Diagnosing nozzle faults in high-end industrial printers, such as the ones developed by Canon Production Printing (CPP), remains challenging due to the interplay of fluid dynamics and mechanical actuation. These systems rely on self-sensing signals that are often subtle and nonlinear, complicating both detection and interpretation. However, accurate and timely diagnosis is essential to maintain print quality, minimize waste, and reduce maintenance effort. This thesis investigates hybrid fault diagnosis methods that integrate model-based and data-driven techniques to improve detection reliability and generalization, particularly for piezoelectric inkjet systems. Traditional fault detection approaches in this context often rely on rule-based thresholds applied to features extracted from self-sensing signals. Although these methods can be effective, they are typically sensitive to variations in operating conditions. In contrast, model-based techniques use simplified system dynamics to generate residual signals that reflect deviations from expected behavior. In this thesis, we propose a hybrid framework that addresses the Fault Detection and Isolation (FDI) problem from a frequency domain perspective. By learning from signal characteristics, the method avoids the need for manually defined thresholds and predefined reference signatures. Instead, it uses classifiers trained to distinguish between different fault types and improve the adaptability to unseen cases. Building on this framework, the second part of the thesis addresses Fault Estimation (FE), aiming to reconstruct how faults evolve over time. A linear model-based estimation scheme is developed in both discrete-time and continuous-time forms. Even though this approach simplifies certain nonlinear dynamics, it provides useful fault tracking results, particularly for moderate fault levels. The evaluation on synthetic datasets shows that the proposed FDI and FE methods offer interpretable and reasonably accurate results. However, challenges remain when applied to physics-based data, particularly due to nonlinear effects, variable initial conditions, and numerical sensitivity.

This report investigates the use of Generative Adversarial Nets (GANs) specifically for over-
sampling Imaging Mass Spectrometry spectra. IMS is a technique used to measure the spatial
distribution of molecules, which is valuable in fields like oncology and biomarker discovery.
GANs, on the other hand, are a class of machine learning frameworks where two neural net-
works, the generator, and the discriminator, are trained simultaneously through adversarial
processes. The generator creates synthetic data, while the discriminator tries to distinguish
between real and synthetic data.
GANs-based oversampling aims to increase classifier performance by adding data to classes
that are underrepresented in the original data. Synthetic oversampling is especially relevant
in IMS data as the measuring technique is destructive, making acquiring more real samples
impossible. GANs have been shown to outperform other oversampling techniques such as
SMOTE on various datasets. Applying GANs directly to the dataset proved unsuccessful in
this oversampling task.
Different possible causes of the limited performance of the GANs are studied leading to
improved experiment results using spectra reduced in dimension and the Wasserstein GANs
with gradient penalty. Even though with these changes to the experiment the GANs appear
to generate more realistic data, using this data for oversampling does not increase overall
classifier performance. Rather, it steers the classifier to overfitting towards the minority
classes.
This report demonstrates that applying the designed GANs for oversampling minority classes
on this dataset does increase classifier performance. However, it is shown that GANs can be
trained on IMS data and that GANs might be of use for applications with IMS data besides
oversampling.

This paper presents a controller design approach aimed at optimizing the tracking capability of a freeform optical surface tracking system. It is shown that the second-order disturbance caused by the non-linearity in the system's spring and the changes in surface height is by far the biggest contributor to the tracking error. To reduce this error the paper proposes a position dependent spring force compensation, aiming to use the measurable non-linearity in the spring to effectively linearize the system. Thereafter, it is shown that the tracking error is mainly caused by the change in surface height and an unknown disturbance acting around the bandwidth. Since the change in surface height is known, a feedforward control structure is used to suppress the effect of this disturbance. Following this modification, the unknown disturbance becomes dominant. To suppress the effect of this disturbance a so called 'Constant in Gain Lead in Phase' controller is added that decreases the magnitude of the sensitivity function in the active frequency range of the disturbance, without increasing the sensitivity's magnitude at other frequencies. The proposed controller modifications are all experimentally validated on a freeform optical surface measurement machine through the evaluation of the tracking error during a tilted flat measurement.

This thesis addresses the detection and prevention of adversarial attacks on self-triggered control (STC) systems and demonstrates how manipulating the sampling times can be used to provide a secure control framework. Secure control is vital to guarantee both the safety and security of critical infrastructure, which has been the target of malicious attacks over the past decade.

First, a novel watermarking scheme is proposed based on the early triggering of a well-designed STC policy, such that stability is guaranteed to be preserved. We show that this watermarking scheme, together with an event-triggered χ² detector we design, is able to detect replay attacks. An online heuristic for obtaining an optimal early triggering policy is provided. If certain assumptions hold we show that the early triggering policy is a discrete uniform one. Through an illustrative example, both a quantitative and qualitative comparison between two other watermarking schemes are provided. We conclude that none of the watermarking schemes can claim absolute superiority, and trade-offs between all considered schemes exist.

Next, we propose a new type of attack called a switched zero dynamic attack (ZDA), and provide an algorithm on how to construct these switched ZDA. We show that certain STC systems are susceptible to such attacks, and demonstrate that by tuning the triggering parameters there exist sufficient conditions such that these attacks are no longer disruptive. The effect of additive perturbations and a non-zero initial condition, as well as the proposed tuning method, are shown in a numerical example. We provide a qualitative comparison between several other countermeasures in the literature, which we extend for aperiodic sampling when needed. Finally, shortcomings and future directions are discussed.

