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This research addresses the scalability bottleneck in training neural network observers for safety-critical systems by introducing a two-stage framework that decouples accuracy optimization from global stability certification. By pre-training on sampled states before applying Linear Matrix Inequality fine-tuning, the method achieves formal provable stability guarantees while significantly reducing computational costs compared to direct semidefinite programming approaches. The work advances the intersection of machine learning and control theory, offering a practical pathway for deploying robust observers in complex dynamical systems like aircraft.
Learning Provable Neural Network Observer for Uncertain Dynamical Systems
arXiv:2609.30819v1 Announce Type: new Abstract: In many safety-critical applications, control of uncertain dynamical systems relies on observers that estimate states and external disturbances. Neural network observers can improve estimation accuracy, but certifying their Lyapunov stability via Linear Matrix Inequality (LMI) constraints leads to large-scale semidefinite programs (SDPs) that are difficult to solve for large networks. To overcome this scalability bottleneck, we propose a novel two-stage training framework for provably stable neural network observers. Our approach decouples the optimization into a point-guided Lyapunov pre-training phase, which rapidly achieves high estimation accuracy and local stability over sampled states, followed by an LMI fine-tuning phase that efficiently satisfies a strict global Lyapunov stability certificate. We provide formal theoretical guarantees for local stability radii and probabilistic coverage over a prescribed compact error-state domain under specified regularity and sampling assumptions. Experiments on nonlinear control benchmarks and X-29 aircraft ablations show that our LMI-certified neural network observers train significantly faster than direct LMI-based methods and generalize robustly across diverse systems, achieving improved tracking accuracy over a range of observer baselines. The code is available at https://github.com/Berry-Myon/LearningNeuralNetworkObserver.
Abstract:In many safety-critical applications, control of uncertain dynamical systems relies on observers that estimate states and external disturbances. Neural network observers can improve estimation accuracy, but certifying their Lyapunov stability via Linear Matrix Inequality (LMI) constraints leads to large-scale semidefinite programs (SDPs) that are difficult to solve for large networks. To overcome this scalability bottleneck, we propose a novel two-stage training framework for provably stable neural network observers. Our approach decouples the optimization into a point-guided Lyapunov pre-training phase, which rapidly achieves high estimation accuracy and local stability over sampled states, followed by an LMI fine-tuning phase that efficiently satisfies a strict global Lyapunov stability certificate. We provide formal theoretical guarantees for local stability radii and probabilistic coverage over a prescribed compact error-state domain under specified regularity and sampling assumptions. Experiments on nonlinear control benchmarks and X-29 aircraft ablations show that our LMI-certified neural network observers train significantly faster than direct LMI-based methods and generalize robustly across diverse systems, achieving improved tracking accuracy over a range of observer baselines. The code is available at this https URL.
Machine Learning (cs.LG); Optimization and Control (math.OC)
arXiv:2609.30819 [cs.LG]
(or arXiv:2609.30819v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.30819 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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