Wheel suspension models relying on differential algebraic equation solvers for simulations are computationally resourceful and often not hard real-time capable. In this work, a neural network approach is developed that is based on the NARX model. A multibody vehicle simulation in ADAMS CAR and a high-precision object-oriented SIMULINK-model are utilized to obtain adequate datasets for NN training and testing purposes. Different settings of the model are tried. The final NARX model, containing 5 hidden nodes, is trained in open- and closed-loop configuration by BAYESIAN Regularization. The generalization ability is proven in several different, driving-situation-based test instances. It is shown that the NN can model wheel carrier acceleration and spring / damper reaction force effectively while requiring significantly lower computation time than a DAE-based simulation.
Erstellung eines Radaufhängungsmodells mithilfe von neuronalen Netzen ; Neural Network-Based Wheel Suspension Modeling
2020-08-20
Theses
Electronic Resource
English
ddc:620 , Fakultät für Ingenieurwissenschaften » Maschinenbau und Verfahrenstechnik » Institut für Mechatronik und Systemdynamik , wheel suspension modeling -- double wishbone suspension -- vehicle dynamics -- real-time -- kinematic loop -- neural network modeling -- recurrent neural network -- NARX
British Library Conference Proceedings | 2022
|Automatische Schiffsidentifikation mit Neuronalen Netzen
SLUB | 1996
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