The steel industry is one of the largest emitters of greenhouse gases. Therefore, there is a need to develop revolutionary sustainable methods for producing iron and steel. HIsarna is one such sustainable method developed by TATA Steel Europe for a long time. Due to the flexibility offered by this iron-making process which allows using unprocessed iron ore, it is a promising technology. Currently, work is going on to stabilize and optimize this iron-making method. One of the steps of that process is maintaining the optimal level of slag in terms of its chemical composition. It is important to regulate the slag basicity to maintain the quality of iron produced. This is where the concept of distributionally robust control comes into use, as the fluctuations in the slag basicity are random that we wish to control under imperfect knowledge of the distribution of these disturbances. The aim of this project is twofold. First, the existing controller (from previous work done on HIsarna) is implemented on the real system. This controller outperformed human operators in a simulation environment which motivates this step. Second, using techniques from distributionally robust control, improves the robustness and performance of the controller. While the existing controller was trained in a simulation environment, the uncertainty in the real system may be different from that of the simulator. Using actual measurements together with more sophisticated training may lead to a controller that can handle various material properties and operating ranges appearing more common in production.

This thesis investigates the potential of state-dependent sampling strategies (SDSS) for the control of heavy-haul trains. Event-triggered control (ETC) is a control approach in which data is only sent when some state-dependent condition, the triggering condition, is satisfied. In this way, the number of communications required to stabilise a system can be drastically reduced. Periodic event-triggered control (PETC) is a variant of ETC in which the triggering condition is checked periodically. By sometimes sampling earlier than a PETC-generated deadline, long-term pay-offs in the average inter-sample times are possible. As searching for formal models of early-triggering controllers quickly becomes computationally infeasible as system dimensions grow, a sample-based approach using reinforcement learning was utilised to find the SDSS controller, which takes the form of a neural network mapping states to the time until the following sample, trained to hopefully yield long-term payoffs in sample efficiency. It was found that, by choosing suitable control parameters, the SDSS controller can outperform the PETC baseline in terms of inter-sample times (IST). Next, a hardware-in-the-loop (HIL) setup was made to evaluate the optimised controller’s performance in a real-time context controlling a non-linear train system subject to noise, disturbances, and tasked with attaining different speed setpoints while minimising the inter-wagon forces. It was found that the optimised controller did not perform well during these scenarios. Since the controller’s robustness was not considered during the training process, performance suffered under these conditions. To improve the robustness of the controller, the system must be trained on realistic (noisy) data. Simulations with more wagons must be performed to assess whether improvements can still be attained when using larger models. Finally, the accuracy of the HIL setup needs to be improved by using an appropriate network stack that would allow each wagon to send its own data and turn off the sensor node radios when otherwise idly listening for incoming data.

Event-Triggered Control (ETC) is a control method where the controller is only updated when necessary. The control inputs are kept fixed until a state-dependent event triggers their re-computation. The triggering condition is designed to guarantee the stability and desired performance of the control system. This prevents the excessive use of scarce communication and energy resources. To fully take advantage of these savings when dealing with a (physically) distributed system, it is vital to decentralise the triggering condition. A decentralised triggering condition can be checked locally at the nodes of the system using the locally available states, eliminating the need to send updated sensor measurements to a central location constantly. However, the decentralisation of the triggering condition needs to be optimised for the state of the system to fully take advantage of the possible resource savings. This thesis provides a framework to optimally decentralise quadratic triggering conditions for Periodic Event-Triggered Control (PETC) systems in the absence and presence of bounded disturbances. In addition, a method of constructing a region-based map of such optimisations is provided, making it feasible to implement the optimisation approach on larger systems without needing more powerful hardware.

In this thesis we consider multiple Automated Guided Vehicles (AGVs) navigating a common workspace to fulfill intralogistics tasks, typically formulated as the Multi-Agent Path Finding (MAPF) problem. To keep plan execution deadlock-free, one approach is to construct an Action Dependency Graph (ADG) which encodes the ordering of AGVs as they proceed along their routes. Using this method, delayed AGVs occasionally require others to wait for them at intersections, thereby affecting the plan execution efficiency. If the workspace is shared by dynamic obstacles such as humans or third party robots, AGVs can experience large delays. A common mitigation approach is to re-solve the MAPF using the current, delayed AGV positions. However, due to its inherent complexity, solving the MAPF is time-consuming, making this approach inefficient, especially for large AGV teams. To address this challenge, we present a novel concept called a Switchable Action Dependency Graph (SADG) which is used as the basis for a shrinking and receding horizon control scheme to repeatedly modify an acyclic ADG to minimize route completion times of each AGV using an optimization based approach. Our control strategies persistently maintain an acyclic ADG, necessary for deadlock-free plan execution. The proposed control strategies are evaluated in a simulation environment and show a reduc- tion in route completion times when a fleet of AGVs is subjected to random delays. Finally, the methods are also implemented using ROS and validated in the Gazebo simulation envi- ronment to illustrate practical feasibility when applied to real systems.

As the use of Networked Control Systems increases, the need for control methods with more efficient network usage also grows. These methods require a more sophisticated way of pre- dicting their traffic, and an approach for this is using a formal modelling approach using Timed Automata. Timed Automata have been used for over 25 years for several scheduling problems, but have not been adopted by the control systems community for scheduling event- triggered systems. This is a recent development for which no easy to use software tools have been developed, and performance in real-world applications is yet untested.
In this master thesis, an existing approach for scheduling event-triggered controllers is implemented in a set of tools. This approach creates abstractions of communication traffic, models them as timed automata and finds a scheduler avoiding communication conflicts. This set of tools is used to test the scalability with respect to abstraction accuracy and number of systems that can be connected. The set of tools can be used in the future to further improve on the techniques used.