In vehicle dynamics control, engineering a suitable regulator is a long and costly process. The starting point is usually the design of a nominal controller based on a simple control-oriented model and its testing on a full-fledged simulator. Then, many driving hours are required during the End-of-Line (EoL) tuning phase to calibrate the controller for the physical vehicle. Given the recent technological advances, we consider in this article the pioneering perspective where the simulator can be run onboard in the electronic control unit, to calculate the nominal control action in real-time. In this way, it can be shown that, in the EoL phase, we only need to tune a simple compensator of the mismatch between the expected and the measured outputs. The resulting approach not only exploits the already available simulator and nominal controller and significantly simplifies the design process but also outperforms the state of the art in terms of tracking accuracy and robustness within a challenging active braking control case study.

The Twin-in-the-Loop Approach for Vehicle Dynamics Control

Federico Dettu;Simone Formentin;Sergio Matteo Savaresi
2023-01-01

Abstract

In vehicle dynamics control, engineering a suitable regulator is a long and costly process. The starting point is usually the design of a nominal controller based on a simple control-oriented model and its testing on a full-fledged simulator. Then, many driving hours are required during the End-of-Line (EoL) tuning phase to calibrate the controller for the physical vehicle. Given the recent technological advances, we consider in this article the pioneering perspective where the simulator can be run onboard in the electronic control unit, to calculate the nominal control action in real-time. In this way, it can be shown that, in the EoL phase, we only need to tune a simple compensator of the mismatch between the expected and the measured outputs. The resulting approach not only exploits the already available simulator and nominal controller and significantly simplifies the design process but also outperforms the state of the art in terms of tracking accuracy and robustness within a challenging active braking control case study.
2023
Automotive control
digital twins
nonlin-ear control systems
statistical learning
vehicle dynamics
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1247657
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