ARV Nonlinear Disturbance Estimation Based on Extended State Observer

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 experiments were conducted against traditional Luenberger observers. Results demonstrate that the developed observer achieves superior convergence speed and steady-state accuracy in estimating disturbance forces, disturbance moments, surge velocity, heave velocity, and yaw angular velocity. This advancement significantly enhances state tracking capability under complex disturbances.