Towards gain tuning for numerical KKL observers
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Abstract (arXiv)
This paper presents a first step towards tuning observers for general nonlinear systems. Relying on recent results around Kazantzis-Kravaris/Luenberger (KKL) observers, we propose an empirical criterion to guide the calibration of the observer, by trading off transient performance and sensitivity to measurement noise. We parametrize the gain matrix and evaluate this criterion over a family of observers for different parameter values. We then use neural networks to learn the mapping between the observer and the nonlinear system, and present a novel method to sample the state-space efficiently for nonlinear regression. We illustrate the merits of this approach in numerical simulations.
Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Apr 2022.
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Citing papers per year: 2022: 4; 2023: 4; 2024: 0; 2025: 3; 2026: 3.
14 papers.
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- 2022Learning-based Design of Luenberger Observers for Autonomous Nonlinear Systemscited by 11
- 2022Recognition Models to Learn Dynamics from Partial Observations with Neural ODEscited by 8
- 2022Learning Robust State Observers using Neural ODEs (longer version)cited by 6
- 2025KKL Observer Synthesis for Nonlinear Systems via Physics-Informed Learningcited by 4
- 2022Learning Reduced Nonlinear State-Space Models: an Output-Error Based Canonical Approachcited by 1
- 2023Data-Driven Observability Analysis for Nonlinear Stochastic Systemscited by 1
- 2023Sensor Fault Detection and Isolation in Autonomous Nonlinear Systems Using Neural Network-Based Observerscited by 1
- 2023Learning Hybrid Dynamics Models With Simulator-Informed Latent Statescited by 1
- 2023Adaptive Meta-Learning-Based KKL Observer Design for Nonlinear Dynamical Systemscited by 1
- 2025PINN-Obs: Physics-Informed Neural Network-Based Observer for Nonlinear Dynamical Systemscited by 1
- 2025Designing Robust Software Sensors for Nonlinear Systems via Neural Networks and Adaptive Sliding Mode Controlcited by 0
- 2026HyperKKL: Enabling Non-Autonomous State Estimation through Dynamic Weight Conditioningcited by 0
- 2026HyperKKL: Learning KKL Observers for Non-Autonomous Nonlinear Systems via Hypernetwork-Based Input Conditioning31 Mar 2026
- 2026Verifiable Error Bounds for Physics-Informed Neural KKL Observers20 Mar 2026
- 2026HyperKKL: Enabling Non-Autonomous State Estimation through Dynamic Weight Conditioning26 Feb 2026
- 2025Designing Robust Software Sensors for Nonlinear Systems via Neural Networks and Adaptive Sliding Mode Control9 Jul 2025
- 2025PINN-Obs: Physics-Informed Neural Network-Based Observer for Nonlinear Dynamical Systems9 Jul 2025
- 2025KKL Observer Synthesis for Nonlinear Systems via Physics-Informed Learning20 Jan 2025
- 2023Adaptive Meta-Learning-Based KKL Observer Design for Nonlinear Dynamical Systems30 Oct 2023
- 2023Learning Hybrid Dynamics Models With Simulator-Informed Latent States6 Sep 2023
- 2023Sensor Fault Detection and Isolation in Autonomous Nonlinear Systems Using Neural Network-Based Observers18 Apr 2023
- 2023Data-Driven Observability Analysis for Nonlinear Stochastic Systems23 Feb 2023
- 2022Learning Robust State Observers using Neural ODEs (longer version)1 Dec 2022
- 2022Learning-based Design of Luenberger Observers for Autonomous Nonlinear Systems4 Oct 2022
- 2022Learning-based Design of Luenberger Observers for Autonomous Nonlinear Systemscited by 11
- 2022Recognition Models to Learn Dynamics from Partial Observations with Neural ODEscited by 8
- 2022Learning Robust State Observers using Neural ODEs (longer version)cited by 6
- 2025KKL Observer Synthesis for Nonlinear Systems via Physics-Informed Learningcited by 4
- 2022Learning Reduced Nonlinear State-Space Models: an Output-Error Based Canonical Approachcited by 1
- 2023Data-Driven Observability Analysis for Nonlinear Stochastic Systemscited by 1
- 2023Sensor Fault Detection and Isolation in Autonomous Nonlinear Systems Using Neural Network-Based Observerscited by 1
- 2023Learning Hybrid Dynamics Models With Simulator-Informed Latent Statescited by 1
- 2023Adaptive Meta-Learning-Based KKL Observer Design for Nonlinear Dynamical Systemscited by 1
- 2025PINN-Obs: Physics-Informed Neural Network-Based Observer for Nonlinear Dynamical Systemscited by 1
- 2025Designing Robust Software Sensors for Nonlinear Systems via Neural Networks and Adaptive Sliding Mode Controlcited by 0
- 2026HyperKKL: Enabling Non-Autonomous State Estimation through Dynamic Weight Conditioningcited by 0
12 most cited of 14 All 14 (JSON)
- 2026HyperKKL: Learning KKL Observers for Non-Autonomous Nonlinear Systems via Hypernetwork-Based Input Conditioning31 Mar 2026
- 2026Verifiable Error Bounds for Physics-Informed Neural KKL Observers20 Mar 2026
- 2026HyperKKL: Enabling Non-Autonomous State Estimation through Dynamic Weight Conditioning26 Feb 2026
- 2025Designing Robust Software Sensors for Nonlinear Systems via Neural Networks and Adaptive Sliding Mode Control9 Jul 2025
- 2025PINN-Obs: Physics-Informed Neural Network-Based Observer for Nonlinear Dynamical Systems9 Jul 2025
- 2025KKL Observer Synthesis for Nonlinear Systems via Physics-Informed Learning20 Jan 2025
- 2023Adaptive Meta-Learning-Based KKL Observer Design for Nonlinear Dynamical Systems30 Oct 2023
- 2023Learning Hybrid Dynamics Models With Simulator-Informed Latent States6 Sep 2023
- 2023Sensor Fault Detection and Isolation in Autonomous Nonlinear Systems Using Neural Network-Based Observers18 Apr 2023
- 2023Data-Driven Observability Analysis for Nonlinear Stochastic Systems23 Feb 2023
- 2022Learning Robust State Observers using Neural ODEs (longer version)1 Dec 2022
- 2022Learning-based Design of Luenberger Observers for Autonomous Nonlinear Systems4 Oct 2022
12 newest of 14 All 14 (JSON)