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Research Papers

Simultaneous Estimation of Vehicle’s Center of Gravity and Inertial Parameters Based on Ackermann’s Steering Geometry

[+] Author and Article Information
Zitian Yu

Department of Mechanical
and Aerospace Engineering,
The Ohio State University,
Columbus, OH 43210
e-mail: yu.553@osu.edu

Junmin Wang

Fellow ASME
Department of Mechanical and
Aerospace Engineering,
The Ohio State University,
Columbus, OH 43210
e-mail: wang.1381@osu.edu

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received March 6, 2016; final manuscript received September 26, 2016; published online January 10, 2017. Assoc. Editor: Shankar Coimbatore Subramanian.

J. Dyn. Sys., Meas., Control 139(3), 031006 (Jan 10, 2017) (13 pages) Paper No: DS-16-1128; doi: 10.1115/1.4034946 History: Received March 06, 2016; Revised September 26, 2016

Onboard vehicle parameter estimation is an important procedure for advanced vehicle control tasks, especially for vehicles whose payload configurations vary in day-to-day use. This study presents a newly proposed estimation method based on the Ackermann’s steering geometry (ASG) that aims to estimate multiple vehicle's center of gravity (CG) position and inertial parameters at the same time. In this method, the vehicle planar motion equations are first synthesized into a form where only the lateral force of one front wheel and longitudinal forces appear. This way, the influence of uncertainties in the tire lateral force models is greatly reduced. Then, the condition of eliminating the remaining front wheel lateral force term can be derived, which is exactly the Ackermann’s steering geometry. When the influence of lateral tire force terms are eliminated, regression technique is applied to estimate the needed vehicle parameters. Vehicle’s suspension kinematics is also considered in the processing of dynamic signals. Unlike conventional methods in estimating vehicle’s payload related parameters, the new method requires neither lateral tire force model nor accurate suspension property parameters. Simulations in CarSim®-Simulink environment verified that the proposed method is capable of estimating vehicle parameters such as CG position and inertial parameters at the same time.

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References

Figures

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Fig. 1

A schematic illustration of the vehicle model

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Fig. 2

Top view of the vehicle model

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Fig. 3

Illustration of the Ackermann’s steering geometry

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Fig. 4

Driving torque example

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Fig. 5

ŵfr−m̂ curves for A-class hatchback, B-class hatchback, European-van, and E-class sedan (from left to right in the figure)

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Fig. 6

RLS results for the A-class hatchback and the E-class sedan models

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Fig. 7

RLS results for the B-class hatchback and the European-van models

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Fig. 8

Comparison of the corrected signals and the measured signals

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Fig. 9

Comparison between results of the default payload and the varied payload

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Fig. 10

Driving torque and active suspension spring force injection

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Fig. 11

Comparison between ŵfr−m̂ curves with or without active suspension

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Fig. 12

Comparison between RLS results with or without active suspension

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Fig. 13

Estimated yaw inertia as a function of point O planar position

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Fig. 14

ŵfr−m̂ curves when the bias from Ackermann’s steering geometry is varying

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Fig. 15

Estimation results when the bias from Ackermann’s steering geometry is varying

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Fig. 16

ŵfr−m̂ curves when front-right RWSA bias is varying for the left maneuvers

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Fig. 17

Estimation results when front-right RWSA bias is varying for the left maneuvers

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