Remotely Operated underwater Vehicles (ROVs) are growing in importance in the ocean environment for observation and manipulation tasks, particularly when used to maintain offshore energy and offshore renewable energy assets. Many such tasks require the dynamic positioning of ROV in challenging sea conditions with multiple disturbances resulting from the effects of waves, currents and turbulence. This work presents a novel, nonlinear, model predictive dynamic positioning controller that accounts for such complex stochastic disturbances. These external disturbances are modelled as 6-degree of freedom forces and moments within the nonlinear ROV dynamic and propulsion model. A nonlinear model predictive dynamic positioning strategy based on the nonlinear model predictive control (NMPC) is proposed for the disturbance rejection in this work. A numerical water tank model is used to test the performance of the strategy using hardware in-the-loop simulation. The results of the simulation have been compared against baseline proportional-integral-derivative (PID) and linear quadratic regulator (LQR) controllers tested under wave and current conditions in the FloWave basin. A quantitative comparison of the controllers is presented. The resulting controller is shown to maintain a small root mean squared error (RMSE) in position when subjected to multiple directional disturbance, with minimal control effort. This study contributes an important insight on future theoretical design of model predictive disturbance rejection controllers and illustrates their practical implementation on real hardware.

Predictive control methods can substantially improve the performance of Unmanned Underwater Vehicles (UUVs), particularly in shallow water environments or near the free surface where wave induced disturbance are of magnitude comparable to the vehicle characteristic inertia. To facilitate the adoption of these methods, a fast estimation of the time evolution of hydrodynamic forces acting on a vehicle is required. To this end, we perform experiments in a wave tank with an ROV to validate the use of Linear Wave Theory (LWT) to capture the time history of surge, heave and pitch wave induced forces and moments. Validation is performed for various sea states, reconstructed with a mean correlation of 0.9138 in comparison to experimental measurements, displaying a maximum normalised mean error deviation between simulation and experimental data of 0.16 and 0.27 respectively for surge and heave forces, and 0.34 for pitch moment. The effectiveness of employing real-time wave disturbance forecasting for the purpose of anticipatory control is then assessed by incorporating the predicted loads within a Model Predictive Controller. Results display a mean RMS positional error reduction of 47.32% in comparison to a standard PD controller. This presents evidence that accurate, near real-time predictions of the wave-generated forces and moments on an ROV can be produced, laying the foundation for developing model-based predictive control strategies that better suit operation in harsh environments.

The remotely operated vehicles (ROVs) are important to provide the technology support for both the traditional offshore structures and rapidly-growing renewable energy facilities during their full-lifecycles, such as site survey, installation, inspection, maintenance and repair. Regarding the motion and performance of a ROV, the understanding of its hydrodynamic properties is essential when exposing to the disturbances of wave and current. In this study, a numerical model is proposed within the frame of an open-source platform OpenFOAM. The hydrodynamics of the adopted ROV (BlueRov2) in its four principal degrees of freedoms (DOFs) is numerically simulated by a Reynolds-Averaged Navier-Stokes (RANS) solver. Meanwhile, an experimental test is carried out by using a novel technique on measuring the hydrodynamic forces and moments. To validate the numerical prediction methodologies, a set of systematic simulations of the ROV subjected to the disturbances caused by various flow conditions are performed. Comparing to the model test measurement, the numerical model proved to be reliable in offering a good estimation of the hydrodynamic parameters. This also indicates that the presented numerical methodologies and experimental techniques can be applied to other types of open-frame ROVs in quantifying the hydrodynamic parameters, capturing the physics of the fluid-structure interaction (FSI) and feature of the turbulent vorticity which are all essential for the effective control of the ROVs under the nonlinear flow disturbances.

Hydrodynamic forces are an important input value for the design, navigation and station keeping of underwater Remotely Operated Vehicles (ROVs). The experiment investigated the forces imparted by currents (with representative real world turbulence) and waves on a commercially available ROV, namely the BlueROV2 (Blue Robotics, Torrance, USA). Three different distances of a simplified cylindrical obstacle (shading effects) were investigated in addition to the free stream cases. Eight tethers held the ROV in the middle of the 2 m water depth to minimise the influence of the support structure without completely restricting the degrees of freedom (DoF). Each tether was equipped with a load cell and small motions and rotations were documented with an underwater video motion capture system. The paper describes the experimental set-up, input values (current speed and wave definitions) and initial processing of the data. In addition to the raw data, a processed dataset is provided, which includes forces in all three main coordinate directions for each mounting point synchronised with the 6DoF results and the free surface elevations. The provided dataset can be used as a validation experiment as well as for testing and development of an algorithm for position control of comparable ROVs.

Renewable energy systems that extract power from sustainable sources such as wind, wave and solar can provide clean energy to a range of marine applications. Currently a major challenge is in providing an uninterrupted power supply, given the variation and uncertainty in these renewable energy sources. To deal with the unpredictable nature of renewable energy resources, this paper investigates the use of hybrid systems (systems that harvest multiple sources) to reduce the variation and uncertainty of the power generated. Considering the use of multiple renewable energy resources including solar, wind and wave, this paper first evaluates the available energy resources and the technical recoverable power using open-source, yearly, global, meteorological datasets. Based on the technical recoverable potential the complimentary features between solar and wind resources are investigated and compared to the propulsive power demand of various sized ocean going platforms and routes. The analysis shows that the resources can be complementary and that hybrid systems are more suitable for small vehicles, craft or maritime robotics system such as autonomous surface and underwater vehicles and oceanographic buoys.