The shipping industry is forced to reduce its emissions and underwater radiated noise in order to decrease the impact on the environment and limit global warming. Moreover, a low acoustic signature can be lifesaving for a navy ship, especially during submarine on mine threats. To be able to operate as flexible as possible under these threats, a flexible propulsion plant is required. A diesel engine with a controllable pitch propeller (CPP) is contributing to this. A method to reduce the emissions and noise of such a plant is to improve the control strategy. Often this is tested with numerical models. However, frequently they contain many simplifications of reality, for example, the neglection of several hydrodynamic effects. Hardware In the Loop (HIL) takes these effects into account by replacing a software part of the model with a hardware component. In this research, the CPP is the scaled hardware part. A feasibility study is performed to determine whether a propeller open water HIL setup can be used to simulate the dynamic behaviour of the propulsion plant. Therefore, a comparison is made between full and model scale numerical simulation of a diesel engine with a CPP controlled by adaptive pitch control (APC). To reduce the pitch actuations with the APC strategy a Kalman filterwith deadband is implemented. Full-scale simulations with irregular waves demonstrated that the pitch actuations could be reduced during acceleration and at a constant speed. The validated full-scale model is Froude scaled and implemented in a model of the open water HIL setup. The results of the full and model scale simulations are very similar. Thus, the HIL setup can be used to simulate the dynamic behaviour of a propulsion plant. However, the hardware limitations such as the backlash in the CPP blades, the low sample rate of the actual pitch and the Froude dissimilarity of the acceleration of the towing tank need to be resolved to ensure the scaled simulations are representative for the actual, full scale, vessel. Due to these limitations, it is not possible to use the setup at this moment to perform experiments and improve novel control strategies. If these physical limitations of the current HIL setup are overcome, the effect of the propeller and the pitch control strategy during turns can be investigated by performing experiments with oblique inflow.

Nowadays, machine learning (ML) methods rapidly evolve for their use in model-based control applications. Model-based control requires an accurate model description of the dynamical system to reassure the performance of the controller. Conventionally, this model description is retrieved from first-principles modelling which can be problematic if the system consists of high-order and/or time-varying dynamics. In these applications, ML methods may benefit because of their promising potential to model complicated system behaviour from data. In the field of ML methods for the modelling and control of dynamical systems, Gaussian processes (GPs) form an interesting opportunity. The ability of GPs to directly learn nonlinear system dynamics prevents huge costs and/or efforts when modelling complex systems. GP dynamical models are capable of accurately predicting the behaviour of dynamical systems while also measuring the confidence level of the prediction. For making predictions, GPs do not require huge amounts of data which benefits them over other ML methods. The powerful model predictive control (MPC) and the fully data-driven dynamical modelling capabilities of GPs makes their combination an interesting candidate for sophisticated control systems. MPC has advantages over other control methods since the controller allows operational constraints that provide freedom in controller design or prevent the system to be steered in an infeasible direction, and, the controller is easily extendable to nonlinear and multivariable control. Next to this, an MPC controller is naturally modified to incorporate GP dynamical models (GP-MPC). The GP-MPC controller exploits the GP dynamical model for making predictions over the prediction horizon while it is also possible to incorporate the confidence of the predictions for increased robustness of the controller. Whereas GP-MPC is studied extensively as an augmented model for other modelling techniques, fully data-driven GP-MPC approaches are also deemed to be promising. This thesis considers the use of GPs for learning and modelling dynamic systems for incorporation in a realtime MPC application. The dynamic system studied in this thesis is a double pendulum system in both a simulation and a real-world environment. The simulation provides initial insights into the problem and allows the rapid development of an algorithm. Eventually, the proposed realtime GP-MPC algorithm is tested on a physical laboratory-scale setup. The results show that the realtime data-driven controller is able to track a reference with high accuracy, while also being robust to disturbances. These promising results on GPs for realtime nonlinear control might be a step for GPs to be incorporated into future control systems.

Offshore wind energy is a promising prospect to meet the ever-growing demand for energy. Its reputation is, however, sullied by high costs not yet competitive with conventional energy resources. The Delft Offshore Turbine (DOT) attempts to radically reduce the Levelized Cost of Energy (LCOE) of offshore wind by utilising a seawater-hydrostatic transmission from individual wind turbines to a central power station, where power is generated collectively. Recent theoretical research on the DOT employed variable displacement pumps in the wind turbines interconnected through a collective pipeline. A variable displacement pump does not yet commercially exist and leaves the implementation of a fixed-displacement pump more viable in the near future. Recent practical research implemented a fixed-displacement pump with an individual pipeline. To benefit from the economy of scale however, a collective pipeline is preferred. The DOT wind farm considered in this thesis incorporates fixed-displacement pumps interconnected through a collective pipeline. The difficulty herein arises from the combination of an equal hydraulic torque but varying aerodynamic torque throughout the wind farm. The combined effect results in the inability to control individual rotor speeds, which inevitably leads to suboptimal individual rotor efficiency. This research explores a novel control strategy that obtains the best suboptimal performance when the wind turbines are controlled through a collective pressure.