Abstract:
In the context of automated driving, the control of vehicle dynamics is one of the important issues. In addition to conventional control strategies, algorithms with predi...Show MoreMetadata
Abstract:
In the context of automated driving, the control of vehicle dynamics is one of the important issues. In addition to conventional control strategies, algorithms with predictive working principles are particularly relevant here. Using mathematical models, the future system behavior can be predicted and thus optimally set. The present paper deals with an integrated non-linear model-based predictive vehicle dynamics control, taking into account the roll and pitch behavior of a vehicle. Due to the optimization, such model-based predictive control algorithms usually result in high computation efforts. With respect to this issue, a non-linear model-based predictive control algorithm regarding an integrated vehicle dynamics control is represented by a co-active neuro-fuzzy inference system. The validation of the two vehicle dynamics control algorithms is done with respect to the control quality and the computation effort.
Published in: 2020 IEEE Intelligent Vehicles Symposium (IV)
Date of Conference: 19 October 2020 - 13 November 2020
Date Added to IEEE Xplore: 08 January 2021
ISBN Information: