ARV Nonlinear Disturbance Estimation Based on Extended State Observer

• Published in: 水下无人系统学报 Vol. 33; no. 3; pp. 433 - 440
• Main Authors: Xiaohan LIU, Chenhao ZHAO, Haomiao NIE, Feng XIANG, Chenguang LI, Min ZHAO
• Format: Journal Article
• Language: Chinese
• Published: Science Press (China) 01.06.2025
• Subjects: autonomous and remotely-operated vehicle; disturbance estimation; extended state observer
• ISSN: 2096-3920
• DOI: 10.11993/j.issn.2096-3920.2025-0035

Autonomous/remotely-operated vehicles(ARVs) are susceptible to complex flow field disturbances during underwater path tracking missions, where traditional linear observers exhibit suboptimal performance in addressing flow field-induced nonlinear disturbances. This paper proposed a dynamic high-gain extended observer method to resolve the nonlinear disturbance estimation challenge for the “Siyuan” ARV. Firstly, a nonlinear kinematic and dynamic model of the ARV was established, with external disturbance data acquired through sea trial path tracking experiments. Secondly, a dynamic gain compensation mechanism was introduced to address nonlinear system observation, effectively overcoming limitations in conventional methods such as the difficulty in determining Lipschitz function coefficient and empirical dependence in parameter tuning. The convergence of dynamic gains was rigorously ensured through the incorporation of performance constraint parameters. To validate the proposed method, comparative simulation exp