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.

This research paper describes the design and implementation of a Model Predictive Controller, using economic engineering principles, on a model of the US economy. The purpose of the Model Predictive Controller is to mimic the US
government policy by maximizing the Net Domestic Product. The Model Predictive Controller is integrated with a bond graph model which is modeled based on macroeconomic principles and used to simulate the US economy. In all cases, the Model Predictive Controller was able to successfully stabilize the US economy, while maximizing the Net Domestic Product e.g. the economic output of the economy. The model described in this paper provides a promising new way of generating more accurate prospects of future market movements.