A Location-Aware Adaptive Vehicle Dynamics System (LAAVDS) is currently being developed to predict and maintain vehicle handling capabilities through upcoming maneuvers. This system depends heavily on an understanding of the interplay between the vehicle’s longitudinal, lateral, and vertical forces, as well as their resulting moments. These vehicle dynamics impact the Performance Margin metric and ultimately the point at which the Intervention Strategy will modulate the throttle and brake controls. Real-time implementation requires the development of computationally efficient predictive models of the vehicle dynamics. A method for predicting future vehicle states for smooth but tortuous roads is developed in this work using perturbation theory. An analytical relationship between the change in these forces and the resulting change in the Performance Margin is also derived. This model is implemented in the predictor-corrector algorithm of the Intervention Strategy. Corrections to the predicted states are made at each time step using a detailed, full, non-linear vehicle model; this full vehicle model is a precursor to incorporation of the LAAVDS in a real vehicle. Eventually, this work will be expanded to include the impact of rough terrain.
- Dynamic Systems and Control Division
Location-Aware Adaptive Vehicle Dynamics System: Linear Chassis Predictions
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Bandy, RA, Cho, S, Ferris, JB, Schlinkheider, J, & Wimmershoff, M. "Location-Aware Adaptive Vehicle Dynamics System: Linear Chassis Predictions." Proceedings of the ASME 2014 Dynamic Systems and Control Conference. Volume 1: Active Control of Aerospace Structure; Motion Control; Aerospace Control; Assistive Robotic Systems; Bio-Inspired Systems; Biomedical/Bioengineering Applications; Building Energy Systems; Condition Based Monitoring; Control Design for Drilling Automation; Control of Ground Vehicles, Manipulators, Mechatronic Systems; Controls for Manufacturing; Distributed Control; Dynamic Modeling for Vehicle Systems; Dynamics and Control of Mobile and Locomotion Robots; Electrochemical Energy Systems. San Antonio, Texas, USA. October 22–24, 2014. V001T10A003. ASME. https://doi.org/10.1115/DSCC2014-6092
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