The placement of wind turbines in wind farms is economically beneficial to the industry. Consequently, the wake generated by the upstream turbines introduces interactions with downstream turbines that significantly affect power generation. However, it is common practice to control the individual turbines to operate at their optimal settings and neglect the wake effect. Turbines in a wake experience lower wind speeds and an increase in turbulence. This results in lower power generation and increased fatigue loads on the downstream turbine. Wind farm control aims to increase overall power generation and reduce fatigue loads by considering all turbines in the wind farm. Recently, numerous studies have been conducted to control the wake itself. The helix approach is one such strategy and seeks to initiate better mixing of the wake. For this, it uses IPC to create sinusoidal blade bending moments acting on the rotor disc. This in turn creates a helical structure in the velocity of the wind, allowing it to better mix with the surrounding energy-rich wind. As a result, the waked downstream turbine experiences an increase in power due to the reduced velocity deficit in the wake. An extensive load analysis of the helix method has shown that the rotational frequency of this helical wake can be measured in the bending moments of the waked downstream turbine. To further develop the field of dynamic wake mixing, this work investigated the initialization of the helix method in a downstream turbine by amplifying these signals through synchronization with the incoming helical wake. This method was tested in simulation environments with increasing levels of fidelity and number of turbines simulated. The results with low fidelity showed that synchronization could be achieved without loss of performance. In addition, less pitch actuation was required to generate large bending moments due to amplification. The final experiment was conducted for a three-turbine setup and was performed in a high-fidelity model using CFD methods. Here it was found that -90 degrees out-of-phase synchronization with the incoming helical wake resulted in a 9.8% increase in power generation in a turbine further downstream. These results demonstrate the clear potential of wake synchronization in a multi-turbine setting. This study, therefore, contributes to the field of dynamic wake mixing by introducing a novel field of wake synchronization.

Due to an increase demand for renewable and sustainable forms of energy, the offshore wind industry is growing rapidly. To make wind energy more cost-effective, new wind turbines are becoming larger in size and are placed further offshore. This results in deeper waters, making the installation more difficult. The current methodology uses a jack-up vessel, butt will not be capable of installing the next generation of XXL wind turbines. The legs will simply be too short and the monopile too heavy. Floating vessels with a dynamic positioning system are believed to be the solution since they are able to operate in deeper waters and are assumed to be more time efficient. The foundation of a windturbine needs to be installed within an angle of tolerance of 0.25°. In order to control the position of the monopile during the installation, a Motion Compensated Gripper Frame can be used. This research focuses on the control design of such a gripper frame. The goal is to design a robust multi-model controller for the gripper frame which will guide the monopile during the installation. The controller should minimise the angle output of the system, the used control force and the resulting force which is experienced by the vessel. One of the main challenges is the unknown soil stiffness. This model parameter has large effects on the system dynamics and will show large uncertainties during installation. The robust stability property is shown for the designed cascade controllers. The implementation of these controllers in a multi-model controller show stable results. The fast inner-loop measures the position of the gripper and is able to reject any vessel motion disturbance. The slower outer-loop compensates for angle deviations of the monopile. The optimal controller is activated for the different operating points using gain-scheduling. Compared to gain-scheduling with two controllers, three controllers reduce the mean resulting force on the vessel with 50% and the total required power by 0.06% during the installation. But the deviations of the angle of the monopile increases with 5% but does not violate the set constraints. The results show a stable system for all the changing degrees of soil stiffness. The developed method makes it possible to install the next generation XXL wind turbines in deeper waters, and replaces the current limited method which uses jack-up vessels. Using the designed cascade multi-model controller will result in a robustly stable controller which minimises the used control forces and power. Which in the end minimises the costs of this gripper construction and making the wind-industry even more cost-effective.

