2019
DOI: 10.1177/1077546319890011
|View full text |Cite
|
Sign up to set email alerts
|

Terrain estimation via vehicle vibration measurement and cubature Kalman filtering

Abstract: The extent of vibrations experienced by a vehicle driving over natural terrain defines its ride quality. Generally, surface irregularities, ranging from single discontinuities to random variations of the elevation profile, act as a major source of excitation that induces vibrations in the vehicle body through the tire-soil interaction and suspension system. Therefore, the ride response of off-road-vehicles is tightly connected with the ground properties. The objective of this research is to develop a model-bas… Show more

Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
22
0

Year Published

2020
2020
2023
2023

Publication Types

Select...
9

Relationship

2
7

Authors

Journals

citations
Cited by 17 publications
(22 citation statements)
references
References 29 publications
0
22
0
Order By: Relevance
“…where f ð⋅Þ and hð⋅Þ are the nonlinear functions. In the CKF, the time updating process is derived by [27,28]:…”
Section: Models For Gnss/ins Integrated Systemsmentioning
confidence: 99%
“…where f ð⋅Þ and hð⋅Þ are the nonlinear functions. In the CKF, the time updating process is derived by [27,28]:…”
Section: Models For Gnss/ins Integrated Systemsmentioning
confidence: 99%
“…This work provides an improved algorithm of the work by Yuankai Li [57]. The real-time terrain estimation by two-layer process improves the performance of Extended Kalman filter [58,59] and Recursive Gaussian Newton algorithm [60]. The algorithm provides a switching property to select between filters.…”
Section: Recent Developments In Terrain Parameter Estimation Of Wheeled Robotsmentioning
confidence: 99%
“…e commonly used vehicle state observers include sliding mode observer, robust observer, fuzzy observer, and so on. e nonlinear Kalman filter has been popularly utilized to address the state estimation problems [22], such as the extended Kalman filter (EKF) [23][24][25], unscented Kalman filter (UKF) [26][27][28][29], and their ramification [30,31]. Compared with the observer-based approach, the UKF has good robustness against sensor errors [20].…”
Section: Introductionmentioning
confidence: 99%