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.

Autonomous driving technology aims to improve road safety, reduce energy consumption, and increase traffic efficiency. Developing a full-scale autonomous vehicle is costly and subject to strict regulations, making model-scale autonomous vehicle platforms more accessible alternatives for research. In recent years, an autonomous vehicle platform has been developed as a testbed for autonomous driving at the Delft Center for Systems and Control (DCSC).

However, several challenges are encountered during the development phase due to sensor malfunctions and inaccuracies in vehicle system modeling. The external vision-based localization system (Motion Capture System) generates inaccurate measurements, and the communication between the central controller and the sensors can be unstable, leading to packet loss. Additionally, there is a Radio Control (RC) input layer between the controller and the DCSC vehicles, and the dynamics of the RC inputs are unknown. These challenges have limited the implementation of general control architectures in the system.

To address these challenges, an Outlier-Robust Path-Tracking Framework is proposed in this thesis to perform path-tracking tasks under simulated sensor malfunctions and unknown vehicle dynamics. Within the framework, the motion of the vehicle is simulated using an identified DCSC vehicle model.
A first-order system is employed to model the relationship between the RC commands and the vehicle's rear wheel angular velocity and front wheel steering angle. Adaptive Outlier-Robust Kalman Filter is proposed to accommodate both the observational outliers and the model mismatch between the process model and the simulation model.

The performance of the framework is evaluated using synthetic datasets. Three types of observational disturbances—Gaussian noise, observational outliers, and packet loss—are introduced to the observations. The results show that, compared to the basic Kalman filter, the proposed filter provides more precise state estimation under observational disturbances. Additionally, compared to other non-adaptive outlier-robust Kalman filters, the proposed method can adjust the process noise covariance without requiring an accurate prior for the process noise covariance. The proposed framework demonstrated promising results for the path-tracking task under disturbances. Although the framework is tailored to our platform, the approach can be adapted to other systems with noisy measurements and model discrepancy.

Wind turbines have been deployed worldwide to address the growing energy demand while also targeting ambitious plans for the energy transition to renewable sources. Although wind energy is a key renewable energy source, it still faces unsolved challenges and offers ample room for innovation. One major concern is its variability, with the maximum power available fluctuating over time. Importantly, wakes from upstream turbines also contribute to reducing the power availability for downstream turbines. These wake effects are magnified by the construction of large wind farms with densely spaced wind turbines, aiming to efficiently use the allocated space.

Wind farm control strategies can be implemented to mitigate these wake effects and optimize wind farm power generation. In scenarios requiring on-demand response, such as those explored in this thesis, wind turbines are leveraged to provide flexibility, constrained by their maximum power availability. The power delivery of wind power plants upon request is facilitated by a closed-loop wind farm controller, providing active power control at fast timescales. Active power control involves adjusting the resource's active power to assist power grid operators in balancing energy supply and demand, thereby improving energy security.
Our proposed closed-loop control solution provides superior response capabilities by
compensating for reduced power availability, ultimately enhancing the reliability of on-demand power generation.

The wind variability across turbines, intensified by wake effects, contributes not only to attaining fluctuations in power generation but also to fluctuations in structural loads on the turbines. Amplified by wake-induced turbulence, this structural load variability across turbines leads to uneven degradation of turbine components over the long term. In offshore scenarios, where accessibility is limited and maintenance operations must be minimized due to higher costs compared to onshore counterparts, controlling turbines to prolong their lifetime is of significant interest. In this thesis, this aspect is addressed at both the wind farm and wind turbine levels.

At the farm level, we propose that farms fulfilling grid energy demands must also balance the aerodynamic forces of their turbines to evenly distribute structural degradation among them.
This can be achieved without compromising the power generation when the turbines operate below their maximum energy extraction capacity. We have demonstrated that by implementing a real-time feedback loop, it is feasible to balance aerodynamic loads while meeting wind farm energy demands, albeit limited by wind availability. Moreover, we have demonstrated that balancing aerodynamic forces is advantageous for active power control in a wind farm affected by wake effects, compared to simply distributing power requests uniformly.

At the wind turbine level, we introduced two wind turbine controllers designed to individually restrict real-time aerodynamic loads as a surrogate of structural loads in turbine components. These controllers are referred to as load-limiting controllers. The first load-limiting controller employs an optimal control approach. The operator can impose structural load constraints, using a convex model predictive control for power tracking. The second controller, which is more practical, utilizes a switching mechanism with integral control that allows the operator to prioritize a structural load setpoint over a power demand setpoint. This prioritization aims to reinforce structural safety in situations where turbines are compromised from their design conditions. This could be a consequence of numerous factors, such as unpredictable degradation, installation issues, vessel collisions, and others.

As wind turbines prove to be a viable, reliable, and eco-friendly energy source, new wind farm projects are becoming more ambitious, incorporating a larger number of turbines than ever before. Additionally, there is a substantial growth in wind turbine installations within existing wind farms. This growth in the number of turbines poses an implementation challenge for wind farm control systems. Similar challenges have been encountered in controlling other large-scale systems with collective goals, where agents must instead make decisions based on partial information due to communication limitations in processing or transmission.

