Vehicle Dynamics Estimation using Kalman Filtering

Vehicle dynamics and stability have been of considerableinterest for a number of years. The obvious dilemma is that peoplenaturally desire to drive faster and faster yet expect theirvehicles to be infinitely stable and safe during allnormal and emergency maneuvers. For the most part, people paylittle attention to the limited handling potential of theirvehicles until some unusual behavior is observed that often resultsin accidents and even fatalities.This book presents several model-based estimation methods whichinvolve information from current potential-integrable sensors.Improving vehicle control and stabilization is possible whenvehicle dynamic variables are known. The fundamental problem isthat some essential variables related to tire/road friction aredifficult to measure because of technical and economical reasons.Therefore, these data must be estimated.It is against this background, that this book's objective isto develop estimators in order to estimate the vehicle's loadtransfer, the sideslip angle, and the vertical and lateraltire/road forces using a roll model. The proposed estimationprocesses are based on the state observer (Kalman filtering) theoryand the dynamic response of a vehicle instrumented with standardsensors. These estimators are able to work in real time in normaland critical driving situations. Performances are tested using anexperimental car in real driving situations. This is exactly thefocus of this book, providing students, technicians and engineersfrom the automobile field with a theoretical basis and somepractical algorithms useful for estimating vehicle dynamics inreal-time during vehicle motion.