Disturbance-Observer-Based Model Predictive Control of Underwater Vehicle Manipulator Systems

This paper presents a disturbance-observer-based Model Predictive Control (MPC) for Underwater-Vehicle Manipulators Systems (UVMSs). First, the nominal MPC is formulated considering the lumped uncertainties representation for unknown terms and external disturbances. Then, a second-order Sliding Mode Disturbance Observer (SMDO) is developed to estimate the lumped disturbances, which are used by the MPC to produce unbiased predictions. The MPC-SMDO is tested through numerical simulations, considering a 5-DoF planar UVMS composed of the classical Twin-Burger autonomous vehicle presented in Ishitsuka et al. (2005) endowed with a 2-link robotic manipulator. To obtain realistic results, sensor noises, the dynamics of thrusters, and the stochasticity of ocean current are considered in the simulations. The results show good performance for the MPC-SMDO in terms of robustness, constraints meeting, and tracking errors minimization compared to two other controllers, based on the Computed Torque Control (CTC) technique and on the Super-Twisting Algorithm (STA).