Anticipating this implementation challenge, we transition from a centralized to a distributed wind farm control solution. Taking advantage of the time scale inherent in typical wind farm controller implementations, we exchange information with neighboring turbines rather than a central workstation. Our aim, in particular, is not to gather partial information but to achieve consensus across the entire farm. However, our control methodology has a negative implication - the addition of delays - which is carefully examined by the derived stability condition for the design and is assessed through simulations. Notwithstanding these delays, the proposed solution is fully distributed and has been demonstrated to be both simple and effective, facilitating the application of our control solutions in large-scale wind farms.

Lastly, we validate our wind farm control solutions through experiments conducted with scaled wind turbines in full-wake conditions. In this way, we verify the benefits of our control solutions not only through high-fidelity simulations but also through real-world experimentation.

The work presented in this thesis emphasizes the importance of wind turbine controllers capable of offering demanded power to the grid while enhancing reliability in power delivery and addressing structural and maintenance concerns. We introduce closed-loop wind farm controllers designed to handle these challenges. Furthermore, we expand the implementation through a distributed approach on one front, while on the other front, we validate the solutions by means of experiments. The findings from this research contribute to the efficient operation of future wind farms by employing feedback control strategies across clusters of wind turbines.

Lithium-ion batteries are the dominant electrochemical energy storage device and are a key enabler of portable electronics and electric vehicles. However, the technology is often criticized for its use of rare metals. Extending the lifetime of lithium-ion batteries can allow for more time to develop rare metal recycling efforts but this requires accurate knowledge of the battery's state-of-health.

The state-of-health is affected by degradation mechanisms that cause a fade in capacity and power over time, which eventually leads to end-of-life. A common way to track capacity fade is to compare state-of-charge values with changes in charge across a time period. The change in charge can be found by accumulating current, while state-of-charge can be tracked using estimation frameworks that use equivalent circuit models to mirror battery behavior. These models require a relationship between state-of-charge and open-circuit voltage to link the cell's internal state with the observed voltage. However, establishing this relationship is a time-consuming process.

Furthermore, the battery voltage partly depends on the direction of current, i.e. whether the battery is being charged or discharged. This hysteresis effect is highly nonlinear and persists when no current is applied. In past lithium-ion cell chemistries, voltage hysteresis was often neglected but recent developments have introduced silicon doping, which improves energy density, leading to a higher range for electric vehicles, but also increases the magnitude of the hysteresis effect. This degrades the accuracy of model-based estimation frameworks that neglect hysteresis. To improve state-of-charge estimation accuracy for silicon-doped cells, battery models can be extended with additional complexity to match the effect of hysteresis and improve the accuracy of state estimates.

This work combines the Plett hysteresis model with a single RC-pair equivalent circuit, the RC-H model, and uses a joint unscented Kalman filter to identify model parameters and estimate the state-of-charge during different dynamic testing profiles performed on an NMC/Si-C lithium-ion battery. A second version of the model replaces the predetermined OCV function with a simplification of the SOC-OCV relationship that is adaptively estimated using the Kalman filter, the A-RC-H model. Both models are benchmarked against a single RC-pair model, with a predetermined SOC-OCV relationship and no hysteresis component.

The results show that the RC-H model produces lower SOC errors, but comes at the cost of additional complexity and lower robustness to initial SOC error. While the A-RC-H model produces reasonable SOC estimations and can reconstruct the main OCV trend, the RMSE and MAE are only close to the RC model when the initial SOC error is small.

Future work could be aimed at understanding typical initial SOC errors and uncertainties in practical scenarios, improving the filter tuning by adapting noise covariances online, and reducing computational cost by separating states and parameters into two filters running at different frequencies. Furthermore, the different frameworks should be combined with capacity estimation methods to confirm the hypothesis that higher SOC accuracy leads to improved estimation accuracy of the total capacity.

Due to the increase in traffic, road congestion has gone up. Vehicle platooning is a possible way to increase the capacity of a given road, by decreasing the distance between the vehicles in the platoon. At the moment, the control of vehicle platoons is commonly done using PID controllers. The advantage of this is that it requires little computational resources. With improvements in computing technology in recent years, the possibility of using a more computationally costly method has opened up. But, parallel with that, dealing with inherently unmodeled dynamics and large parameter variations or faults, is a challenging task while
controlling any system. Classical control techniques do not provide satisfactory responses in most of the settings, and often external supervision systems have to be designed to handle the faults. Recent research has shown that active inference, a unifying neuroscientific theory of the brain, bares the potential of intrinsically coping with strong uncertainties in the system, mimicking the adaptability capabilities of humans. However, the current state-of-the-art regarding active inference in vehicle platooning is non-existent.

