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A New Method in Estimating Vehicle Center of Gravity Position Parameters Based on Ackermann’s Steering

[+] Author Affiliations
Zitian Yu, Junmin Wang

Ohio State University, Columbus, OH

Paper No. DSCC2016-9674, pp. V002T31A001; 9 pages
doi:10.1115/DSCC2016-9674
From:
  • ASME 2016 Dynamic Systems and Control Conference
  • Volume 2: Mechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Combustion Engines; Modeling and Validation; Motion and Vibration Control Applications; Multi-Agent and Networked Systems; Path Planning and Motion Control; Robot Manipulators; Sensors and Actuators; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamic Controls; Vehicle Dynamics and Traffic Control
  • Minneapolis, Minnesota, USA, October 12–14, 2016
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5070-1
  • Copyright © 2016 by ASME

abstract

The determination of vehicle’s center of gravity position is an important but challenging task for control of advanced vehicles such as automated vehicles, especially under daily usage condition where the system configurations and payload condition may change. To address this problem, a new method is proposed in this paper to estimate the vehicle’s 3-dimensional center of gravity position parameters without relying on detailed suspension configuration parameters or lateral tire force models. In the estimation problem, the vehicle’s planar dynamic equations are synthesized together to reduce the number of unknown lateral tire forces, then the condition of Ackermann’s Steering Geometry can be found to eliminate the influence of the remaining unknown front wheel lateral tire forces. When the unknown tire forces are cancelled, the recursive least squares (RLS) regression technique is used to identify the 3-dimensional center of gravity position parameters. The vehicle model with the sprung mass modeled as an inverted pendulum is developed to assist the analysis and conversion of sensor measured signals. Simulations conducted in a high-fidelity CarSim® vehicle model have demonstrated the capability of this proposed method in estimating the vehicle’s center of gravity position parameters.

Copyright © 2016 by ASME

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