Offshore Wind Turbine (OWT) structural design can be optimized to reduce structural costs, thereby lowering wind energy costs. Increasing turbine size and further structural cost reduction require more advanced insights into the dynamic system. One practice of analysing these vibrating structures is called Operational Modal Analysis (OMA). OMA estimates parameters of a structural dynamic model of which damping is considered especially important due to the direct relation with the magnitude of the response. Although OMA is an established field, damping estimation of operational Offshore Wind Turbines is often difficult due to violation of the stationary white noise excitation condition posed by classical OMA algorithms. Rotor rotation causes a harmonic loading on the non-rotating part of the structure, which is problematic for conventional methods. Harmonics-mitigating OMA methods were developed in recent years to tackle this issue. Four state-of-the-art OMA algorithms are selected to identify the damping of an operational OWT. Each algorithm is applied to a dataset obtained from a simple simulation test case and a wind turbine simulation before considering field measurements. Subsequently, algorithm robustness is examined by comparing estimates from multiple measured datasets with corresponding simulations and estimates from different algorithms for multiple wind speeds. It was found that the first mode corresponds well with the model for wind speeds under 15 m/s, whereas the second mode frequency has a large offset from the modelled value, revealing modelling flaws. Moreover, the estimates of the Modified Least-squares Complex Exponential (LSCE), PolyMAX, and Kalman Filter-based Stochastic Subspace Identification (KF-SSI) algorithms correspond well for both measured and simulated data. They are well-suited for identifying the damping of operational OWTs. Cepstrum editing is not recommended for damping identification as the editing affects the damping value. Enhanced Power Spectral Density Transmissibility (PSDT) is not recommended when few sensor levels are available. Novel enhancements were developed in this thesis. First, the results of the KF-SSI algorithm were improved by concatenating datasets in the identification steps and reducing variance. Also, numerical issues were addressed by implementing the Square Root Covariance Filter (SRCF) instead of the conventional Kalman filter. Other developments include an algorithm for automated interpretation of stabilization charts and a harmonic localization method based on the rotor velocity.

To accelerate the growth of offshore wind power, its Levelised Cost Of Energy (LCOE) must be reduced. A means of reducing the LCOE of offshore wind power is by mitigating the adverse wake effect by employing Wind Farm Control (WFC) for power maximization. A recent WFC technology is the helix approach, which uses Individual Pitch Control (IPC) to initiate early wake mixing. Research regarding the helix approach is scarce, yet early results suggest that significant power gains are achievable. Simultaneously, increases in wind turbine loads are expected. To aid future researchers in making a trade-off between increased loading and performance, an extensive load analysis is conducted, aiming to analyze and quantify the load impact of the helix approach on the SG 11.0-200 DD offshore wind turbine. A sensitivity analysis is conducted to investigate the influence of the helix parameters on the load impact on the wind turbine tower, blades, main bearing and pitch bearing. It is found that the load impact on all components is mostly sensitive to the pitch amplitude. Most components show some sensitivity to the Strouhal number as well, except for the main bearing. For the blade pitch bearings, an upward and downward trend can be observed for Counter-Clockwise (CCW) and Clockwise (CW) helix directions, respectively, which are related to the pitch frequency. The helix approach is part of the plans for the Hollandse Kust Noord (HKN) wind farm. For this reason, a lifetime fatigue analysis is conducted to quantify the load impact on a wind turbine at the HKN wind farm over its full lifetime for several helix implementations. The fact that the helix approach is only employed under certain conditions is taken into account. It was found that the load impact on the pitch bearings is significant. The load impacts on the tower and main bearing become considerable for helix implementations with a large pitch amplitude. All other components are mildly affected. Finally, the load impact of the novel concept of Closed-Loop Helix Control (CLHC) is compared to conventional helix. This approach considers asymmetric loading on the rotor (caused by e.g. wind shear and tower shadow) when employing helix. Results found that CLHC reduces blade loads with respect to the helix approach. However, since the blades are mildly affected by the helix approach in general, choosing CLHC over conventional helix is not imperative.

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.