This thesis presents a novel active inference controller for adaptive cruise control systems and as a general adaptive fault tolerant solution for control of vehicle platoon. First, we demonstrate the applicability of active inference in classical control scheme in order to control a platoon of vehicles. Second, we verify that the proposed active inference framework is computationally efficient and with high performance against a benchmark model. Third, we access the adaptive properties of the designed framework in presence of large parameter variations and actuator faults. This work reveals that not only active inference is applicable in vehicle platooning, but it also outperforms the benchmark model in some characteristics, and it allows to deal efficiently with parameter variations and actuator faults. This thesis represents a first step towards the implementation of the current state-of-the-art of active inference for vehicle platooning, and it lays the foundations for further research in this direction.

Cyber-physical systems are vulnerable to malicious attacks, which can lead to severe consequences. Active detection methods have emerged as a promising approach for identifying such attacks. However, existing active detection methods are susceptible to malicious parameter identification attacks, where attackers exploit eavesdropped data to identify and manipulate the active detection mechanisms. In this work, we propose two methods to address the issue of malicious parameter identification: the system immersion coding method and the hybrid multiplicative watermarking method. These approaches have a primal focus on disturbing the identification of attackers and defending against malicious parameter identification. Besides, as active detection methods, both of them are capable of detecting multiple attacks.

The system immersion coding method, derived from the privacy solution in federated learning, is adapted to enhance its capability to detect malicious attacks by merging the input information and defend malicious parameter identification by leveraging its privacy-preserving properties. This method involves mapping the plant output into a higher-dimensional space and introducing carefully defined noise, which can create arbitrarily large disturbances without compromising performance. The introduced disturbance disrupts the attacker's parameter estimation. Theoretical conditions are provided to discuss the detection performance of replay attacks, control-signal-injection zero-dynamics attacks, and sensor-signal-injection zero-dynamics attacks. However, we also identify that the system immersion coding method is vulnerable to known-plaintext attacks.

Watermarking is a promising active diagnosis technique for the detection of highly sophisticated attacks. Motivated by the computational hardness problems of cryptography analysis, we propose a hybrid multiplicative watermarking scheme as an active diagnosis technique. In this scheme, watermarking parameters are periodically updated based on the dynamics of unobservable states in specifically designed piecewise affine (PWA) hybrid systems. We conduct a theoretical analysis to assess the impact of this scheme on closed-loop performance, demonstrating its stability preservation. We also provide conditions to detect replay attacks and control-signal-injection zero-dynamics attacks. Furthermore, we demonstrate that the proposed approach makes it challenging for an eavesdropper to reconstruct watermarking parameters, considering both computational complexity and systems theoretic perspectives.

Increasing the efficiency of wind farms is important for speeding up the transition from fossil fuels to renewable energy sources. Current wind farm control relies on maximization of power generation of individual turbines. However, research has demonstrated that plant-wide wind farm control could optimize the performance of a wind farm. Wind farm simulation tools are crucial in designing, testing, and validating wind farm controllers. Fatigue, Aerodynamics, Structures, and Turbulence Farm tool (FAST.Farm) is a recently developed multi-physics engineering tool for modeling power performance and structural loads by solving the aero-hydro-servoelastic dynamics of each individual turbine within a farm. FAST.Farm aims to balance the need for accurate modeling of the relevant physics while maintaining low computational costs. However, designing controllers in FAST.Farm lacks flexibility and interactivity compared with MATLAB/Simulink. The capabilities of FAST.Farm for control design purposes can be extended through a co-simulation with MATLAB/Simulink. Therefore this thesis presents a FAST.Farm and MATLAB/Simulink interface. Consequently, this interface was used to implement and simulate wind farm controllers in FAST.Farm. FAST.Farm and MATLAB/Simulink are coupled by linking the individual Open Fatigue, Aerodynamics, Structures, and Turbulence tool (OpenFAST) instances in FAST.Farm to MATLAB with the use of an Message Passing Interface (MPI) and MATLAB Executable (MEX) functions. An Active Power Controller (APC) was implemented and simulated in FAST.Farm with the use of this interface. The APC responds to grid requirements through the control of wind farm power output. Comparing FAST.Farm and Simulator fOr Wind Farm Applications (SOWFA) simulation results of the APC shows that FAST.Farm reduces the computation time for a 10-minute simulation from 24 hours to 15 minutes, with little detriment to the accuracy of the simulation results. Although SOWFA remains the preferred validation tool, the FAST.Farm and MATLAB/Simulink interface supports developing and accelerates testing advanced closed-loop control at the wind turbine and wind farm levels.

Without us realizing it, solutions for safety and security are present all around us. However, everyone has undoubtedly also experienced how inconvenient some safety and security measures can be. For example, think about security checks at the airport, the need to wear a bicycle helmet, or being asked to perform 2-factor authentication to log into an online account. Such inconveniences caused by safety and security measures can delay or even prevent their implementation, which is undesired. This reluctance to tolerate inconveniences for the sake of safety and security provides a challenge for engineers to find solutions with minimal impact on normal behaviour.

