With the increasing water depths of the new offshore wind farms, the challenging soil conditions, the availability of assets, and other factors, jack-up installation vessels may no longer be suitable to complete the installation scope of work for the new wind farms. Therefore, installation methods and techniques using floating vessels must be further developed to allow safe and efficient installation of wind turbines. Due to the response of floating installation vessels, excessive motions can be transmitted to the lifted object, making the installation operation very weather sensitive. To increase workability, Heerema Marine Contractors (HMC) has developed the RNA method for wind turbine installation using a semi-submersible crane vessel. This method uses a temporary support structure on the vessel deck (installation tower) where the RNA is fully assembled with the assistance of the GREP (Guided Root End Positioning) tool to constrain the blade root to the top of the installation tower and consequently the hub. Finally, the RNA is installed in a single lift on the WTG permanent support structure. However, the GREP tool is compatible only with a specific range of wind turbine blade dimensions; therefore, for a different-size wind turbine blade a new GREP tool must be designed, fabricated, and mobilised. This project proposes an improvement to the HMC's RNA method to eliminate the necessity of the GREP tool. That is, a motion-compensated Stewart platform attached to the crane boom, where the blade is fixed to be installed in the hub on top of the installation tower. The project is developed by investigating the initial assumptions; those are crane boom stiffness, blade deflection, installation tower motions and their influence on the vessel's response, and aerodynamic loads acting on the blade during installation. The kinematics (inverse and forward) and dynamics of a Stewart platform are formulated, as well as the mechanical concept of the proposed system and the blade installation process using a Stewart platform attached to the crane boom. Furthermore, to eliminate the requirement of the GREP tool, a control system is developed to compensate for the blade root motions relative to the hub. The motion control system uses sensors to measure the hub's motions and generate the Stewart platform actuators' set points. Different possible sensor set-ups are evaluated, and a filter is designed to reduce the influence of the sensors' noise. The control system is developed on the basis of feedback PI (Proportional and Integral) and adaptive feedforward control to the actuators (hydraulic cylinders). It is concluded that it is technically feasible to use a motion- compensated Stewart platform for blade installation in the RNA method. However, the economic aspects of the proposed solution must be investigated.

The growing importance of renewable energy to meet the demands of growing population has driven much focus for research in the wind energy sector. Currently most of the power production in the wind energy sector is done using the Horizontal Axis Wind Turbine (HAWT). due to its higher efficiency and reliability as compared to Vertical Axis Wind Turbine (VAWT). However, due to limitations of HAWTs such as the complex yaw mechanism, higher positioning of the center of mass and relatively difficult up-scaling, VAWT are receiving more attention. Attempt to access high range of power from VAWTs at offshore locations may damage the turbine blades due to increased loads. This thesis is dedicated to reduce the periodic disturbances on the turbine blades of VAWTs without affecting the total power production in a rotation, thereby ensuring the reliability and safe operation of VAWT. A control technique called Subspace Predictive Repetitive Controller (SPRC) is used for the recursive identification to estimate the parameters of wind turbine model and further providing an optimal control law accordingly. Basis functions have been used to reduce the dimensionality of the system, following which the system identification has been performed in the lifted domain. Using the identified model, two types of control approaches have been applied. In the first approach, the objective function is to track a specific reference that indicates blade load. This reference ensures a reduction in peak loads of the VAWT by transferring the loads from their upstream to their downstream part, without comprising on the power production in one whole rotation of VAWT. In the second approach, a general control strategy is used in which the controller is given freedom to choose the pitch trajectory so as to reduce the blade loads. The controller is specified with a power reference that ensures the maintenance of total power. This strategy allows the turbine to be operated in different wind conditions, as a higher weighting is assigned to power production when wind speeds are less than rated wind speeds, and a higher weighting is assigned to the reduction of blade load when wind speeds are more than the rated wind speeds. . These results show a real potential of the data-driven SPRC approach in wind turbines, and highlights new mechanisms to reduce the turbine loads on c{VAWT}s. These mechanisms offer valuable insights into enhancing the functions of VAWTs in a more reliable and damage-free manner.

