This paper presents an Optimized Unscented Kalman Filter for vehicle dynamics virtual sensing. An automated procedure to optimize the virtual sensor parameters based on metaheuristic algorithms is presented in order to avoid the time-consuming and complex manual tuning task. Specifically, Genetic Algorithm Optimization (GA) and contrast-based Fruit Fly optimization (c-FOA) are applied to minimize the estimation error in steady-state and transient driving maneuvers. The virtual sensor is implemented in a high-fidelity vehicle dynamics simulation software (IPG-CarMaker ®) and results demonstrate the improvement of the estimation accuracy with respect to a preliminary filter tuning carried out using a systematic trial and error approach.
|Title of host publication||Evolutionary and Deterministic Methods for Design Optimization and Control With Applications to Industrial and Societal Problems|
|Publication status||Published - Sep 2018|
|Event||International Conference On Evolutionary And Deterministic Methods For Design Optimization And Control With Applications To Industrial And Societal Problems - Madrid, Spain|
Duration: 13 Sep 2017 → 15 Sep 2017
|Name||Computational Methods in Applied Sciences|
|Conference||International Conference On Evolutionary And Deterministic Methods For Design Optimization And Control With Applications To Industrial And Societal Problems|
|Abbreviated title||EUROGEN 2017|
|Period||13/09/17 → 15/09/17|
Acosta, M., & Kanarachos, S. (2018). Optimized Vehicle Dynamics Virtual Sensing using Metaheuristic Optimization and Unscented Kalman Filter. In Evolutionary and Deterministic Methods for Design Optimization and Control With Applications to Industrial and Societal Problems (pp. 275-290). (Computational Methods in Applied Sciences; Vol. 49). Springer Verlag.