This challenge is especially pronounced in so-called cyber-physical systems (CPSs), in which digital automation is used to coordinate the actions of one or more physical systems. Examples of CPSs are airplanes, robotic arms or the power grid. Such CPSs have the combined advantages of the physical and cyber world, but are also subject to both threats to safety and security. In fact, the integration of physical and cyber parts in a CPS means that security issues can cause safety issues, and although less common safety issues can cause security issues.

Measures for safety and security of CPSs are categorized as prevention, resilience, and detection & accommodation. These different types of precautions can be used independently, but typically they need to be combined to provide adequate safety and security of a CPS. In this dissertation, three advances within safety and security of CPSs are presented which cover contributions on each of the different types of safety and security measures. Firstly, anomaly detection is addressed by extending existing sliding mode observer (SMO) based anomaly estimation methods with detection capability. To this end, two SMO based anomaly detectors are presented, which are applicable to a large class of SMOs. These detectors, by design, have no false alarms and allow for strong theoretical guarantees on detectability.

Secondly, a topology-switching coalitional control technique which integrates resilience, detection and accommodation is designed for safe control of a collaborative vehicle platoon (CVP) subjected to man-in-the-middle (MITM) cyber-attacks. Here resilience to undetected attacks is achieved by means of scenario-based model predictive control (MPC) and detected anomalies are accommodated by disabling the affected communication links. Lastly, a real-time implementation of encrypted control based on fully homomorphic encryption (FHE) is presented. FHE allows for manipulation of encrypted data, such that it can prevent confidentiality breaches during communication and computation.

Each contribution of this dissertation addresses a specific topic within safety and security of CPSs. By doing so, they demonstrate the potential of these methods to increase safety and security of CPSs while minimizing their impact on normal behaviour. This will promote the adaptation of safety and security measures and allows for safety and security throughout the continued progress in automation.

An Industrial Control System (ICS) is used to monitor and control industrial processes and critical infrastructure, and is therefore crucial to modern society. This makes them attractive targets for malicious cyber-attacks, which have become more advanced and abundant in recent history. To properly defend ICSs from these cyber-attacks, appropriate cyber-defensive mechanisms should be continuously designed and updated, cyber-attack detection mechanisms included. These mechanisms should undergo sufficient testing before being implemented in actual ICSs to minimise unforeseen consequences. Existing literature indicates that Dynamic Multiplicative Watermarking (DMWM) is a promising form of cyber-attack detection, which could improve overall detection performance. Thus far, this technique has not yet been applied to Automatic Generation Control (AGC) (a prominent form of Load Frequency Control (LFC) in power grids) to detect data integrity attacks (specifically scaling and replay attacks).

Ergo, this research aims at testing the performance of DMWM against data integrity attacks on AGC. To perform attack detection, a Luenberger observer it utilised. This observer generates a residual, which is compared to a robustly designed threshold. For the purpose of adequate testing, the HILDA (Hardware-In-the-Loop Detection of Attacks) testbed is designed and constructed. By using this testbed, more realistic scenarios can be simulated than with regular desktop simulations. After verifying the correct construction of the testbed, the DMWM performance is examined both on a desktop simulation environment using MATLAB & Simulink, and on the HILDA testbed. It is shown that the addition of DMWM increases the detection performance in the context of both scaling and replay attacks. For replay attacks, this performance increases notably, while for scaling attacks the improvement is more modest. It is shown that, overall, the attacks are detected more quickly when simulated on the HILDA testbed compared to simulations performed on the MATLAB & Simulink environment. On the other hand, the overall detection ratio was better when simulated on the MATLAB & Simulink environment. This discrepancy in detection performance demonstrates the added value of the HILDA testbed.

Hydrogen is a promising fuel for both reducing greenhouse gas emissions and being used as an energy carrier for renewable energy sources. The adaption of hydrogen instead of natural gas as a fuel in gas turbine combustors introduces additional challenges regarding flame stability. One instabilities is boundary layer flashback. This phenomenon occurs if the flame speed exceeds the local flow velocity. Then the flame can propagate upstream, which can result in equipment failure. Lean premixed hydrogen mixtures are more prone to boundary layer flashback because hydrogen flames have both a smaller quenching distance and higher flame speed compared to natural gas. Therefore, operating at 100% hydrogen fuel content is an enormous challenge. Active control strategies are a promising strategy to prevent and withstand possible occurrences of boundary layer flashback in gas turbine combustors. In this work, the detection, prevention, and counteraction of flashback are investigated in unconfined Bunsen burners. After an experimental analysis of suitable sensors, a tracking controller is therefore designed and different control design approaches evaluated.

