This paper presents an estimator synthesis methodology that uses an optimization approach to construct estimators which minimize the vertical acceleration response of a hydrofoil craft in ocean waves. The optimal estimator structure is identified, and a fundamental tradeoff between reducing wave-following motion and the required strut height is discovered and characterized. The developed methodology is validated through its application to a scale-model hydrofoil ship, resulting in a reduction of up to 70% in vertical acceleration magnitude compared to the ship's original estimator. ... Read more

Data-Driven Predictive Control (DDPC) has emerged as a promising alternative to Model Based Control (MBC), enabling direct control using Input-Output (I/O) data without requiring explicit model identification. This thesis advances DDPC by bridging the gap between theoretical developments and real-world implementation. The study focuses on four algorithmic variants: Subspace Predictive Control (SPC), Closed Loop Subspace Predictive Control (CL SPC), Recursive Closed Loop Subspace Predictive Control (R-CL SPC), and Constrained Recursive Closed Loop Subspace Predictive Control (CR-CL SPC), to enhance adaptability and ensure real-time feasibility.

A key insight in computational efficiency is the R-CL SPC algorithm, which updates system parameters online using recursive least squares estimation combined with Givens rotations. This reduces computational complexity by avoiding large-scale matrix inversions at each time step. Additionally, the CR-CL SPC variant introduces constraint handling via a Quadratic Programming (QP) solver, enabling input and output constraints.

These algorithms’ performances were evaluated through simulation studies and real-time experiments on a piezo-actuated beam setup, where the control objective was to suppress the first two natural vibration modes. The R-CL SPC algorithm demonstrated a strong balance between computational efficiency and control performance, achieving execution times below 0.2 ms while maintaining effective vibration damping. Meanwhile, though at a higher computational cost, CR-CL SPC validated constraint enforcement capabilities.

This thesis demonstrates that adaptive SPC algorithms can be successfully implemented on real-world hardware. The results contribute to the growing knowledge on direct DDPC strategies and provide a foundation for their broader application in real-time, constrained control systems. ... Read more

Haptic technology focuses on the recreation of haptic information, i.e., a type of sensory input that uses tactile cues, forces, vibrations, or pressure to provide users with the sensation of touch, enabling users to interact physically with virtual or remote environments. One promising application of this technology lies in haptic training, where the possibility of using haptic feedback to facilitate or promote motor learning is studied. The focus of this paper lies on performance-enhancing haptic training methods, with a focus on designing a dynamic motor task. The objective of this paper is, therefore, to establish a preliminary framework that can be used to provide minimal haptic feedback while flying a quadcopter through a set of gates.
We focused on creating a preliminary framework that provides haptic feedback on the altitudinal axis of the quadcopter to the pilot using the control method Model Predictive Control (MPC). The haptic feedback is provided on the z-axis of a haptic Sigma.7 robot, which is also used as a remote controller to fly the quadcopter. The MPC implements the dynamical models of the quadcopter, and a haptic Sigma.7 robot, to determine the minimal force required to steer the Sigma.7 robot towards motor task completion. The system should provide minimal haptic force feedback within the proposed design requirements to prevent reliance on the assistance. We evaluated the effectiveness of our framework by evaluating its ability to control the quadcopter to the desired altitude setpoint under autonomous conditions using a haptic Sigma robot. Additionally, the design and performance of each of the individual building blocks of this framework, i.e. the quadcopter model, the haptic interface, and the MPC, were evaluated separately. The quadcopter, with the implementation of the onboard PID controllers, eliminating the steady-state errors and meeting the required settling times. The Sigma.7 model was sufficient within the established time horizon and range of operation, although shows limitations due to unmodelled frictional forces. The completed framework is capable of providing the Sigma.7 with the necessary input command to autonomously guide the quadcopter to its desired references in real-time, therefore completing its primary objective. Future work should explore improving the model components and integrating human elements into the predictive model. ... Read more

This thesis investigates how alternative fuels and energy reduction technologies influence the technical and economic viability of research vessels under market uncertainties and varying operational profiles. The study addresses the growing need for decarbonisation in the maritime sector, where research vessels face particular possibilities due to limited regulatory requirements and constraints due to their demanding mission requirements.