Infant mortality is a significant problem in mooring design. The objective of this thesis is to design a generic fault detection system for mooring line failures, which can accurately detect a mooring line failure within a certain time frame. The research is done through Catenary Arm Leg Mooring (CALM) buoy systems, as this thesis is a Proof of Concept. Two types of mooring line failures are considered in the XZ-plane, a top segment failure close to the buoy and a bottom segment failure close to the anchor. Both failures characterize themselves by a constant offset in equilibrium position and a changed mooring stiffness. Using theoretic concepts about mooring configurations and waves, it is shown that the shift in equilibrium position has most potential for detecting a mooring line failure. These analysis are verified by hydrodynamic simulations in OrcaFlex. For the bottom segment failure the breaking stage cannot be predicted, as this stage depends on the mooring design, the external loads, and the location of the failure. The focus must lie on what stage the failure becomes detectable, rather than how long it takes. The fault detection algorithm is using a model-based approach for fault detection as a general framework which can be used for any variation in mooring design. Current flows, tankers mooring at the buoy, and non-linear effects of mooring configurations in waves cause a constant offset in position as well. By including measurements of the current flow velocity and the tanker hawser tension as inputs of the CALM buoy model, the fault detection algorithm can distinguish between the sources of the constant offset. Additionally, the non-linear effects of a mooring configuration in waves are for small wave heights not significant and not included in the modeling of the buoy. The mooring tensions and system parameters are introduced as specific parameters per mooring design. Via an Unknown Input Observer (UIO), the two unknown input loads are estimated: the wave load and the residual force. The residual force is chosen to represent the mooring line failure in the model. When there is no mooring line failure this force should be zero. The two unknown input forces are estimated by decoupling them in the frequency domain. Using a shaping filter the wave forces are restricted by a predefined frequency bandwidth. The non-linear effects of a mooring configuration in waves are determining the thresholds for the fault detection algorithm. Therefore, no wave load can be mistaken for a mooring line failure. The load cases show that for small offsets the fault detection algorithm is capable of accurately detecting the mooring line failures. The two unknown forces are decoupled in a way that the residual force can be used as residual for fault detection. The residual signal in RY-direction can be used for fault detection. Additionally, it is shown that no step in position data is required for the algorithm to detect a failure. The answer on the research question, is yes. It is possible to accurately detect to accurately detect a mooring line failure. Since this thesis is only applied for the three Degrees of Freedom (DoF) in the XZ-plane, a six DoF model needs to be investigated in future research. Furthermore, by including the non-linear effects in the modeling of the CALM buoy, higher wave amplitudes could be considered. Another possible way to deal with these non-linear effects is to use adaptive thresholds.

The economic engineering group at the DCSC uses Newtonian and analytical mechanics to model economic systems but makes no use of thermodynamics. The introduction of thermodynamics to the economic engineering framework would increase the extent of analysis and interpretation of economic systems in economic engineering. In this thesis the foundations of the thermodynamics of economic engineering are developed in order to include thermodynamic modeling of economic systems in the field of economic engineering. After the development of the thermodynamics of economic engineering theory, the theory is applied to analyze economic growth, factor productivity, and the value of a business. An axiomatic approach is taken to derive economic analogs to thermodynamic concepts. The meaning of an axiomatic approach is that these economic-thermodynamic analogs are developed in a logical order, e.g., the economic analog to temperature is not introduced before developing the economic analog to the first law. Key analogs between economics and thermodynamics are established in this thesis that include but are not limited to two fundamental economic laws, work as an expenditure, heat as an expense, temperature as a price level, and entropy as a quantity referred to as human capital in the thesis. By deriving key analogs, the thesis establishes the foundational principles of thermodynamics within economic engineering. Utilizing the theory of the thermodynamics of economic engineering results in applications to economic growth. Empirical growth accounting is modeled by the fundamental thermodynamic relationship. A relationship between linear production functions and Cobb-Douglas type production functions is shown by analyzing the productivity of an economy. Furthermore, a method to evaluate the worth of a business is created by determining the business' total potential earnings. Additionally, the thesis shares a vision of how to include thermodynamics within a control engineering framework. A higher-dimensional energy-based approach to model dynamical systems offers a way forward to include thermodynamic energy and entropy within control formalisms. Such a framework would account for availability and heat buildup in controlled dynamical systems. One potential application is to account for the heat build up that occurs in integrated circuits.