Both the increase in flame fluctuations and in flame angle are reasonable as indicators for a flame moving towards flashback as the flow velocity is reduced. However, the complex nature of a flame makes it challenging to measure these effects in a flame. Four types of sensors, i.e. an ion sensor, a thermocouple, a photo-detector and a microphone, were investigated for their ability to detect flashback and to find precursors that indicate the onset of flashback. A Bunsen burner equipped with the different types of sensors was used in an experiment to determine the most suitable sensor type. Of the investigated sensors, the thermocouple is the most promising sensor for both flashback detection and use the temperature signal as a control variable. The temperature is a precursor for the onset of flashback since the temperature increases with increasing flame angle. Also, by placing multiple thermocouples in $360^oC$ configuration on the burner rim, it is possible to estimate the position, where the flame entered the burner.

A complete fault-tolerant framework to control a flashback event is proposed. This framework increases the flashback resistance of the system and it counteracts possible flashback events, which is considered as a fault in the system. A supervisor block has two functions. Firstly, the supervisor uses a fault detection method, known as trend checking, to detect flashback. Secondly, it decides which strategy should be applied, based on the temperature measurements. The fault-tolerant control strategy is threefold. First, the prevention controller closes the loop, so it can steer away from flashback conditions by rejecting disturbances and tracking a desired reference path using the temperature data. Different tuning methodologies based on the traditional proportional and integral (PI), linear-quadratic-integral (LQI) and pole placement regulator (PPR) are explored for this purpose. The robustness of the proposed controller was verified through loop shaping interpretation. However, flashback could be consider as an stochastic process that still could be triggered. Therefore, the second task is to detect present flashback events and apply counteraction to oppose the fault. A design is proposed by the injection of pressurized air upstream of the burner rim to push back the flame by diluting the boundary layer and increasing the flow speed. A proof-of-concept is validated in the experimental set-up as a possible and feasible counteraction. The third task is a final safety measure. A safety switch would be applied to shut off the fuel supply to fail safe after a potential unsuccessful counteraction attempt. The fault-tolerant control framework was simulated in Simulink for a demonstration purpose, where the models were identified from experimental data.

The proposed fault tolerant control framework is intended to increases the flashback resistance of the system by being more robust towards disturbances and flashback events that would normally require a restart of the system. Its potential to do so has been confirmed by simulations. However, further research on the prediction of flashback and controller implementation are still required to run burners on 100% hydrogen without flashback.

Achieving human-like action planning requires profound reasoning and context-awareness capabilities. It is especially true for autonomous robotic mobile manipulation in dynamic environments. In the case of component failure, the autonomous robotic system requires reliable adaptation capabilities combined with a consistent understanding of the task, environment, and the robot’s capabilities for successful task completion. Recent research has shown that
Active Inference, a unifying neuroscientific theory of the brain, has the potential to intrinsically handle substantial uncertainties in the system, resembling the adaptability of humans. These works, however, have the following limitations: (1) no distinction is made between actions with some commonality, capable of satisfying similar tasks, and (2) actions are assumed to be always feasible when preconditions are satisfied, regardless of their context. Given the situation, certain actions satisfying a task might not lead to task succession. This work
proposes the AI for retail (Airet) framework, a novel extension of action planning through Active Inference for mobile manipulation. The Airet framework uses Bayesian networks and Ontological Reasoning to facilitate context-awareness in action planning through Active Inference. Reasoning on robot components, action-, manipulation- and environmental constraints is facilitated through a description-logic-based reasoner and an OWL-based ontology containing concepts relevant for action selection in a retail context. The capabilities of the Airet framework are demonstrated through the following cases (1) irrecoverable task & component. Failure prevention when dealing with ill-defined tasks, (2) Selection of the best action given the situation & the component capabilities through context-awareness (3) failure recovery & adaptation when dealing with component failure. Lastly, these situations are compared with
research on reactive task planning through Active Inference without context-awareness. This thesis represents a leap forward from the current state-of-the-art in Active Inference for task planning in robotics, laying the foundations for further research in the direction of this thesis

Currently, encryption is part of daily life, from commercial to industrial applications. Secure, long-distance communication is vital to the safe and reliable operation of industrial feedback control. Utilising public networks as a medium is often cost-effective at the risk of a security breach. Current industrial feedback control systems generally utilise end-to-end encryption to communicate control signals and gains securely. Data has to be decrypted for processing. Homomorphic encryption allows for manipulation of encrypted data. This eliminates the need for decryption to update controller states and calculating control effort. Partially Homomorphic Encryption supports either multiplication or addition of encrypted values, whereas Fully Homomorphic Encryption allows for both. Besides being flexible, Fully Homomorphic Encryption schemes are thought to be quantum safe. Unfortunately, Fully Homomorphic Encryption schemes are computationally expensive limiting practical applications. This thesis presents an enhanced version of the of the popular Fully Homomorphic Encryption scheme by Gentry. The encryption scheme is enhanced through the introduction of three alterations. New notation is introduced that streamlines its description. The main functions that compose the encryption scheme are all replaced with analytical equivalents. The so called reduced cipher is introduced. Rewriting the encryption scheme using the improved notation, analytical functions and reduced cipher leads to a more computationally and memory efficient implementation. The alterations make the encryption more suitable for implementation on Field Programmable Gate Arrays which decreases compute time. Such an implementation is presented and used to demonstrate the efficacy of the enhanced encryption scheme.

