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Original Article
Vehicle Lateral Motion State Estimation Based on Adaptive Cubature Kalman Filter
Zhen-yu ZHANG
master at College of Mechanical and Vehicle Engineering, Hunan University, China
Corresponding Author
ORCiD: https://orcid.org/0000-0003-4292-5223
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Accurate and reliable vehicle state information is very significant to the vehicle lateral stability control, while it is hard to get information such as body sideslip angle and lateral tire forces due to lack the nonlinearity of the tire. This paper presents a new combined method (adaptive cubature Kalman filter(ACKF) and adaptive proportion integral observer(APIO)) to estimate body sideslip angle, yaw rate and lateral tire force for vehicle system. Firstly, based on a four-wheel vehicle dynamics model, (ACKF) is used to estimate body sideslip angle and yaw rate with considering the nonlinear lateral tire force stage. Due to system nonlinearities and un-modeled dynamics, APIO is used to improve the estimated body sideslip angle by utilizing the estimated yaw rate and vehicle lateral speed. Then, ACKF is used to estimate the front and rear lateral tire forces based on the one-order tire dynamics model. By utilizing the partition coefficient calculated by the vertical force model, the front and rear lateral tire forces are further distributed to left and right wheels. For comparison, estimation model based on extended Kalman filter(EKF) is built and investigated. Simulation using Matlab/Simulink-CarSim and car test verifies the effectiveness of the proposed method.
Keywords
State estimation, Vehicle system, Adaptive cubature Kalman filter, Adaptive proportion integral observer, Partition coefficient
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Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 15 Dec, 2020
No community comments so far
Reviewer #1 agreed
On 13 Jan, 2021
Reviewers invited
Invitations sent on 31 Dec, 2020
Editor assigned
On 08 Dec, 2020
Submission checks complete
On 08 Dec, 2020
Editor invited
On 08 Dec, 2020
First submitted
On 07 Dec, 2020
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Subject Areas
Mechanical Engineering
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This preprint is under consideration at Chinese Journal of Mechanical Engineering. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Original Article
Vehicle Lateral Motion State Estimation Based on Adaptive Cubature Kalman Filter
Zhen-yu ZHANG
master at College of Mechanical and Vehicle Engineering, Hunan University, China
Corresponding Author
ORCiD: https://orcid.org/0000-0003-4292-5223
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 15 Dec, 2020
No community comments so far
Reviewer #1 agreed
On 13 Jan, 2021
Reviewers invited
Invitations sent on 31 Dec, 2020
Editor assigned
On 08 Dec, 2020
Submission checks complete
On 08 Dec, 2020
Editor invited
On 08 Dec, 2020
First submitted
On 07 Dec, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Mechanical Engineering
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Accurate and reliable vehicle state information is very significant to the vehicle lateral stability control, while it is hard to get information such as body sideslip angle and lateral tire forces due to lack the nonlinearity of the tire. This paper presents a new combined method (adaptive cubature Kalman filter(ACKF) and adaptive proportion integral observer(APIO)) to estimate body sideslip angle, yaw rate and lateral tire force for vehicle system. Firstly, based on a four-wheel vehicle dynamics model, (ACKF) is used to estimate body sideslip angle and yaw rate with considering the nonlinear lateral tire force stage. Due to system nonlinearities and un-modeled dynamics, APIO is used to improve the estimated body sideslip angle by utilizing the estimated yaw rate and vehicle lateral speed. Then, ACKF is used to estimate the front and rear lateral tire forces based on the one-order tire dynamics model. By utilizing the partition coefficient calculated by the vertical force model, the front and rear lateral tire forces are further distributed to left and right wheels. For comparison, estimation model based on extended Kalman filter(EKF) is built and investigated. Simulation using Matlab/Simulink-CarSim and car test verifies the effectiveness of the proposed method.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.