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State and parameter estimation for v...

Ryu, Jihan.

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State and parameter estimation for vehicle dynamics control using GPS.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	State and parameter estimation for vehicle dynamics control using GPS./
作者:	Ryu, Jihan.
面頁冊數:	111 p.
附註:	Source: Dissertation Abstracts International, Volume: 65-11, Section: B, page: 6015.
Contained By:	Dissertation Abstracts International65-11B.
標題:	Engineering, Mechanical. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3153205
ISBN:	0496136194

State and parameter estimation for vehicle dynamics control using GPS.
Ryu, Jihan.

State and parameter estimation for vehicle dynamics control using GPS. - 111 p.

Source: Dissertation Abstracts International, Volume: 65-11, Section: B, page: 6015.

Thesis (Ph.D.)--Stanford University, 2005.

Many types of vehicle control systems can conceivably be developed to help drivers maintain stability, avoid roll-over, and customize handling characteristics. A lack of state and parameter information, however, presents a major obstacle. This dissertation presents state and parameter estimation methods using the Global Positioning System (GPS) for vehicle dynamics control. Several key vehicle states---sideslip angle, longitudinal velocity, roll and grade---can be estimated by combining automotive grade inertial sensors with a GPS receiver. Kinematic estimators that are independent of uncertain vehicle parameters integrate the inertial sensors with GPS to provide high update estimates of the vehicle states and the sensor biases. With a two-antenna GPS system, the effects of road grade and vehicle roll can be compensated to improve the accuracy of the vehicle state and sensor bias estimates. In addition, calibration procedures for the sensitivity and cross-coupling of inertial sensors are provided to reduce measurement error further.

ISBN: 0496136194Subjects--Topical Terms:

783786
Engineering, Mechanical.

State and parameter estimation for vehicle dynamics control using GPS.
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245 1 0 $a State and parameter estimation for vehicle dynamics control using GPS.
300 $a 111 p.
500 $a Source: Dissertation Abstracts International, Volume: 65-11, Section: B, page: 6015.
500 $a Adviser: J. Christian Gerdes.
502 $a Thesis (Ph.D.)--Stanford University, 2005.
520 $a Many types of vehicle control systems can conceivably be developed to help drivers maintain stability, avoid roll-over, and customize handling characteristics. A lack of state and parameter information, however, presents a major obstacle. This dissertation presents state and parameter estimation methods using the Global Positioning System (GPS) for vehicle dynamics control. Several key vehicle states---sideslip angle, longitudinal velocity, roll and grade---can be estimated by combining automotive grade inertial sensors with a GPS receiver. Kinematic estimators that are independent of uncertain vehicle parameters integrate the inertial sensors with GPS to provide high update estimates of the vehicle states and the sensor biases. With a two-antenna GPS system, the effects of road grade and vehicle roll can be compensated to improve the accuracy of the vehicle state and sensor bias estimates. In addition, calibration procedures for the sensitivity and cross-coupling of inertial sensors are provided to reduce measurement error further.
520 $a To verify the estimation results, this dissertation shows that the state estimates match calibrated models and statistical predictions. Since the proposed estimation scheme is based on a cascade estimator structure, the convergence of the cascade estimator structure is also proven. As an application, the estimated vehicle states are used to modify a vehicle's handling characteristics through a full state feedback controller. This shows that the estimated states are accurate and clean enough to be used in vehicle dynamics control systems without additional filtering.
520 $a The dissertation then examines parameter estimation for vehicle dynamic models. Several important vehicle parameters such as tire cornering stiffness, understeer gradient, and roll stiffness, can be estimated using the estimated vehicle states. Experimental results show that the parameter estimates from proposed methods converge to the known values. Finally, the dissertation presents a new method for identifying road bank and suspension roll separately using a disturbance observer and a vehicle dynamic model. Experimental results verify that the estimation scheme gives separate estimates of the suspension roll and road bank angles. The results of this work can improve the performance of stability control systems and enable a number of future systems.
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