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The aim of the presented work is to estimate the vertical forces of heavy vehicle and identify the unknown dynamic parameters using sliding mode observers approach. This observation needs a good knowledge of some dynamic parameters such as damping coefficient, spring stiffness…etc. We propose in this paper, to identify some of these parameters which are, in practice very difficult to obtain and to measure. This identification will improve the quality of vertical forces estimation. Some experimental results are presented in order to show the quality of the estimation and identification. These estimation results are then compared to the measures coming from an instrumented tractor.

In this paper, we are interested in developing a robust tire-force estimator for heavy duty vehicles. We use a combined model of the articulated vehicle: a yaw plane model for the chassis motion and a vertical plane model for the axles. In the proposed method, we make use of the on-board available sensors to which low-cost sensors are added. In order to optimize the sensors configuration, a robust exact differentiator is used in order to obtain accelerations from the measured velocities. Once the differentiation is obtained, the model is inverted to determine the unknown input forces. The approach is validated by comparing the estimation results to those given by the software simulator prosper .

The aim of this paper is to elaborate a reliable rollover risk evaluator of a heavy vehicle in order to supply reliable information to the warning or control system. This is done by calculating the probability of rollover risk. The evaluation is based on the load transfer ratio between the right and left sides. Sensitivity analysis is given to find the most influential parameters on the risk. Then, probabilistic modeling of these parameters is obtained by using Maximum Entropy Principle. After that, Unscented Kalman Filter is developed to estimate all dynamic states and to identify the height center of gravity that cannot be directly measured. Finally, the obtained probability laws are used in a reliability method to approximate the probability of rollover risk.

In this paper, we present an adaptive observer to estimate the unknown parameters of a vehicle. The system unknown inputs, representing the road profile variations, are estimated using sliding mode observers. First, we present some results related to the validation of a full car modelization, by means of comparisons between simulations results and experimental measurements (coming from a Peugeot 406 as a test car). Because, we don't know exactly pneumatic parameters and because these parameters can be changed, an other sliding mode observer is developed to estimate the longitudinal forces (which depend on these parameters) acting on the wheels. The estimated Road Profile is compared to the measured one coming from the LPA (longitudinal profile analyser ) in order to test the robustness of our approach.