Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | Computational Methods in Applied Sciences |
| Editors | Esther Andrés-Pérez, Leo M. González, Jacques Periaux, Nicolas Gauger, Domenico Quagliarella, Kyriakos Giannakoglou |
| Publisher | Springer |
| Pages | 275-290 |
| Number of pages | 16 |
| Edition | 1 |
| ISBN (Electronic) | 978-3-319-89890-2 |
| ISBN (Print) | 978-3-319-89889-6 |
| DOIs | |
| Publication status | Published - 7 Sept 2018 |
Publication series
| Name | Computational Methods in Applied Sciences |
|---|---|
| Volume | 49 |
| ISSN (Print) | 1871-3033 |
Funding
Acknowledgements This research is part of the Interdisciplinary Training Network in Multi-actuated Ground Vehicles (ITEAM) and has received funding from the European Unions Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No: 675999.
ASJC Scopus subject areas
- Civil and Structural Engineering
- Modelling and Simulation
- Biomedical Engineering
- Computer Science Applications
- Fluid Flow and Transfer Processes
- Computational Mathematics
- Electrical and Electronic Engineering
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