Improved Anti-Peak Extended State Observer Based Data-Driven Trajectory Tracking Control for Unmanned Marine Vehicles

Li-Ying Hao; Xuqi Zhang; Huiying Liu

RA-L 20260 citations

Improved Anti-Peak Extended State Observer Based Data-Driven Trajectory Tracking Control for Unmanned Marine Vehicles

Abstract

For extended state observer (ESO)-based trajectory tracking, the peaking phenomenon in state estimates can cause instability or performance degradation. This work proposes an improved anti-peak ESO-based data-driven control strategy for unmanned marine vehicles (UMVs). A local compact form dynamic linearization (CFDL) model is established, relaxing the traditional requirement of non-zero input differences. An improved anti-peak ESO estimates lumped disturbances for compensation, while output error rate enhances tracking accuracy. The approach improves historical data utilization with low computational cost. Stability is proven via mathematical induction, and simulations show superior performance in tracking error reduction and peaking suppression compared to conventional ESO methods.

BibTeX

@inproceedings{ral2026_improvedantipeak,
  title = {Improved Anti-Peak Extended State Observer Based Data-Driven Trajectory Tracking Control for Unmanned Marine Vehicles},
  author = {Li-Ying Hao and Xuqi Zhang and Huiying Liu},
  booktitle = {RA-L 2026},
  year = {2026}
}