Underwater position estimation is challenging due to the absence of Global Navigation Satellite System (GNSS) signals. Underwater vehicles are typically equipped with a Doppler Velocity Log (DVL) that measures the velocity relative to the seafloor. Aside from the velocity, the DVL also measures the range of each of the four beams. When compared against a bathymetry height map, these measured ranges provide additional information enabling improving position estimates. Unfortunately, surveys often occur in areas where detailed bathymetry maps are unavailable. In these cases, bathymetry Simultaneous Localization and Mapping (SLAM) could be used to improve the position estimates compared to the velocity integration position. With SLAM, the map is being estimated during the mission while at the same time using the map for position determination. In this thesis, reduced rank Gaussian processes (GPs) are used as map representation for SLAM. The downside of regular GPs is a time complexity of O(n^3) , reduced rank GPs improve the computation performance. GPs provide Gaussian distributions at any point, leading to a neat integration with probabilistic SLAM algorithms. To the best of the author’s knowledge, this has not been used in bathymetry SLAM. This report investigates how reduced rank approximated GPs, representing the bathymetry, can be integrated into a SLAM algorithm to improve the position estimates of an underwater vehicle equipped with a DVL and low-quality gyroscopes. A squared exponential kernel is used as GPs model of the bathymetry. The reduced rank approximation is vital for real-time SLAM performance. This map representation is integrated with a Rao Blackwellized particle filter (RBPF) that estimates both the underwater vehicle’s trajectory and the bathymetry map. The SLAM algorithm is evaluated using data from an underwater vehicle operated at the surface such that a GNSS reference position is available. Experiments of the SLAM algorithm show a reduced position error compared to the GNSS reference. The resulting algorithm has a computation time of up to 30 times faster than the Autonomous Underwater Vehicle (AUV) collects data while improving position estimates. This concludes that the RBPF using reduced rank GPs is capable of onboard improved position estimation on underwater vehicles.

Drifting, a specialized form of sideslip control, involves intentionally inducing and maintaining a state of oversteer for lateral sliding of the vehicle. While previous research has primarily focused on autonomous drift control, the integration of the driver in the control loop remains largely unexplored. This thesis aims to explore how a vehicle sideslip control system can be designed and evaluated for driver-involved drifting scenarios. To this end, a seven degree-of-freedom vehicle model and a tire model calibrated on data from a test vehicle were used to develop a model-based optimization scheme, leveraging the capabilities of all-wheel torque vectoring to achieve the desired drifting behavior. Phase portrait and real data analyses during drifting maneuvers were conducted to understand drift dynamics and develop the reference generator, which calculates targets for the controller. The reference generator and controller were validated through simulations using IPG CarMaker, drifting along a constant radius circle and a section of a track. The results demonstrate that the developed control system enables stable and controllable drift behavior with a driver-in-the-loop and allows drivers to fully exploit their vehicle’s capabilities.

Offshore excavators are large hydraulically driven machines which are difficult to control due to slow dynamics, inherent nonlinearities, a varying environment and complex kinematics. As digging is performed under water, only limited visual feedback of the task can be provided by means of a visualization interface. Operators require an extensive amount of practice before being capable of achieving sufficient and consistent performance. Often, automation is implemented as a means of reducing costs related to expensive operators and attaining consistent performance. However, automation struggles with adapting to unforeseen situations and a large task variety, which are areas human operators excel in. Instead of attempting to fully automate excavators, this thesis takes a more human-centered approach, and focuses on the design and evaluation of a human-like controller to partially automate excavator operations, while assuming a human operator is still present to trade or share control with. In order to simultaneously deal with the various nonlinearities in the system while providing human-like control this work proposes the use of an Adaptive Model Predictive Controller, whose underlying principles are similar to those of humans.To determine whether the controller is indeed human-like a complex excavator model including a realistic soil model was developed and used to implement and tune the controller. Finally, a simulator experiment was conducted to compare the subjects and the controller in terms of performance for various tasks and the control behavior similarity for a well-trained task. Eight subjects controlled the excavator model and performed four stages, starting with a familiarization stage in which the subject got accustomed to the system. The other three stages (easy, difficult, boulder) featured a 9 m long target path, with conditions of varying difficulty between stages. The controller showed 2 to 3 times lower tracking errors for both the easy and difficult stage while providing 1.5 to 5 times smoother inputs, but could not overcome the unforeseen boulder whereas all subjects could, showcasing the importance of having humans and automation complement each other. Furthermore, a high quality fit (VAF > 70%) was found between the boom inputs of the subjects and the controller in the well-trained easy stage, indicating human-like control.