Problem: Due to high rejection rates regarding prostheses’ use, the assessment of the amputee’s use of the prosthesis has become more critical. Today’s prosthesis research is limited to assessing a users’ performance to perform tasks in a controlled environment. Therefore, these studies cannot wholly assess how the prosthesis is used in the daily lives of amputees. Purpose: The purpose of this thesis project is to create, test, and examine the performance of methods that can be used to enhance prostheses' research and evaluation. Results: We have created several enhancements and extensions regarding prosthesis research and evaluation for three sensor scenarios. In the first scenario, we only use an accelerometer, which allowed us to create a new method for creating a vector magnitude (VM) that can show us what type of arm movement is made; additionally, we created a novel scoring system to determine the intensity of movements that are performed. In our second scenario, we used an additional gyroscope, which opened up possibilities for using more advanced Sensor Fusion (SF) techniques. We created an accurate tilt estimation algorithm that remains robust against high levels of gyroscope noise, accelerometer outliers, and measurement model violations. With the gyroscope, we were also able to create a VM from rotational velocity, which displays information about the arms’ rotational movement. Additionally, we created a novel scoring system that also shows the intensity of the performed movements. Our last scenario considers the use of two Inertial Measurement Units (IMUs); we present a novel algorithm for estimating the relative sensor orientation. This algorithm uses a joint kinematic estimation method that incorporates the connection between adjacent segments within a SF algorithm that remains accurate in the vicinity of common real-world disturbances, among which are; high levels of gyroscope noise, accelerometer outliers, and Soft-Tissue-Artifacts (STA). Conclusion: The best way to enhance the analysis for prostheses research and evaluation is to start incorporating gyroscopes into the research process. This can either be in the form of the single sensor case or the double sensor system. The additional gyroscope(s) will enable the use the SF techniques and methods as discussed in this report.

Maritime piracy has been a problem for the last decades and peaked in 2010. This resulted in large investments in anti-piracy measures, but as piracy waned over the following years, so did the investments in anti-piracy measures. This could be an indication that the stage for maritime piracy has been reset. This report suggests a more efficient and cheaper approach to counteract maritime piracy on merchant vessels indefinitely. SeaState5, the company at which this research is done, came up with the idea to combat pirate attacks on merchant vessels using a Fast Rescue Craft (FRC). Merchant vessels are obligated by law to carry an FRC; used for instance for man over board missions. SeaState5 focuses on developing an add-on plug and play system compatible with existing FRC. It turns these sole-purpose rescue craft into a multi-purpose craft, in this research referred to as the Beagle. This Beagle will serve both rescue and anti-piracy purposes. In this report the concept development of this add-on system is established. This is done by developing a simulation in MATLAB and Simulink that mimics a pirate attack on a merchant vessel. During the pirate attack the Beagle uses a defense strategy to repel the pirates. A Beagle defense strategy is a combination of a non-lethal weapon and a set of parameters that define the trajectory of the Beagle. Different Beagle defense strategies are subjected to different pirate attack scenarios to find the most effective one. The simulations resulted in a list of requirements for the Beagle. A check has been done to prove the feasibility of equipping an existing FRC with the add-on system meeting these requirements.

