State observer for diagnosis of dynamic behavior of vehicle in its environment

Abstract : Nowadays, a variety of advanced driving assistance systems are being developed by research centers and automobile manufactures. Stability control is an essential topic in the modern industrial automobile society. Driving safety is widely concerned in the passenger cars to prevent potential risks. More and more electronic active safety systems are fitted out as a standard option, such as Electronic Stability Control (ESC) and Traction Control System (TCS). These safety systems are efficient in helping the driver maintain control of the car and also are considered highly cost-effective. However, for the future development trend of these systems, a more complex and integrated control unit requires more information about the vehicle dynamics. Some fundamental parameters such as tire road forces and sideslip angle are effective in describing vehicle dynamics. Nevertheless, it is lacking an effective and low-cost sensor to measure directly. Therefore, this study presents amethod to estimate these parameters using observer technologies and low-cost sensors which are available on the passenger cars in real environment. In addition, these systems are designed for dealing with vehicle current state where danger situation has always occurred, the capacity of these systems is limited to minimize the effects. We were wondering whether shall we predict and effectively avoid a crash before it occurred. Therefore, this work is also addressed to develop a risk prediction method for proposing driver a safe speed to negotiate the upcoming curves. First, this dissertation develops a new approach to estimate the vertical load distribution in real environment. The influence of bank angle is considered in the phase of reconstruction of vehicle model. The vertical tire force on banked road is estimated by using Kalman filter. In order to estimate the lateral tire force, two nonlinear observers are addressed to solve the nonlinearity of vehicle model. The Extended Kalman filter is widely discussed in the previous literature, while we firstly use a Particle filter to estimate the vehicle dynamics parameters. Two different observer technologies are proposed and compared using the experimental data. Second, extending the consideration of road safety to the future instant. Prediction of vehicle dynamics parameters, evaluation of potential risk as well as establishment of advisory speed on curves are introduced to reduce the possibility of crash occurrence on curves. Last but not least, the real-time sampling and process system is presented, the estimator with EKF and PF has been developed as a real-time application. Experimental results are performed to demonstrate the performance of these integrated systems in real-time.
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Bin Wang. State observer for diagnosis of dynamic behavior of vehicle in its environment. Other [cs.OH]. Université de Technologie de Compiègne, 2013. English. ⟨NNT : 2013COMP2126⟩. ⟨tel-01016796⟩

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