Wind turbine installation vessels (WTIVs) are ships that are specifically designed to install offshore wind turbines. These WTIVs have four or six large truss-like legs that are lowered towards the seabed by means of jacking systems. These jacking systems are regulated by control systems to ensure the leg is lowered with a constant velocity regardless of external disturbances. The bottom of these legs are outfitted with spudcans, which have a conical shape to allow for penetration into the seabed. When these spudcans have settled in the seabed, the hull is lifted above the waves. Consequently, wave-exciting forces on the hull are prevented and the motions of the hull are near-zero so that the on-board crane can perform the installation operations with minimised disturbances. The objective of this thesis is to develop and analyse a model that is able to describe and simulate the dynamics of these jacking systems in great detail in response to external loads and the dynamics of the WTIV. The control systems of the jacking systems are included in this model to simulate and evaluate the interaction between the control system and the dynamics of the WTIV. Two conventional control systems are considered: the Volts-per-Hertz (V/Hz) and the direct torque control (DTC) method. In the process of lowering the legs and subsequent platform lifting, a transient phase can be identified during which the spudcans are penetrating the seabed. Due to the periodical motions of the ships, multiple impacts with the seabed are expected. Additionally, the jacking systems and the leg undergo a change of load direction as initially the leg is in tension and the jacking systems are generating power, and afterwards the leg is in compression and the jacking systems are consuming power. This thesis is focused on this seabed penetration phase as this phase introduces complicated dynamics. In literature, no model is available that has the abilities to simulate the WTIVs and its jacking systems with control systems in such level of detail. This research gap is addressed by developing such a simulation model. This model is written in Python and developed using finite element (FE) techniques and solved using numerical time integration. Seabed characteristics are derived using a detailed coupled Eulerian-Lagrangian (CEL) FE models. Multiple control strategies are simulated and evaluated, each differentiating how the velocity and torque setpoints of the jacking systems are calculated. From the simulation model, it is found that in order to achieve load sharing between jacking systems, torque and velocity require to be independently controlled which only the DTC method has the ability to. Furthermore, each of the jacking systems should be provided with its own power supply. Best performance and stability was achieved when each chord of the leg is given a common torque and velocity setpoint, which is equivalent to a common torque and setpoint per leg in reality. Moreover, load sharing can be improved without a control system by increasing the relative stiffness ratio between the chord and the mechanical contact between rack and pinion.

In the mission to slow down global warming, the replacement of fossil fuels by renewable energy resources is key. A tipping point in the adoption of renewable energy resources is notable, as they are becoming economically more viable. Offshore wind energy is considered essential in realizing the greenhouse gas emission reduction targets. However, high construction, installation, and maintenance costs cause offshore wind to remain in competition with fossil fuel-based energy. Therefore, further reduction of offshore wind energy costs is crucial. The most common method of wind energy cost reduction is the upscaling of nominal power ratings by increasing the size of the rotor. However, an alternative way for attaining cost reductions might be the employment of a radically different hydraulic drivetrain concept. Hydraulic-drivetrain wind turbines have the potential of lowering the construction and maintenance cost of wind farms by using a shared hydraulic network. Hydrostatic power, generated by individual wind turbines, is transmitted to a central location where electrical power is collectively generated. However, challenges arise in the control of hydraulic-drivetrain wind farms, e.g., limited pump torque controllability and increasingly complex coupled dynamics for a rising amount of employed wind turbines. Current control strategies for hydraulic-drivetrain wind farms are developed based on classical control methods. These methods become less suitable for maximizing the collective power of larger wind farms. Therefore, this thesis presents a modification of the existing convex economic model predictive control (CEMPC) framework for conventional wind turbines, such that it becomes compliant to the domain of single-turbine and multi-turbine hydraulic-drivetrain wind farms. The CEMPC method provides computational tractability by circumventing the nonlinear nature of the dynamics. A novelty in this work is that the CEMPC framework is scalable for the control of multiple wind turbines. Moreover, an additional algorithm is proposed to extend the applicability of this framework to control wind turbines containing digital displacement pumps. In a simulation study, the performances of the developed CEMPC framework applied to a single-turbine and two-turbine hydraulic-drivetrain wind farm are compared for different wind speed scenarios. In all scenarios, the proposed CEMPC framework shows its ability to adequately control the employed wind turbines. When comparing the obtained power production efficiencies with a conventional wind farm employing NREL 5-MW reference turbines, the efficiency of the hydraulic-drivetrain wind farm is 10%-17% lower compared to the reference wind farm. To reduce the levelized costs of offshore wind energy, the hydraulic-drivetrain wind farm concept has to provide at least an equivalent cost reduction over the lifetime of the wind farm. The scalability in the number of controlled turbines makes the proposed CEMPC framework for hydraulic-drivetrain wind farms a promising candidate for realizing the necessary cost reduction.

