To achieve net-zero greenhouse gas emissions by 2050, the Global Wind Energy Council (GWEC) emphasizes the need for a significant expansion in global wind power capacity. A key factor of this growth is the upscaling of wind turbines, which increases the swept area and exposes the blades to higher wind speeds at higher altitudes, thereby increasing energy yield. However, this upscaling introduces significant control challenges. Larger wind turbines increase aeroelastic complexity, with stronger nonlinear dynamics arising from increased blade flexibility and more significant wind shear across the expanded rotor diameter. Additionally, the increased rotor inertia delays the system response, making rotor speed regulation more difficult under varying wind conditions.

This thesis proposes an adaptive closed-loop Subspace Predictive Control (SPC) framework designed in an attempt to handle the complexity of larger, nonlinear wind turbines. Closed-loop SPC is a direct Data-Driven Predictive Control (DDPC) method that does not rely on explicit state-space modeling, but instead uses measured input-output data to predict future outputs and compute optimal control action. For the optimal control action, it sets up a receding horizon optimization that regulates above-rated rotor speed. This thesis focuses on the above-rated region, where aeroelastic complexity becomes more pronounced due to higher wind speeds and greater wind speed variations, posing significant control challenges.

To capture time-varying and nonlinear behavior more effectively, the closed-loop SPC incorporates Recursive Least Squares (RLS). The controller adapts to time-varying conditions through online parameter estimation using RLS, which updates a locally linear model in real time. Three RLS variants are examined: standard RLS without forgetting, RLS with exponential forgetting, and RLS with directional forgetting. Standard RLS weighs all past data equally, which may be effective when the system dynamics remain stationary but limits adaptability to changing conditions. Exponential forgetting addresses this by placing more weight on recent data, improving adaptiveness, but at the potential cost of losing parameter estimation accuracy in less-excited directions. Directional forgetting refines this further by applying forgetting selectively along the directions of incoming data, preserving excitation in recently unexcited directions and enhancing estimation robustness.

To reduce the phase lag introduced by increased rotor inertia, wind preview information is incorporated into the closed-loop SPC as a feedforward signal. This wind preview is included in the receding horizon optimization problem, enabling the controller to anticipate and proactively respond to upcoming wind changes. Additionally, the wind preview is demonstrated using more realistic measurements obtained through a LIDAR simulator.

The adaptive closed-loop SPC is validated on the DTU 10 MW reference turbine using QBlade, a high-fidelity wind turbine simulator. Various wind scenarios, including gusts, ramps, and turbulent inflow, are evaluated with and without wind preview feedforward. Results demonstrate that the inclusion of wind preview significantly improves rotor speed tracking performance and reduces pitch activity. This improvement is also observed when more realistic LIDAR wind measurements are used in simulations with a turbulent wind field. In the conducted wind cases, among the RLS-based adaptive closed-loop SPC strategies, exponential forgetting combined with wind preview consistently outperformed the other RLS approaches across all scenarios evaluated in this thesis. These findings demonstrate that introducing adaptiveness through forgetting, together with feedforward wind information, can enhance closed-loop SPC performance in rated rotor speed tracking.

Floating Offshore Wind Turbines (FOWTs) pave the way to accessing deep water regions with abundant wind resources that are unreachable to bottom-fixed turbines. This technology is not widely deployed due to the increased cost of producing energy. A suitable control strategy can improve the power quality and extend the lifespan of the FOWT, resulting in a reduction of the final cost of energy. However, FOWTs face a specific set of control challenges in the above-rated operational region, such as the negative damping issue, rough environmental conditions, and increased model complexity due to both the floating structure and the increase in wind turbine size. Advanced control strategies are the most suited to resolve the negative damping problem, but obtaining a control model that balances low complexity with good accuracy is an increasingly difficult task. To mitigate the effect of environmental disturbances, feedforward control using wind preview is most commonly employed. Although waves have been shown to increase rotor speed oscillations and turbine loads, wave preview-based methods remain scarce.