Currently there is strong interest in the deployment of renewable energy sources such as wind, solar and hydro energy. This is driven in part due to the negative consequences of burning fossil fuels, such as health issues and climate change. To optimize the energy extracted in a wind farm the turbines require some control algorithm. Today, wind turbines that are part of a wind farm do not take neighbouring turbines into account when determining their control settings. This results in greedy control, where each turbine tries to align itself with the dominant wind direction and optimize its energy production using generator torque control and pitch control. During operation, each turbine creates a volume of slow-moving turbulent air behind its rotor, which is called a wake. The issue with greedy control is that a turbine does not take into account where its wake will end up with respect to downwind turbines. By misaligning a turbine with the wind direction its trust can displace the wake. This will cause an efficiency loss on the misaligned turbine, but it can be used to increase the efficiency of a downwind turbine, leading to an increase in net power production. We know that a controller that uses yaw optimization can optimize the power production in a small scale experimental setup [Campagnolo et al., 2016]. In this particular research a line of three turbines produced 15% extra power compared to greedy control. Wake redirection can be thought of as an attempt to efficiently mix the slower moving air in the wake with the faster stream surrounding it. This allows more of the total energy in the free stream to be extracted by any given wind farm. Yaw redirection has the potential to mix in the high velocity air that could otherwise pass through the empty space between the turbines. Tilt redirection has the potential to more efficiently mix the high velocity air that would otherwise pass over the wind farm into the air that hits the rotors [Annoni et al., 2017]. This work focuses on the development and implementation of a wake model that can be used to predict the effects of rotor tilting on the wake. We started with the FLOw Redirection and Induction in Steady state (FLORIS) model as described in [Gebraad et al., 2014]. This model can be used to estimate the power production of a wind farm. It models the wake intensity and position and combines wakes when they overlap. The power production and wake characteristics of each turbine are predicted using three sub-models. There is one model for the wake intensity and its velocity profile, one to estimate wake deflection and one to determine how wakes are added to each other. In current practice, the wake deflection is modeled only in a horizontal plane, it can be driven by rotor yaw. In this work, the deflection model will be extended in such a way that the effects of rotor tilting on the wake position can be modeled. Tuning a reduced order model such as FLORIS is notoriously difficult. In the FLORIS model, as described in [Gebraad et al., 2014] there are twelve hand tuned parameters. An important part of this research was attempting to simplify the tuning problem by making a robust parameter tuning procedure. A sensitivity analysis of the tuning parameters on the predicted power signals by FLORIS was performed. This analysis was used to identify situations where a subset of the model parameters is responsible for the variance in the predicted power production. Such situations with specific sensitivity were identified, but I lacked time and resources to fully leverage these and accurately tune the tilt extended model. We stuck with the original nominal parameters of the different parts constituting FLORIS to conduct a case study. The case study compared predicted power increases by FLORIS with high fidelity simulations in two different Large Eddy Simulation (LES) packages. The case study was conducted by trying to optimize the power production of a small wind farm containing six turbines. The wind turbines were positioned in a two by three grid and the wind direction was aligned along the three turbines. The FLORIS model was used to optimize the yaw angles, tilt angles and both of them simultaneously for this layout. This led to four sets of control settings, the baseline case with greedy control and three optimized sets. We found that the FLORIS model strongly overestimated the power gain caused by turbine tilt. The main reason for this over-estimation seems to be that FLORIS currently has no implementation of the ground. In effect, the wakes in FLORIS can simply disappear into the ground. The case where only the yaw angles are optimized matched the high fidelity simulations to a higher degree confirming the work done in [Bastankhah and Port´e-Agel, 2016]. In conclusion, tilt control seems to have the potential to extract more energy from a certain atmospheric region. This is postulated because the air that gets forced on to rotors using tilt control would otherwise have passed unused over a wind farm. However, the model proposed in this thesis is insufficient to analyze the possible energy gains because it overestimates the effect of turbine tilting. This biggest problem with the FLORIS model is that it lacks a method for modeling the interaction between the ground and a wake. For future research the main priority should be implementing a solution to that problem.

This thesis presents the development of a contactless sensing system and the dynamic evaluation of an air-bearing based precision wafer positioning system. The contactless positioning stage is a response to the trend seen in the high-tech industry, with the substrates becoming thinner and larger to reduce the cost and increase the yield. With contactless handling, it is possible to avoid damage and contamination. The system works by floating the substrate on a thin film of air. A viscous traction force is applied on the substrate by steering the airflow. A cascaded control structure has been implemented to the contactless positioning system, where the Inner Loop Controller (ILC) controls the actuator which steers the airflow and the Outer Loop Controller (OLC) controls the position of the substrate by controlling the reference of the ILC. The dynamics of the ILC are evaluated and optimized for the performance of the positioning of the substrate. For the OLC a linear charge-coupled device (CCD) has been implemented as a contactless sensing system. The sensing concept is implemented in the contactless actuation system and the results are presented.

