Road Grades and Tire Forces Estimation Using Two-Stage Extended Kalman Filter in a Delayed Interconnected Cascade Structure

被引：0
|
作者：
Cordeiro, R. A. [1 ]
Ribeiro, A. M. [2 ]
Azinheira, J. R. [2 ]
Victorino, A. C. [3 ]
Ferreira, P. A. V. [1 ]
de Paiva, E. C. [4 ]
Bueno, S. S. [5 ]
机构：
[1] Univ Estadual Campinas, Sch Elect & Comp Engn, Campinas, SP, Brazil
[2] Univ Lisbon, Inst Super Tecn, Lisbon, Portugal
[3] Sorbonne Univ, Univ Technol Compiegne, CNRS, Heudiasyc,UMR 7253,CS 60 319, F-60203 Compiegne, France
[4] Univ Estadual Campinas, Sch Mech Engn, Campinas, SP, Brazil
[5] Ctr Informat Technol Renato Archer, Div Robot & Comp Vis, Campinas, SP, Brazil
来源：
2017 28TH IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV 2017) | 2017年
基金：
巴西圣保罗研究基金会;
关键词：
VEHICLE;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Intelligent vehicles sense their dynamics and the environment to make proper decisions. Some of this information are hard to be measured or need expensive sensors. This paper addresses the estimation of road grade angles, along with tire-ground interaction forces, in a delayed interconnected cascade observer structure. A new approach using a Two-Stage Extended Kalman Filter is proposed, allowing a robust simultaneous estimation of the slow and fast dynamics variables. Experimental data is used to validate the estimator.
引用
收藏
页码：115 / 120
页数：6
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