ITSC 2025 Paper Abstract

Close

Paper FR-LM-T41.3

Fu, Yonghao (Zhejiang University), Hu, Cheng (Zhejiang University), Xiong, Haokun (Zhejiang University), Bao, Zhanpeng (Zhejiang University), Du, Wenyuan (Zhejiang University), Ghignone, Edoardo (ETH Zurich), Magno, Michele (ETH Zurich), Xie, Lei (Zhejiang University), Su, Hongye (Zhejiang University)

Residual Koopman Model Predictive Control for Enhanced Vehicle Dynamics with Small On-Track Data Input

Scheduled for presentation during the Regular Session "S41a-Motion Planning, Trajectory Optimization, and Control for Autonomous Vehicles" (FR-LM-T41), Friday, November 21, 2025, 11:10−11:30, Broadbeach 1&2

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Keywords Real-time Motion Planning and Control for Autonomous Vehicles in ITS Networks, Energy-efficient Motion Control for Autonomous Vehicles, Autonomous Vehicle Safety and Performance Testing

Abstract

In vehicle trajectory tracking tasks, the simplest approach is the PP Control. However, this single-point preview tracking strategy fails to consider vehicle model constraints, compromising driving safety. MPC as a widely adopted control method, optimizes control actions by incorporating mechanistic models and physical constraints. While its control performance critically depends on the accuracy of vehicle modeling. Traditional vehicle modeling approaches face inherent trade-offs between capturing nonlinear dynamics and maintaining computational efficiency, often resulting in reduced control performance. To address these challenges, this paper proposes RKMPC framework. This method uses two linear MPC architecture to calculate control inputs: a MPC computes the baseline control input based on the vehicle kinematic model, and a neural network-based RKMPC caculates the compensation input. The final control command is obtained by adding these two components. This design preserves the reliability and interpretability of traditional mechanistic model while achieving performance optimization through residual modeling. This method has been validated on the Carsim-Matlab joint simulation platform and a physical 1:10 scale F1TENTH racing car. Experimental results show that RKMPC requires only 20% of the training data needed by traditional KMPC while delivering superior tracking performance. Compared to traditional LMPC, RKMPC reduces lateral error by 11.7%–22.1%, decreases heading error by 8.9%–15.8%, and improves front-wheel steering stability by up to 27.6%. The implementation code is available at: https://github.com/ZJU-DDRX/Residual_Koopman.

 

 

All Content © PaperCept, Inc.


This site is protected by copyright and trademark laws under US and International law.
All rights reserved. © 2002-2025 PaperCept, Inc.
Page generated 2025-10-18  21:25:26 PST  Terms of use