Application Specific System Identification for Model-Based Control in Self-Driving Cars
Application Specific System Identification for Model-Based Control in Self-Driving Cars
- Neue Suche nach: Salt Ducaju, Julian M.
- Neue Suche nach: Tang, Chen
- Neue Suche nach: Tomizuka, Masayoshi
- Neue Suche nach: Chan, Ching Yao
- Neue Suche nach: Salt Ducaju, Julian M.
- Neue Suche nach: Tang, Chen
- Neue Suche nach: Tomizuka, Masayoshi
- Neue Suche nach: Chan, Ching Yao
Linear Parameter Varying (LPV) models can be used to describe the vehicular lateral dynamic behavior of self-driving cars. They are particularly suitable for model-based control schemes such as model predictive control (MPC) applied to real-time trajectory tracking control, since they provide a proper trade-off between accuracy in different scenarios and reduced computation cost compared to nonlinear models. The MPC control schemes use the model for a long prediction horizon of the states, therefore prediction errors for a long time horizon should be minimized in order to increase the accuracy of the tracking. For this task, this work presents a system identification procedure for the lateral dynamics of a vehicle that combines a LPV model with a learning algorithm that has been successfully applied to other dynamic systems in the past. Simulation results show the benefits of the identified model in comparison to other well-known vehicular lateral dynamic models.
Dokumentinformationen
Application Specific System Identification for Model-Based Control in Self-Driving Cars
2020-01-01
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
| DDC: | 629 |
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