strategy that attempts to generalize from autonomous regimes via training heuristics alone. The novel app...