Maximizing the extraction of energy from wind farms with ever higher densities is becoming increasingly more important in order to achieve climate targets and simultaneously preserve nature. Improving the yield of a wind farm can be achieved by optimizing the layout, applying control, especially wake steering through yaw control has shown great results, or even combining the optimization of the layout and control into one joint optimization. In this thesis, a case study is performed on the Dutch wind farm ’IJmuiden Ver’ to investigate the real-world applicability of joint optimization. The employed method uses the genetic algorithm, capable of handling the discontinuous domain, and an improved version of the geometric yaw relationship, making coupled or nested optimization redundant. In the IJmuiden Ver case, the levelized cost of electricity (LCOE) of a joint optimized layout compared to a sequential optimized layout is around 0.3% better, even remaining around 0.2% to 0.3% better when shrinking the domain to give nature more space. This shows that joint optimization is applicable in practice and has the potential to increase the yield of a wind farm substantially without significantly increasing the computational intensity of the wind farm layout optimization problem (WFLOP).

As the world is shifting away from fossil fuel-based electricity production, mainly due to concerns for man-made climate change, a sharp rise in demand for wind powered electricity production is seen. In order to meet future demand, the vast amount of off-shore wind resources can be unlocked. A key technology for this task are floating wind turbines (FWTs), as they are less constrained by water depth than fixed wind turbines. One challenge for FWTs is sometimes referred to as ‘negative aerodynamic damping’: interaction between the blade pitch controllers and the fore-aft motions of the floating platform (pitch and surge) can cause instabilities leading to large oscillations in platform motion and rotor speed. These interactions translate to right half-plane (RHP) zeros, limiting the system bandwidth and thereby the performance of the wind turbine. This increases the variability in the power output of the FWT. This thesis looks into a promising type of non-linear control to overcome this limit: reset control. Reset control is characterized by a higher phase compared to linear controllers. This could be used to improve the stability margins of the system and/or to increasethebandwidth. ThisthesisdesignsaCgLpcontrollerspecifically[1]. UsingtheCgLp, a theoretical improvement is seen in the frequency domain. Therefore, a better performance is expected. However, after time domain simulations, it is shown that this controller is not fit for application in blade pitch control. Two different configurations of the CgLp are considered. In the default configuration, large peaks are present in the control signal, leading to excessive motions of the blades. The second configuration performs well under idealized circumstances, but with high frequency disturbance (such as turbulent wind or sensor noise), excessive amounts of resets prevent the controller from reacting appropriately.

Computer vision has been on the rise over the last decades. The applications of computer vision are mostly found within the domain of robotics and Unmanned Aerial Vehicle's. The use of computer visions in precision industry is new and offers both opportunities and challenges. In high performance precision systems the demands on sensors is set high in terms of precision, delay and sample rate. In previous work a computer vision sensor was developed for the control of a microscopy stage. This development opened a path for computer vision sensors in precision systems. However, the limitations of computer vision sensors are challenging in the domain of control. A computer vision sensor needs to process an image before it can estimate the position of an object. This processing requires time which introduces a time delay into the control system. Due to the nature of the computer vision algorithm and computing devices, the delay in the sensor is time varying. Furthermore, since a controller typically requires a tenfold sample rate of the control bandwidth the limited frame rate of computer vision sensors imposes additional challenges. In previous work, the delay and limited sample rate of the computer vision sensor limited the control bandwidth of a microscopy to 0.87Hz in translational range. Since high performance precision systems require higher bandwidths a new computer vision sensor is developed in this thesis. An existing microscopy stage is used to test the performance of a computer vision sensor. Ground vibrations in a laboratory environment have a peak value at 10Hz which defines the target bandwidth. In order to reach this target bandwidth a new computer vision sensor is proposed. The sensor uses a cost-effective camera and a computer to run the computer vision algorithm. By decreasing the delay from 60ms to 10ms a control bandwidth of 7Hz, using PID, is obtained in this work. Since the target bandwidth is 10Hz time delay compensation is considered to improve the performance. By implementing a Filtered Smith Predictor the target bandwidth of 10Hz is reached. The maximum control bandwidth of the controller, 15Hz, was derived in order to inspect the limit of computer vision sensors in precision systems.

