Name
Title | ||
|---|---|---|
Robust Multi-Model Longitudinal Tire-Force Estimation Scheme: Experimental Data Validation |
Abstract | ||
|---|---|---|
A longitudinal tire-road interaction force estimation scheme with improved accuracy and robustness is introduced in this article. In order to develop the computing algorithm, two ground vehicle models are associated in a multimodel estimation strategy and addressing the different driving modes (brake/acceleration). A bicycle model is considered to further evaluate the longitudinal force at the front and rear virtual tires, together with a hoverboard model which is used to compute the longitudinal force at the left and right virtual tires. Kalman Filter is later used to estimate the stochastic states and provide robustness while improving algorithm's precision. The performance of the proposed observers is validated with experimental data. |
Year | Venue | Field |
|---|---|---|
2017 | 2017 IEEE 20TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC) | Brake,Experimental data,Simulation,Robustness (computer science),Kalman filter,Acceleration,Engineering |
DocType | ISSN | Citations |
Conference | 2153-0009 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Angel G. Alatorre Vazquez | 1 | 0 | 0.34 |
| Alessandro Victorino | 2 | 10 | 2.25 |
| A. Charara | 3 | 69 | 14.17 |