A unified adaptive control formulation for safe and high-performance ground vehicle lateral guidance

In this paper, we propose a unified adaptive control formulation to improve the safety and performance of ground vehicle lateral guidance systems. This approach uniquely combines two features: prescribed performance control and hyperbolic sine-based attracting-manifold adaptation. The formulation offers two major benefits: first, prescribed performance control guarantees the forward invariance of tracking errors within predefined funnels, ensuring safety; second, the hyperbolic sine-based attracting-manifold adaptation enhances closed-loop performance by actively attenuating perturbations from parametric uncertainties and achieving adaptation without regret. By orchestrating these two mechanisms, the proposed approach outperforms designs that rely on either technique alone. Developed in a general framework, the formulation is applicable to a broad class of second-order control-affine systems in controllable canonical form. We demonstrate its effectiveness by applying it to ground vehicle lateral guidance and validate its performance through high-fidelity simulation using the CarSim-Simulink joint platform. Notably, the proposed controller achieved a preview tracking error convergence time of approximately 2 s—50% faster than baseline methods—and reduced peak lateral deviation by up to 66% during sharp lane-change scenarios, highlighting its practical benefits in both safety and performance.

Authors

Institutions

Publication Details

Journal
Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering
Published
2025-11-04
DOI
https://doi.org/10.1177/09544070251387214
Citations
1
Primary Topic
Vehicle Dynamics and Control Systems
Type
article
Field-Weighted Citation Impact
0.68
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A unified adaptive control formulation for safe and high-performance ground vehicle lateral guidance

Junzhe Shi, Xingyu Zhou, Zhenwu Fang, Jinhao Liang
1 citations
Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering
Vehicle Dynamics and Control Systems
0.68
article

A unified adaptive control formulation for safe and high-performance ground vehicle lateral guidance

Junzhe Shi, Xingyu Zhou, Zhenwu Fang, Jinhao Liang
article en
1 citations

Abstract

In this paper, we propose a unified adaptive control formulation to improve the safety and performance of ground vehicle lateral guidance systems. This approach uniquely combines two features: prescribed performance control and hyperbolic sine-based attracting-manifold adaptation. The formulation offers two major benefits: first, prescribed performance control guarantees the forward invariance of tracking errors within predefined funnels, ensuring safety; second, the hyperbolic sine-based attracting-manifold adaptation enhances closed-loop performance by actively attenuating perturbations from parametric uncertainties and achieving adaptation without regret. By orchestrating these two mechanisms, the proposed approach outperforms designs that rely on either technique alone. Developed in a general framework, the formulation is applicable to a broad class of second-order control-affine systems in controllable canonical form. We demonstrate its effectiveness by applying it to ground vehicle lateral guidance and validate its performance through high-fidelity simulation using the CarSim-Simulink joint platform. Notably, the proposed controller achieved a preview tracking error convergence time of approximately 2 s—50% faster than baseline methods—and reduced peak lateral deviation by up to 66% during sharp lane-change scenarios, highlighting its practical benefits in both safety and performance.

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering
National University of Singapore (SG), Walker (United States) (US), Southeast University (CN), The University of Texas at Austin (US), University of California, Berkeley (US)
Openalex Percentile: Top 24%
Vehicle Dynamics and Control Systems
0.68
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.