Due to the increase in traffic, road congestion has gone up. Vehicle platooning is a possible way to increase the capacity of a given road, by decreasing the distance between the vehicles in the platoon. At the moment, the control of vehicle platoons is commonly done using PDD controllers. The advantage of this is that it requires little computational resources. With improvements in computing technology in recent years, the possibility of using a more computationally costly method has opened up. MPC is such a method, with a couple of important advantages for vehicle platooning. Firstly it is (finite-time) optimal so should lead to smaller deviations from the desired trajectory, thereby decreasing the minimum safe distance between the vehicles. Secondly, it is inherently able to include constraints on the behaviour of the platoon. This is important because it allows for explicitly enforcing the minimum safe distance between the vehicles, thereby preventing collisions.
While many studies have looked into the possibility of using MPC for vehicle platooning before. None of them have made the direct comparison with the current standard e.i. PDD control in a practical setup. This is important as it will give a good indication of both the possible advantages and pitfalls MPC could have in real platoons. To investigate this both a PDD and MPC controller are implemented on a platoon of RC-vehicles and their performance is compared.
To compare the performance five metrics are used on four scenarios. The metrics used are coherence, combined local error, velocity error, energy expenditure and minimum headway. The scenarios are a step up, step down, ramp up and a ramp down.

From the simulation results, it is clear that in an idealised case the MPC outperforms the PDD controller. However, when looking at the lab results this difference is less pronounced. In the lab setting, it is also clear that both controllers struggle to reliably deal with the added uncertainties caused by measurement noise and inconsistent behaviour of the vehicles.
In future research more emphasis should be placed on the robustness of the controllers used, to mitigate these problems.

Industrial control systems (ICSs) are used widely throughout the world for the control of large, complex industrial plants and consist of the entire setups of control system including sensors, PLCs (Programmable Logic Controllers), actuators and communication devices. The communication between these ICS devices is performed using industrial communication protocols such as Modbus, EtherCAT, etc. With the advancements in the use of the internet, ICS are being enabled to share real-time information over the internet worldwide. While these features make the ICS more accessible for remote supervisory control, they also make them vulnerable to cyber-attacks. This makes it the need of the hour to investigate risks and impacts of cyber attacks on ICS. Generating, injecting and testing cyber attacks on real world ICS, controlling critical infrastructure will involve several financial risks and safety issues. This gives rise to the necessity of an ICS testbed, with the ability to inject and test cyber attacks, in a safe and secure environment. A testbed provides a cheaper alternative for testing impacts of cyber attacks and also offers more flexibility to simulate multiple industrial scenarios. Together, these aspects form the core reasons behind the requirement of an ICS testbed. As the real world ICSs are often costly and specialized for industrial usage, there are not many research laboratories around the world with the availability of a testbed to study cyber attacks. Therefore, this Master of science thesis, through its primary research question, investigates if a cyber-attack testbed can be built in the NERD lab at the Delft University of Technology, which is able to replicate a real-world ICS network to identify and test vulnerabilities of ICSs working on Modbus protocol to cyber-attacks? To answer this question, this report studies the vulnerabilities in ICS working with Modbus protocol. A novel design for an ICS testbed for generating and testing cyber attacks at NERD Lab in TU Delft is presented during this thesis. The proposed testbed utilizes real world ICS components such as PLC and HMI, combined with a plant simulator, which is used for simulating an industrial process. The testbed utilizes a Linux based attack PC to generate and inject various cyber attacks. A virtualization platform connects the attack PC to the ICS network, giving the flexibility of injecting attacks on the testbed, without the attacker being physically present on the plant site. With the use of real world ICS, the testbed therefore allows to replicate a typical ICS scenario in the real world industry. Further, a simulated version of the actual testbed, with open source softwares, mimicking the ICS systems has been developed in this report. This simulated version provides a cheaper and flexible platform to perform initial testing on the working of the testbed and checks the feasibility of the actual testbed. The testbed simulates a plant, controller, and HMI in Matlab/Simulink on different physical PCs, which communicate with the Modbus protocol. An attack PC with a virtualization environment has been used to launch cyber attacks on the simulated testbed, same as that to be used in the proposed testbed at Delft University of Technology. Two main types of cyber attacks namely, Man-In-The-Middle (MITM) and Denial-of-Service (DoS) attack have been successfully implemented on this simulated testbed. To conclude this thesis, advanced versions of these attacks have also been developed and their impacts have been analysed.

