PINN-Obs: Physics-Informed Neural Network-Based Observer for Nonlinear Dynamical Systems

• URL: http://arxiv.org/abs/2507.06712v1
• Date: Wed, 09 Jul 2025 10:09:45 GMT
• Title: PINN-Obs: Physics-Informed Neural Network-Based Observer for Nonlinear Dynamical Systems
• Authors: Ayoub Farkane, Mohamed Boutayeb, Mustapha Oudani, Mounir Ghogho,
• Abstract summary: This paper introduces a novel Adaptive Physics-Informed Neural Network-based Observer (PINN-Obs) for accurate state estimation in nonlinear systems.<n>Unlike traditional model-based observers, which require explicit system transformations or linearization, the proposed framework directly integrates system dynamics and sensor data into a physics-informed learning process.
• Score: 2.884893167166808
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: State estimation for nonlinear dynamical systems is a critical challenge in control and engineering applications, particularly when only partial and noisy measurements are available. This paper introduces a novel Adaptive Physics-Informed Neural Network-based Observer (PINN-Obs) for accurate state estimation in nonlinear systems. Unlike traditional model-based observers, which require explicit system transformations or linearization, the proposed framework directly integrates system dynamics and sensor data into a physics-informed learning process. The observer adaptively learns an optimal gain matrix, ensuring convergence of the estimated states to the true system states. A rigorous theoretical analysis establishes formal convergence guarantees, demonstrating that the proposed approach achieves uniform error minimization under mild observability conditions. The effectiveness of PINN-Obs is validated through extensive numerical simulations on diverse nonlinear systems, including an induction motor model, a satellite motion system, and benchmark academic examples. Comparative experimental studies against existing observer designs highlight its superior accuracy, robustness, and adaptability.

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