The development of offshore wind technology has progressed rapidly since the first offshore wind farm came to birth in 1991. During the last three decades, offshore wind has become an economically attractive option of renewable energy source for many countries to fulfill their commitment to combat the climate crisis. However, an open challenge remains in offshore wind, namely the aerodynamic interactions between multiple turbines, referred to as wake effects. When the upstream turbines extract energy from wind, they generate the wake with low velocity and high turbulence level on the downstream turbines, which substantially reduces the overall wind farm performance. The annual power production loss due to wake effects is generally 10 - 40%, which limits the reduction of the levelized cost of electricity for offshore wind energy. Therefore, developing effective wind farm control strategies which could mitigate the wake effect has become an emerging research area in the past decade. Recently, a novel wind farm control strategy called the Helix approach is proposed. The Helix approach adopts the individual pitch control technique to dynamically deform the wake into the helical shape, which induces wake instability and thereby stimulates wake recovery. This control strategy has demonstrated promising potential to mitigate the wake effect and to enhance the wind farm performance. To date, the Helix approach employs single harmonic pitch signals. However, more complex and higher-harmonic signals to potentially improve the effectiveness of the Helix approach have never been explored. Therefore, the purpose of this master thesis is to explore the potential of using multi-sine pitch profiles to further enhance the wake mixing. The high-fidelity aeroelastic simulator OpenFAST with its recent free vortex wake code is adopted to simulate the dynamic wake evolution. A Fourrier stability analysis is used to quantitatively identify the wake breakdown position. According to the simulation results for the multi-sine pitch profiles, the wake breaks down at 1.75 D, which is earlier than the one at 2.50 D for the original single-sine Helix strategy. The earlier wake breakdown indicates the potential of faster wake recovery, which is required to be validated by the higher-fidelity model in the future studies.

Wind energy currently is one of the most attractive solutions to help in the goal of switching to a more sustainable way of energy production. To stay competitive with other forms of energy production, the reduction of the Levelized Cost of Energy (LCoE) is an important indicator. One way of achieving this goal is by increasing the size of the wind turbine. As a result, the increased blade length also comes with a significant increase in fatigue loads present on the wind turbine’s rotating and fixed structure. Individual Pitch Control (IPC) forms an interesting opportunity in attenuating these fatigue loads. IPC is generally applied with the help of the Multi-Blade Coordinate (MBC-) transformation. The IPC control strategy uses the Out-of-Plane (OoP) bending moments measured on each blade. The MBC-transformation transforms the measured OoP bending moments towards the non-rotating reference frame. As a result the OoP bending moments are transformed into non-rotating yaw- and tilt-moments. The minimisation of these signals is then used as a control objective. Subsequently, the provided non-rotating control signals are then transformed back to the rotating domain to obtain the implementable individual pitch signals. In the literature this controller synthesis is often employed by two separately operating Single-Input Single-Output (SISO) control loops. Whereby implicitly (or sometimes explicitly) assuming that the yaw- and tilt-moments are sufficiently decoupled to make this type of control viable. In the literature, a recent frequency domain analysis has shown that the coupling is non-negligible. Literature suggests that the introduction of an offset in the inverse MBC-transformation can help decouple these yaw- and tilt-moments, although this offset is usually found in a heuristic manner and its real effects are unknown. In this study a thorough analysis on the effects of the azimuth offset is given on simplified and high-fidelity models. It is shown that the choice of blade-dynamic model structures has a significant effect on the analysis for maximum decoupling. It is also shown that a first-order model approximation is able to locate the ideal offset of a complex high-fidelity non-linear wind turbine model, which is subsequently verified by simulations and a sensitivity function analysis.