Robust Dynamic State Estimation for Lateral Control of an Industrial Tractor Towing Multiple Passive Trailers
Shunbo Zhou, Hongchao Zhao, Wen Chen, Zhe Liu, Hesheng Wang, Yun-Hui Liu
Abstract
In this paper, we propose a dynamic state estimation framework for lateral control of a heavy tractor-trailers system using only mass-produced low-cost sensors. This issue is challenging since the lateral velocity of the lead tractor is difficult to measure directly. The performance of existing dynamic model-based estimation methods will also be degraded, as different trailers and payloads cause the tractor model parameters to change. We address this issue by incorporating a kinematic estimator into a dynamic model-based estimation scheme. Accurate and reliable tire cornering stiffness and dynamics-informed lateral velocity of the lead tractor can be output in real-time by using our method. The stability and robustness of the proposed method are theoretically proved. The feasibility of our method is verified by full-scale experiments. It is also verified that the estimated model parameters and lateral states do improve the control performance by integrating the estimator into a lateral control system.
BibTeX
@inproceedings{iros2020_robustdynamicsta,
title = {Robust Dynamic State Estimation for Lateral Control of an Industrial Tractor Towing Multiple Passive Trailers},
author = {Shunbo Zhou and Hongchao Zhao and Wen Chen and Zhe Liu and Hesheng Wang and Yun-Hui Liu},
booktitle = {IROS 2020},
year = {2020}
}