As autonomous driving is a popular and ever growing field of research, real world experiments provide a required manner of testing. In this thesis a driving research platform is developed, with a focus on platooning using visual messaging. These visual messages are conveyed using LED matrices. This thesis proposes two methods of LED matrix detection using YOLOv2, one using a sliding window, and one using the entire image. Furthermore two ways of distance estimation are proposed, one using the centers of the estimation bounding boxes and one using the used camera proprietary toolbox depth map. Results from an online experiment show best results from the depth map based depth estimation. The LED matrix detection using a sliding window gave generally dependable results in different environments, at the cost of being computationally demanding. The detection using the entire image provided less consistent results, but was significantly less computationally demanding. In a second offline experiment using a preannotated validation dataset as groundtruth all LED matrices were detected for all detectors. The SqueezeNet based YOLOv2 detector using a sliding window had the best results between tested detectors, with the highest intersection over union between detection and groundtruth.

Unmanned vehicles are a vital topic in today’s science and technology field. The safety problem of unmanned vehicles has been paid more attention from researchers. People are continually developing new control technologies, making the auxiliary driving or control of vehicles more accurate and reliable. Before designing a reliable controller, researchers need to obtain an accurate model of the vehicle system. However, the vehicle is a complex system, and various vehicle parameters are difficult to obtain by direct sensor measurement. In addition, there are deviations between the actual vehicle and the simulation model. At this time, it is necessary to make system identification to obtain reliable vehicle parameters and models. In this paper, vehicle simulation is carried out based on the CarSim C-Class model under the environment of CarSim vehicle simulation software. The model includes tire, suspension, steering, driver, and other subsystems. This platform can simulate a vehicle closed to the real one. The vehicle model can be controlled through Simulink and output the real-time data of various variables of the vehicle system. Then, the data required for identification and validation can be obtained through the joint simulation of CarSim and Simulink. By comparing different vehicle and tire models, different identification data sets and different algorithms, this paper summarizes the advantages and disadvantages of different choices and their applicability. First, the comparison between the bicycle model and the four wheels vehicle model is implemented. The Interior-point algorithm was used to identify the two models under different control data sets. The results of parameter validation and vehicle validation are analyzed. Then, under the same control data set, the four wheels model is selected for the comparative experiment between different algorithms. The first comparison with the Interior-point algorithm is the Unscented Kalman Filter (UKF) and its improved method Particle Swarm Optimization-Unscented Kalman Filter (PSO-UKF). The Magic Formula tire model was then identified by Genetic Algorithm (GA) algorithm, which compares with the Dugoff tire model. Each model and algorithm has its suitable scenarios. Also different data sets lead to various result. The analysis and application suggestions of different algorithms, data sets and models will be given in the end.

Uncertainty can be defined as imperfect or unknown information arising in a stochastic environment. Due to the very limited knowledge, it is difficult to propagate and quantify various uncertainties affecting the system to its next step. As a result, it has been a challenge to consider multiple uncertainties affecting the system in various fields, such as Fault Detection and Diagnosis. Fault detection has been an essential part of a large industrial and manufacturing system to take a proper corrective measure into account in a case of unexpected behavior. However, determining a robust threshold bound for fault detection is a big challenge. Several uncertainties affect the system (such as parametric uncertainties, experimental uncertainties, process noise, measurement noise, etc.). Ignoring the effects of various uncertainties (i.e., Deterministic Bound) can lead to a false alarm.

Therefore, to design a robust threshold probabilistic-based technique is used where all unknown parameters are taken into account. However, the major problem lies in propagating these unknown parameters into the next time step with their limited knowledge. A novel Message Passage Bilinear Uncertainty Propagation (MPBUP) algorithm is being proposed, which is used to quantify and propagate various uncertainties affecting the dynamical system into the next time step. The main aim of the algorithm is to quantify and propagate various uncertain parameters affecting the system at each time step. Uncertainty is propagated in terms of mean and covariance at each algorithm iteration to find the overall effect. Therefore the primary research is to design and validate the proposed algorithm and to check the algorithm can be used to determine a probabilistic threshold.

In the report, a detailed explanation of the algorithm for a trivial example is presented. The algorithm is then developed and implemented in MATLAB. Next, validation of the output generated through the algorithm is performed using Monte Carlo simulation. Finally, various analyses based on the MC simulation are discussed to support the results generated through the algorithm. An innovative approach is also discussed to extend the polynomial system algorithm as the proposed algorithm is limited to the bilinear system.

Further, a detailed explanation is given on applying the algorithm on a general state-space model in terms of mathematics involved, which is further extended on applying the algorithm on a real-time application such as the Four Tank System. After successfully implementing the algorithm on the Four Tank System, the algorithm is used to propagate uncertainty into the dynamical system to determine the robust threshold. A robust threshold is found considering the effect of various uncertainty. The threshold found using the proposed algorithm is dynamic as it evolves based upon the state dynamics and satisfies the required condition. As a result presented algorithm satisfy all the requirement and can also be used in other applications. The algorithm was analyzed based upon various criteria to conclude this thesis, and a comparative study is conducted.