Research vessels, which are exempt from many regulatory emission frameworks, operate under highly variable mission profiles that challenge conventional decarbonisation approaches. The review identifies a significant gap in existing studies, which typically overlook the unique operational demands of these vessels.

The literature analysis evaluates a wide array of fuels - including fossil based fuels with lower carbon intensity such as LNG and LPG, renewable, diesel like fuels as HVO, hydrogen carriers as ammonia, hydrogen, and sodium borohydride, alcohol fuels as methanol, as well as metal-based fuels like iron powder -, wind assisted propulsion systems, and energy reduction methods as exhaust heat recovery and solar systems. Assessment is done on physical and chemical properties, emissions, safety, technological readiness and availability, and costs.

To handle the complex and uncertain decision environment, the study proposes the Many Objective Robust Decision Making Framework combined with an Epoch-Era Analysis that models the most important uncertainty, namely the various operational profiles. This methodological foundation allows for evaluating the technical and economic feasibility of many different propulsive combinations across a wide range of plausible futures.

The subsequent analysis shows that no single configuration is universally optimal across all conditions. Fossil and diesel-like fuels such as LNG and HVO remain technically feasible but offer only slight emission reductions. Methanol-ICE configurations emerge as the most robust low-carbon option, offering technical feasibility across all scenarios and significant emission reduction potential. Ammonia-ICE solutions perform well under lower requirements and can approach carbon neutrality if sustainably produced. The integration of energy reduction technologies such as exhaust heat recovery and wind-assisted propulsion improves performance, but effects remain context-specific and do not fundamentally alter the main trade-off’s between cost and emissions. The analysis further shows that blended fuels (e.g., grey/green methanol or ammonia) can serve as transitional pathways, enhancing economic viability while preparing vessels for a green fuel future. A design-oriented iteration of the MORDM indicate that hull form adjustments can improve robustness, however, more detailed calculations need to be done.

In conclusion, the findings underline that future-proof research vessels will need to adopt technically feasible, robust fuel strategies that enable compliance with long-term climate goals. Methanol, and to a slightly lesser extent ammonia, currently offer the most promising pathways, while fossil and diesel-like fuels cannot ensure sustainability under future conditions. ... Read more

In recent years, the yearly share of electricity generated in the Netherlands from renewable sources such as solar and wind has increased significantly, reaching 42% in 2023, and is projected to rise to 70% by 2030. However, production from these sources is highly weather-dependent, unpredictable, and often misaligned with typical periods of peak electricity demand. This mismatch leads to price volatility in electricity markets, emphasizing the importance of a reliable trading strategy for assets with flexible production or demand. Solar panels are partially flexible assets, as their production can be curtailed. However, their output is weather-dependent and cannot be predicted with complete accuracy. This creates a challenge in determining the trade volume in the day-ahead market, resulting in the highest profit. Trading conservatively in the day-ahead market reduces revenue but minimizes imbalance volumes through the possibility of curtailment. On the other hand, trading too much can result in unavoidable imbalances when actual production falls short. Existing literature focuses on minimizing imbalance volumes as extreme prices, high volatility, and lower average prices than day-ahead prices characterize the imbalance market. In these works, it is typically assumed that the real-time imbalance price is unavailable. However, real-time imbalance price predictions are available in this research, enabling optimal real-time decision-making. This provides the opportunity to profit from high imbalance prices while avoiding negative prices. In this study, a coordinated bidding strategy optimizes day-ahead bids, balancing revenue maximization and risk minimization as both the production and imbalance price are uncertain. The primary objective is to explore methods for incorporating uncertain imbalance prices into day-ahead optimization. Various methods from the literature are compared, and a novel decision-focused approach is introduced. Combined with solar generation forecasts, state-of-the-art day-ahead price predictions, and optimization models, monthly revenues are simulated using \ac{EMS} software. Results show that modeling uncertain imbalance prices using historical scenarios achieves the highest and most consistent revenues, especially when combined with \ac{CVaR} optimization. The novel decision-focused approach also performs among the best models, delivering consistently high revenues. Adding battery storage to the solar panels yields similar results, and further revenue increases are possible with improved solar forecasting. This highlights an important direction for future research. ... Read more