A Novel Data-Driven Modeling and Control Design Method for Autonomous Vehicles
This paper presents a novel modeling method for the control design of autonomous vehicle systems. The goal of the method is to provide a control-oriented model in a predefined Linear Parameter Varying (<i>LPV</i>) structure. The scheduling variables of the <i>LPV</i> model th...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-01-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/14/2/517 |
| Summary: | This paper presents a novel modeling method for the control design of autonomous vehicle systems. The goal of the method is to provide a control-oriented model in a predefined Linear Parameter Varying (<i>LPV</i>) structure. The scheduling variables of the <i>LPV</i> model through machine-learning-based methods using a big dataset are selected. Moreover, the <i>LPV</i> model parameters through an optimization algorithm are computed, with which accurate fitting on the dataset is achieved. The proposed method is illustrated on the nonlinear modeling of the lateral vehicle dynamics. The resulting <i>LPV</i>-based vehicle model is used for the control design of path following functionality of autonomous vehicles. The effectiveness of the modeling and control design methods through comprehensive simulation examples based on a high-fidelity simulation software are illustrated. |
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| ISSN: | 1996-1073 |