Continuous–Discrete Time Neural Network Observer for Nonlinear Dynamic Systems Application to Vehicle Systems
| Autor: | Ghani, Hasan Abdl, Dam, Quang Truc, Laghmara, Hind, Ahmed-Ali, Sofiane, Ainouz, Samia, Gao, Xing |
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| Přispěvatelé: | Institut de Recherche en Systèmes Electroniques Embarqués (IRSEEM), Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Normandie Université (NU)-École Supérieure d’Ingénieurs en Génie Électrique (ESIGELEC), Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes (LITIS), Université Le Havre Normandie (ULH), Normandie Université (NU)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA), Informatique, BioInformatique, Systèmes Complexes (IBISC), Université d'Évry-Val-d'Essonne (UEVE)-Université Paris-Saclay |
| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | 22nd World Congress of the International Federation of Automatic Control (World Congress IFAC 2023) 22nd World Congress of the International Federation of Automatic Control (World Congress IFAC 2023), Jul 2023, Yokohama, Japan |
| Popis: | International audience; This paper proposes a novel continuous-discrete (sampled data) time neural network (NSNN) observer for nonlinear systems. It can therefore be applied to systems with a high degree of non-linearity with no prior knowledge of the system dynamics. The proposed observer is a three-layer feedforward neural network that has been intensively trained using the error backpropagation learning algorithm, which includes an e-modification term to ensure robustness of the observer. A structure of the output predictor with a corrective term is added in the structure of the NN observer to overcome the problem of discrete time measurement. Simulations using MATLAB and CarSim are illustrated to demonstrate the performance of the proposed state observer strategy to reconstruct the state variables and parameters of a vehicle system. |
| Databáze: | OpenAIRE |
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