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Vehicle Parameter Identification and...
~
Hong, Sanghyun.
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Vehicle Parameter Identification and its Use in Control for Safe Path Following.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Vehicle Parameter Identification and its Use in Control for Safe Path Following./
作者:
Hong, Sanghyun.
面頁冊數:
116 p.
附註:
Source: Dissertation Abstracts International, Volume: 76-02(E), Section: B.
Contained By:
Dissertation Abstracts International76-02B(E).
標題:
Mechanical engineering. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3640450
ISBN:
9781321259124
Vehicle Parameter Identification and its Use in Control for Safe Path Following.
Hong, Sanghyun.
Vehicle Parameter Identification and its Use in Control for Safe Path Following.
- 116 p.
Source: Dissertation Abstracts International, Volume: 76-02(E), Section: B.
Thesis (Ph.D.)--University of California, Berkeley, 2014.
This item must not be sold to any third party vendors.
This thesis develops vehicle parameter identification algorithms, and applies identified parameters to a controller designed for safe path following.
ISBN: 9781321259124Subjects--Topical Terms:
649730
Mechanical engineering.
Vehicle Parameter Identification and its Use in Control for Safe Path Following.
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A tire-road friction coefficient is estimated using an in-tire accelerometer to measure acceleration signals directly from the tires. The proposed algorithm first determines a tire-road contact patch with a radial acceleration profile. The estimation algorithm is based on tire lateral deflections obtained from lateral acceleration measurements only inside the contact patch. A new model is derived for the lateral deflection profiles, which provides robustness to orientation-variation of the accelerometer body frame during tire rotation.
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A novel algorithm is developed to identify three inertial parameters: sprung mass, yaw moment of inertia, and longitudinal position of the center of gravity. A correlation of inertial parameters is derived and is used for the identification algorithm. Inertial parameters and vehicle states are simultaneously estimated with a dual unscented Kalman filter based on a nonlinear vehicle model. In order to activate and de-activate different modes of the proposed algorithm, a local observability analysis is performed with the nonlinear vehicle model. The performance and robustness of the proposed approach are demonstrated with extensive CarSim simulations and experimental tests on a flat road with a constant tire-road friction coefficient.
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Following a curved road can be dangerous if autonomous vehicles do not take roll motion into consideration. A control algorithm is designed to prevent a dangerous vehicle state induced by roll motion while following a curved road. Roll motion is suppressed throughout cornering with model predictive control. A four-wheel nonlinear vehicle model with roll dynamics and a tire brush model are utilized for the prediction of the vehicle state. An optimal balance in the trade-off between vehicle speed and roll motion is achieved with full braking as a control actuator. Identified vehicle inertial parameters are incorporated into the designed controller. CarSim simulations illustrate the performance of the proposed controller and the effect of the vehicle parameter estimator.
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