Wind farm control is an area of research that looks to maximise the performance of individual but most importantly the collective performance of wind farms. The performance of turbines located in a wind farm is heavily dependent on the effects of turbine wakes, and improving the efficiency of wind farms by mitigating the wake effects is an ongoing and promising field of research. Simulating the wake effects of a turbine is generally done with computationally expensive physics based modelling techniques like CFD simulations. The computational complexity of these models limits the scope of optimisation that can be performed on wake mitigating controls strategies such as dynamic induction control. A substitute engineering model like the free vortex wake model could provide better insight into dynamic induction control by virtue of its computational efficiency allowing a wider optimisation parameter space. In this thesis an optimisation framework based on a 2D implementation of the free vortex wake model and genetic algorithm optimisation is used to investigate dynamic induction control for a simple two turbine wind farm beyond the research that has been done up to this point. While yet to be confirmed with higher fidelity simulations, initial results corroborate earlier findings for simple dynamic induction signals and additionally indicate that dynamic induction control could benefit from multiple harmonics. The optimisation framework achieves its goal in allowing a wider parameter space to be searched for optimisation, even on consumer grade desktop hardware, and shows potential as a tool for further investigation of dynamic induction control.

Ocean Thermal Energy Conversion (OTEC) is a technique to generate electricity by using the thermal gradient available in the ocean to drive a heat engine. In order for OTEC to become successful it is important to reduce the levelized cost of electricity. The development of an effective control system can aid in this endeavour, by making sure that the plant is always operated at maximum capacity. Theoretically, the optimal control inputs can be determined offline by using models. However, due to modelling assumptions, this method can result in a suboptimal operation of the plant. Besides, performance of the plant varies over time as a result wear and tear, fouling, and fluctuating ocean water temperatures. Comparable obstacles are encountered in maximizing the electricity production in wind turbines. A promising solution in this field is Extremum Seeking Control (ESC). ESC is a model-free control strategy used for the real-time optimization of an unknown objective function. In this study, ESC has been implemented on a room-sized OTEC test-setup, called the OTEC-demo. Single parameter as well as multiparameter ESC algorithms are tested, and are shown to be successful in automatically finding the set of inputs for which the power output of the plant is maximized.

Data-driven control approaches have been proved highly effective in applications where the dynamics of the system are unknown. The reason is that the use of data in control overcomes the challenge of parametric modeling which requires effort and often leads to an insufficient description of the system’s behavior. By contrast, the behavior of the system can be captured more accurately when input-output data is used. In this project a frequency-domain approach is proposed for robust controller design for controllers of pre-specified structure. Specifically, an existing technique is extended so that it guarantees not only nominal, but also robust performance. This is achieved by ensuring performance for multiple frequency-domain models acquired from data. The related computational efficiency in the overall approach is also a fundamental criterion and thus we propose a series of techniques by which it can be improved. Additionally, a major contribution of this work is an iterative algorithm that minimizes a linear approximation of the H infinity norm of the closed-loop system. Last but not least, a method to optimize the linear constraints of the optimization scheme is proposed.

