AAC 2022 Paper Abstract

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Paper WeAT2.3

Allamaa, Jean Pierre (Siemens Digital Industries Software), Patrinos, Panagiotis (KU Leuven), Van der Auweraer, Herman (LMS International), Son, Tong (Siemens Digital Industries)

Sim2real for Autonomous Vehicle Control Using Executable Digital Twin

Scheduled for presentation during the Regular Session "Modeling and Simulation Tools for Vehicular Systems" (WeAT2), Wednesday, August 31, 2022, 10:40−11:00, Pfhal Hall 140

10th IFAC International Symposium on Advances in Automotive Control, August 28-31, 2022, Columbus, Ohio, USA

This information is tentative and subject to change. Compiled on April 24, 2024

Keywords Control, guidance and navigation of autonomous vehicles, Testing and validation, Advanced Driver Assist Systems

Abstract

In this work, we propose a sim2real method to transfer and adapt a nonlinear model predictive controller (NMPC) from simulation to the real target system based on executable digital twin (xDT). The xDT model is a high fidelity vehicle dynamics simulator, executable online in the control parameter randomization and learning process. The parameters are adapted to gradually improve control performance and deal with changing real-world environment. In particular, the performance metric is not required to be differentiable nor analytical with respect to the control parameters and system dynamics are not necessary linearized. Eventually, the proposed sim2real framework leverages altogether online high fidelity simulator, data-driven estimations, and simulation based optimization to transfer and adapt efficiently a controller developed in simulation environment to the real platform. Our experiment demonstrates that a high control performance is achieved without tedious time and labor consuming tuning.

 

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