Improving AUV Stability and vSLAM Performance using a Combined Nonlinear Disturbance Observer and Predictive Control under Ocean Disturbances

Autonomous Underwater Vehicles (AUVs) face challenges in maintaining stability during visual Simultaneous Localization and Mapping (vSLAM) operations, particularly when affected by internal solitary waves (ISWs). This study presents a novel integrated control strategy that combines a Nonlinear Disturbance Observer (NDO) and Nonlinear Model Predictive Control (NMPC), specifically adapted to address the nonlinear and unpredictable nature of ISW disturbances in underwater environments. Unlike previous NDO-NMPC implementations in other domains, this framework incorporates dynamic modeling of ISWs and underwater-specific tuning mechanisms to maintain robust vSLAM performance. To the best of our knowledge, this is the first study integrating NDO with NMPC for AUV-vSLAM under ISW disturbances. The proposed method is evaluated using a custom AUV model integrated with the ORB-SLAM2 framework, tested through Software-in-the-Loop (SITL) simulations under various ISW intensities. Results show that the NDO-NMPC algorithm outperforms traditional PID, Sliding Mode Control (SMC), and standalone NMPC controllers in terms of stability, trajectory tracking, and mapping accuracy. This approach reduces the impact of ISWs, improves the number of visual feature points for mapping, and achieves lower Root Mean Square Error (RMSE) in position and velocity. This work offers a robust solution for improving AUV navigation and mapping in dynamic underwater environments, with potential applications in autonomous underwater exploration and surveying.