This thesis implements a model-free, feedforward controller based on wave preview for the above-rated region of a FOWT. The controller uses a preview of wave forces acting on the floating platform and aims for simultaneous rotor speed regulation and platform motion reduction using collective blade pitch control. As a model-free approach, a modified Data-enabled Predictive Control formulation that considers past and future information about measurable disturbances is proposed. The controller is implemented with a linear model of the NREL 5-MW wind turbine installed on the OC3-Hywind spar-buoy platform and tested in several cases. The effectiveness of the wave feedforward data-driven controller is evaluated in a high-fidelity environment using the QBlade simulator. A decrease in rotor speed variance of 67% and platform pitching motions of 71% is obtained, at the cost of a 7-fold increase in blade pitching effort compared to the baseline controller. In turbulent wind conditions, the wind proved to be the dominant disturbance, and including both wind and wave previews in the controller is recommended. This work demonstrated the feasibility of a model-free feedforward control strategy for wave effect mitigation in FOWTs. Further efforts are required to adapt this strategy to closed-loop operation and to validate its effectiveness across the entirety of the above-rated region.

The increasing demand for microchips has driven the search for new control techniques in industry. While linear controllers dominate industrial control due to their simplicity and effectiveness, they face inherent limitations such as the waterbed effect and Bode’s gain-phase trade-offs. To address these challenges, this research explores the potential of nonlinear control, specifically reset control, to improve upon linear controllers. Reset control was used, since the sensitivity function of the reset controller can directly be constructed using the frequency response function of the plant. This thesis introduces a method for incorporating a reset based filter that improves upon linear control through closed-loop frequency domain shaping(pseudo-sensitivity).

In this study we introduce the following: an introduction and shaping method of a modified constant gain and lead in phase (CGLP) element. Which is achieved by adding gain in parallel with the generalized first order reset element (GFORE) to limit high-frequency nonlinear behavior. Furthermore a process for frequency-domain shaping of the added filter that can be implemented with an existing linear controller. Lastly, the thesis demonstrates how to solve pseudo-sensitivity constraint violations and shows the effectiveness of direct pseudo-sensitivity shaping for improving time-domain response.

Validation using both a simulated and an industrially used wirebonder, confirmed that the reset-based controller significantly outperformed optimized linear controller, with a 39.50% decrease in mean settling time and a 40.30% decrease in mean root-mean-square error (RMSE). This research shows the potential of reset control in industrial applications.

Fuel consumption reduction in Hybrid Electric Vehicles (HEV) powertrains has been an important area of research over the past few decades. HEV powertrains have two energy sources : fuel and battery. The important task of splitting the energy/power demand between both these sources is performed by the Energy Management Systems (EMS). There are many EMS methods and the focus of
this thesis is on a method called Modular ECMS (MEMS) implemented by TNO. MEMS finds the optimal power split among the subsystems by minimizing the energy loss in each subsystem. This strategy assumes that the operating speed of the subsystems of the powertrains is known and uses this knowledge to find the optimal power split and torque among these subsystems. The objective of this thesis is to find the optimal operating speed of the subsystems as well. This is done by a least squares fitting of the objective function and constraints as functions of subsystems speed and torque. A revised Optimal Control Problem (OCP) is formulated as a quadratic programming problem of speed and torque and is termed as Speed-Torque Coupled MEMS (ST-MEMS). The ST-MEMS algorithm is tested on a series-hybrid wheel loader powertrain model and its performance is compared to MEMS, with the model and data provided by TNO. It is concluded that the ST-MEMS, while adding the speed and torque bounds as degrees of freedom, does not achieve a good distribution of power between the 2 sources. The reason for this behaviour is analyzed and an alternate approachis suggested for future work.

Robot manipulators are significantly more accurate than their human counterparts and enhance the repeatability of various tasks. However, manufactures still provide reduced information regarding the robot controller functions, typically affecting robots' predictability. Additionally, industrial examples show that system transparency could be improved, helping users to understand the process and apply corrective actions. One step in the direction of improved predictability is the identification of the Limiter block associated with the KUKA robot controllers that limit the acceleration and jerk obtained during motions. This work aims to provide dynamically feasible trajectories with reduced performance deterioration. Specifically, the Limiter's behaviour was analyzed and later predicted in a iterative manner, aiming for the maximum velocity, acceleration and jerk that can be achieved, starting from the current robot state and given specific robot information. Using the predicted bounds, the trajectory generator will determine the maximum achievable point that fulfills all the constraints, running an optimization problem in an iterative fashion. Nonetheless, the practical experiments represent a significant component allowing data collection and results validation.