This paper presents a modular observer structure to estimate the tire-road forces robustly, avoiding the use of any particular tire model, and using standard signals available in current passenger vehicles. The observer consists of a feedforward longitudinal force estimation block and a hybrid lateral force estimation module formed by an Extended Kalman Filter and a Static Neural Network Structure. Road grade and bank angle are estimated using sensor fusion, where a Fuzzy Logic controller combines the outputs from a Euler Kinematic model and a Recursive Least Squares block. The proposed observer is tested and verified using the simulation software IPG CarMaker® under realistic driving situations. Lastly, the feasibility of the longitudinal force block is proved with real-time experiments.
|Number of pages||7|
|Publication status||Published - 1 Jul 2017|
|Event||The 20th World Congress of the International Federation of Automatic Control - Toulouse, France|
Duration: 9 Jul 2017 → 14 Jul 2017
|Conference||The 20th World Congress of the International Federation of Automatic Control|
|Abbreviated title||IFAC 2017|
|Period||9/07/17 → 14/07/17|
- Neural Networks
- tire force estimation
- bank angle
- road grade
Acosta, M., Alatorre, A., Kanarachos, S., Victorino, A., & Charara, A. (2017). Estimation of tire forces, road grade, and road bank angle using tire model-less approaches and Fuzzy Logic. 14836-14842. Paper presented at The 20th World Congress of the International Federation of Automatic Control, Toulouse, France.