An Adaptive Model Predictive Control for Unmanned Underwater Vehicles Subject to External Disturbances and Measurement Noise

This research addresses the trajectory tracking problem of an unmanned underwater vehicle (UUV) within 4 degrees of freedom (DOF) subject to external disturbances and measurement noise. An adaptive control framework consisting of an adaptive model predictive control (MPC) and an error-state extended state observer (ESESO) is proposed. The MPC is utilized to stabilize the system while the ESESO is proposed to estimate the disturbances. In contrast to most conventional ESOs, we explicitly formulate a sensor-fusion problem by tracking the error state of the observer. The ESESO feeds back the filtered state and the estimated lump disturbances to the MPC to achieve the adaptability of the closed-loop system. Sufficient simulation via a semi-physical experiment is conducted to validate the effectiveness and superiority of the proposed control framework.