Disturbance Observer-Based Motion Control of Small Autonomous Underwater Vehicles

This paper presents a trajectory-tracking method using disturbance observer-based model predictive control (MPC) for small autonomous underwater vehicles (AUV). The goal of the work is to design a robust motion controller for AUVs under the system constraints and unknown disturbances such as hydrodynamics and ocean currents. Super-twisting-algorithm (STA) is employed to design the disturbance observer and its output is used and included in the feedback linearization law to compensate for the disturbances. The control inputs are generated using the MPC design with the nominal linearized model. Simulation results are included to validate the effectiveness of the control design and also compare with the traditional MPC motion control.