In order to improve vehicle's active safety systems accurate knowledge about the vehicle's driving stability is necessary. Especially the exact determination of the side-slip angle can be of great importance, since it has major potential for improving current control algorithms. Therefore, a modelbased methodology for online estimation of vehicle's lateral dynamics is presented, while generalizations of the Kalman Filter algorithm, the Extended and Unscented Kalman Filters are used due to the highly non-linear model behavior.The results of the introduced methodologies are presented for two different driving maneuvers and validated comparing to measurements taken with a VW Golf GTI. Furthermore, a qualitative comparison between Extended and Unscented Kalman Filter is realized.

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This paper discusses flatness-based control approaches to reduce oscillations on the load side of mechanical motion systems. Choosing a suitable trajectory is one essential step in implementing a flatness-based control. Existing approaches use slow polynomials or mathematically complex Gevrey-functions to achieve the necessary differentiability. Our approach combines the oscillation reduction of flatness-based control and the dynamic and simplicity of higher order Scurve trajectories generated by a convolution based algorithm. The performance and robustness of the presented methods is experimentally validated with a linear flexible motion system.978-1-4799-7800-7/15/$31.00 ©2015 IEEE

Automotive research and development passed through a vast evolution during past decades. Many passive and active driver assistance systems were developed, increasing the passengers' safety and comfort. This ongoing process is a main focus in current research and offers great potential for further systems, especially focusing on the task of autonomous and cooperative driving in the future. For that reason, information about the current stability in terms of dynamic behavior and vehicle environment are necessary for the systems to perform properly. Thus, model-based online state and parameter estimation have become important throughout the last years using a detailed vehicle model and standard sensors, gathering this information. In this chapter, state and parameter estimation in vehicle dynamics utilizing the unscented Kalman filter is presented. The estimation runs in real time based on a detailed vehicle model and standard measurements taken within the car. The results are validated using a Volkswagen Golf GTE Plug-In Hybrid for various dynamic test maneuvers and a Genesys Automotive Dynamic Motion Analyzer (ADMA) measurement unit for highprecision measurements of the vehicle's states. Online parameter estimation is shown for friction coefficient estimation performing maneuvers on different road surfaces.

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