Within this thesis an attempt is made to improve the seakeeping of a Service Operation Vessel (SOV) by means of an active anti-roll tank (ART). It is researched if the addition of a wave prediction system can improve the ART’s performance. As the seakeeping of the SOV is most important for the installed motion compensated gangway, the workability is assessed for different control methods based on the design criteria of the gangway. The computations have been made in frequency domain. A one directional model is used with two degrees of freedom: the ship roll angle and the tank’s fluid angle. Due to the presence of a wave prediction system, a feed forward loop can be introduced in the control system. It is found that if the pump moment leads the wave excitation moment, the tank’s fluid will oscillate in a less power demanding way. This is caused by the better interaction of the action and reaction forces that are caused by the pump. The action force on the ship’s structure will always oppose the wave moment, but the force acting on the tank’s fluid will only stabilise the vessel at certain frequencies. To obtain a robust model, the feed forward loop is combined with a feedback controlled model. The stability of the models’ feedback loop is assessed using Nyquist plots and sensitivity plots. It is found that for the damping factor belonging to the ART of the SOV, the feedback loop is unconditionally stable. On topic of the powers related to the active ART the dissipated and actual power are analysed. Based on the dissipated powers it is found that for the same amount of dissipated power by the pump, the feed forward controlled model causes a lower root mean square value of the ship’s roll in comparison with the feedback controlled model. This is explained by the better interaction of the forces generated by the pump. Based on the obtained RAO for the roll motion, the workability of the SOV is assessed for a significant wave height of 3m. It is found that even though the seakeeping of the vessel increases because the roll response decreases, the gangway motions aren’t significantly affected by the active control of the ART. The reduction in roll response is not proportional to the reduction of the gangway motions since other vessel motions such as heave and sway also effect the gangway’s motions. All in all, it is concluded that adding a feed forward loop to a feedback controlled model improves the seakeeping of the SOV. However, to improve the workability of the SOV it is recommended to include a passive ART in the design, instead of an active ART. This is because the active control has little impact on the gangway’s motions compared to the passive ART.

A conventional dynamic positioning system is used to control vessel motions in the horizontal plane, i.e. surge, sway and yaw. However, the safety and operability of several offshore operations can be increased when also the roll motion is actively controlled, especially in beam seas. A control system model is proposed for combined thruster induced roll reduction and DP station keeping when the vessel is operating in beam seas. The proposed control system model is applied to a offshore construction vessel and the performance is demonstrated by time domain simulations. The DP footprint is compared to a conventional dynamic positioning control model. The proposed control model enables active roll reduction while the effect on the station keeping performance is unaffected.

Making offshore wind energy more cost competitive in comparison to fossil-fuel based production, is vital to maintain the direction the European Union has taken in renewable energy. Increasing the lifetime of a turbine can play a big role in driving down the overall costs of energy. The more energy the turbine is able to produce in its lifetime, the lower the costs per MWh will be. Fatigue damage is one of the limiting factors in a turbine’s lifetime. These damages are inversely proportional to the damping ratios and as such, estimating these ratios accurately, allows for optimisation of the structural design as well for improving control algorithms. Modelling software is used to estimate the modal properties, such as damping ratios, in the design phase of a turbine. However, these modelled properties often have a mismatch with reality due to differences in material properties, soil characteristic and others. Design based on these mismatched properties can lead to suboptimal control and a decrease in lifetime of the turbine. In order to eliminate this mismatch, it is of importance to accurately obtain the modal properties from the real turbine. System identification can play an important role in this. Using measurement data obtained during idling and operation of the turbine, the modal properties can be identified. When using measurement data, the danger of over-fitting is always present however. Often, a tradeoff needs to be made between the variance and bias of the estimation. To protect against ill-conditioned data matrices, as well to better deal with the variance-bias trade-off, regularisation can be added to the identification algorithm. The first goal of this thesis is to successfully identify the modal properties of the first tower modes and first coupled drive-train mode of a real turbine. Secondly, the effect of adding regularisation will be examined on the estimation of these modal properties. The optimised Predictor Based Subspace Identification algorithm will be used for identification. This will be extended to include Tikhonov regularisation, truncated SVD regularisation and nuclear norm regularisation. The performance of these techniques are compared in two case studies, after which one is selected to be used on the measurement data from the turbine. What follows is the estimation of modal properties from the turbine and evaluation of the results for both with and without added regularisation.