Noise pollution is a major health threat to society. Active noise control systems that attenuate noise through open windows have the potential to create quieter homes while maintaining ventilation and sight. Such systems are commonly realized with closed-loop LMS algorithms. However, these algorithms require a large number of error microphones inside the room and provide only local attenuation. Using many error microphones leads to slow convergence and high computational effort, having additional disadvantages. Therefore, closed-loop active noise control algorithms are undesired for real-world application. In this study, we develop an open-loop wave-domain algorithm that converges instantaneously and operates with low computational effort. As it is open-loop, it does not require error microphones. We position a control region in the far-field that covers all directions from the aperture into the room. In the algorithm, we minimize the sound in that control region. Hence, it inherently ensures cancellation in the whole room. We derive acoustic transfer functions to obtain frequency responses of the aperture and loudspeakers. Those are used for soundfield calculation. The sum of the soundfields, from the aperture and the loudspeaker array, is then expressed in orthonormal basis functions. By minimizing this sum in least mean square sense, we can calculate the filter-weights that minimize the sound energy in the control region. Implementation of these filter-weights with block-wise processing using the Short-Time Fourier Transform generates the signals for the loudspeaker array. However, this processing induces a delay. To compensate for this algorithmic delay, two methods are compared. The first is positioning a reference microphone further in front of the aperture. The second method uses an autoregressive model for signal prediction. Both lead to a loss in attenuation performance compared to the optimal algorithm. We compare the optimal wave-domain algorithm with a LMS-based reference algorithm, as well as both the algorithmic delay compensation methods. The algorithms are tested with a sparse and grid loudspeaker array, and we use rumbler-siren, airplane, and white noise signals as incoming noise. Furthermore, we compare performance for three incident angles. Our simulation results indicate that wave-domain processing has the potential to outperform LMS-based methods in practical active noise control for apertures. More specifically, we obtain an average -10dB global reduction up to 2~kHz for all signal types with the optimal wave-domain approach. In comparison, the performance of the closed-loop algorithm ranges between -5.2 and -9.2dB, depending on the signal type. Furthermore, we indicate the limited impact of the incident angle on performance for the wave-domain algorithm. Positioning a reference microphone in front of the aperture outperforms the predictor approach in all scenarios, and its performance compares to that of the closed-loop LMS algorithms. Eventually, the absence of error microphones and inherent global control ensure that the wave-domain algorithm can be used for a practical active noise control system for apertures. Future work could improve the algorithm by reducing loss of performance with small window-sizes due to time-delay wrapping in the block-wise processing. Furthermore, a natural continuation of this study is to develop and test the wave-domain algorithm for scenarios with a moving primary noise source to further emphasize its advantage over the closed-loop LMS algorithm.

Cyber-attacks long have been a topic reserved for sci-fi movies and books. With the advance of internet and the globalisation of technology supply chains, as well as the growing political and economic pressure around the world, cyber warfare has become the new weapon of choice for covert state operations, but also rogue organizations. In the last 2 decades multiple major industries have suffered some kind of outage – power generation, manufacturing, oil & gas, transport, and others. A typical industrial system is designed for a life span of more than 20 years, making future issues hard to protect against at the planning phase. No widespread efforts exist to identify such threats, to detect attacks and counteract them. This thesis proposes a practical approach for lower level protection of control systems based on linear watermarking as a transparent process to provide detection for any malicious activity that might significantly impact the operations of the plant.

Flat steel strip processing is carried out through a sequence of continuous methods, commonly recognized as hot rolling, pickling, cold rolling, annealing, and hot-dip galvanizing. Among all these processes, hot-dip galvanizing is a process that has been popularly used to produce high quality galvanized cold-rolled sheets that are extensively used in the manufacture of automobile and domestic electrical appliances. In particular, the main objective of galvanizing is to protect the steel strip from corrosion by applying a suitable coating of zinc-based alloy.

During hot-dip galvanization, the steel strip after leaving the annealing furnace is dipped into a bath of molten zinc, enabling the formation of zinc coating. The amount of this coating is controlled by the widely used air-knife wiping system installed just above the zinc bath. The coating thickness obtained after the wiping process depends mainly on the strip speed, wiping gas pressure, and strip-to-knife distance. After a significant distance from the air knives, the cold coating gauge is located to measure the thickness of the zinc coating.

The quality of the product depends on the amount of zinc-based alloy deposited at the strip's surface. The over-deposition of zinc at the strip surface results in excessive use of zinc, which is expensive, and under-coated strip results in a product of poor quality. The control of the coating deposition is based on the air-knife wiping pressure, and therefore, the challenge is to determine and control the pressure, given the operating conditions, which are the strip speed, strip-to-knife distance, and the target coating thickness.

One of the main concerns in the closed-loop control of coating thickness is the need to account for the measurement delay arising from the time-varying strip speed across the gap between the air knives and the cold coating gauge. In this thesis, different modeling and control strategies have been studied to improve the quality of the galvanized sheet produced at Hot-dip Galvanizing Line 1 (HDGL1) of Tata Steel in IJmuiden.