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Institute
This part begins with an introduction to Multibody Systems (MBS). It presents the elements of MBS and discusses different modeling aspects. Then, different methods to generate the equations of motion are presented. Solvers for ordinary differential equations (ODE) as well as differential algebraic equations (DAE) are discussed. Finally, techniques for “online” and “offline” simulations including real-time applications are presented like necessary for car development. Special examples show the connection between simulation and test results.
Nowadays, new technologies are pushing the road vehicle limits further. Promising applications, e.g., self-driving cars, require a suitable control system that can maintain the vehicle’s stability in critical scenarios. In most of current cars, the control systems actuates independently, meaning there is not a coordination or data sharing between them. This approach can produce a conflict between these standalone controllers and thus, no improvements on the vehicle’s stability are achieved or even a worse scenario can be generated. In order to overcome these problems, an integrated approach is developed in this work. This integration, defined in this work as Integrated Control (IC), is done by an intelligence coordination of all standalone controllers inside the vehicle, i.e., Anti-Lock Braking System (ABS), Electronic Stability Program (ESP) and Four-Wheel Steering System (4WS). The ABS model was built using Fuzzy logic, for which only three rules were necessary to get a good performance. To design the ESP and the 4WS, the simple handling vehicle model was used as a reference behavior. The IC was designed using the hierarchical approach with two layers, i.e., the upper and lower layer. The upper one, observes the side slip angle and depends of its value the upper layer triggers the ESP or the 4WS. Finally, in order to prove the improvements of the IC system over the non-integrated approach, a full-size vehicle model was used to perform simulation in run-off-road and μ-split scenarios.
Nach dem Aufbau eines Mehrkörper-Simulationsmodells muss dieses auf Richtigkeit, Funktionalität und Wirtschaftlichkeit getestet werden. Die Ermittlung der Gleichgewichtslage stellt dabei eine erste Plausibilitäts-Kontrolle dar. Eine Linearisierung mit anschließender Analyse der Eigendynamik liefert Aussagen über die Frequenzen und das Dämpfungsverhalten des Modells. Einfache Erregersignale ermöglichen einen ersten Einblick in das nichtlineare dynamische Verhalten des Modells. Modell-Parameter, die nicht genau bekannt sind, können durch gezielte Variationen plausibel geschätzt oder über eine Optimierung sogar mit optimalen Werten belegt werden. Nach all diesen Tests steht das Mehrkörper- Simulationsmodell dann für praktischeUntersuchungen zurVerfügung, die neben reinen Zeitsimulationen auch Methoden der Inversen Kinematik und der Inversen Dynamik mit einschließen.