Journal
NEURAL COMPUTING & APPLICATIONS
Volume 30, Issue 11, Pages 3445-3465Publisher
SPRINGER LONDON LTD
DOI: 10.1007/s00521-017-2932-9
Keywords
Tire force estimation; Grip potential estimation; Neural Networks; Hybrid observer
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Funding
- European Union's Horizon 2020 research and innovation program under the Marie Skodowska-Curie Grant [675999]
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This paper presents a novel hybrid observer structure to estimate the lateral tire forces and road grip potential without using any tire-road friction model. The observer consists of an Extended Kalman Filter structure, which incorporates the available prior knowledge about the vehicle dynamics, a feedforward Neural Network structure, which is used to estimate the highly nonlinear tire behavior, and a Recursive Least Squares block, which predicts the road grip potential. The proposed observer was evaluated under a wide range of aggressive maneuvers and different road grip conditions using a validated vehicle model, validated tire model, and sensor models in the simulation environment IPG CarMaker((R)). The results confirm its good and robust performance.
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