Objects floating on or near the water surface (e.g. vessels, and floating wind-turbines) suffer from motions induced by waves of varying height, direction, and frequency. This not only causes unpleasantness for passengers and crew of ships but also it limits the accessibility to the offshore platforms. Bosch Rexroth with their partner Barge Master have developed the so-called Motion Compensated Gangway that provides a safe passage for cargo and personnel to offshore structures. In order to maintain a motionless connection with the offshore structure, once the tip of the gangway is pushed against the offshore structure. The gangway system actively compensates for the sea-induced motion that acts on the vessel. However, the docking procedure is still manually attained, where accidents may occur due to human error (i.e. insufficient training, loss of concentration). One way to improve the current control scheme is to enable an automated docking scheme. Accordingly, the main of this project focuses on eliminating the human factor from the control loop, so the overall process is accomplished automatically and more efficiently in terms of safety and performance. Inspired by how the operator estimates the relative motion between the Gangway and the target (i.e. the offshore platform). In this thesis, a measurement system is proposed to measure this relative motion. This measurement system comprises a vision sensor, force tip measurements, and Motion Reference Unit (MRU). In this thesis, the proposed automated docking scheme is developed around a nonlinear MPC scheme. For the simulation environment and for the MPC scheme employs, a nonlinear model of the gangway system is derived. This model embeds an approximation of the joint-level control loop of the Gangway system. Also, this model comprises the open-chain kinematic model of the Gangway system and the proposed measurement system including a perspective projection model of the vision sensor. Due to modelling the vision sensor as such and the MPC’s capability in handling various constraints, the proposed control scheme enjoys a singularity-free solution. The proposed control scheme detects and tracks the target in the 2D image plane. To safeguard against visual measurements discontinuity (i.e. cluttering, target outside the field of view), a linear Kalman filter is designed to predict the target position in the image plane. To gain higher performance, the disturbance anticipatory property in MPC is enabled by forecasting the sea-induced motion. Where a neural network with the NARX topology was designed and trained to acquire a multi-step-ahead prediction model of the induced motion. Several numerical experiments were carried to evaluate the performance of the proposed control scheme for automated docking. Where for the nominal case scenarios all the control requirements are fulfilled. Also, more extreme scenarios are performed to evaluates the overall performance under plant model mismatch and against various sea-induced motion conditions. Evidently, the proposed control scheme is prone to camera calibrations error. In terms of the efficiency, the proposed automated docking scheme performs the docking in 4 to 10 seconds (based on the initial conditions and sea state). Whereas the time it takes the operator to perform the docking is up to 3 minutes which depends on his/her experience.

Correction techniques, such as tangential iterative projections (TIP), were developed to reconstruct the image for the whole field of view. However, due to the three dimensional nature of biological tissues, induced aberrations are different throughout the field of view. This Master of Science thesis shows the development of four algorithms. These algorithms apply TIP to deconvolve images locally. Thereafter, the local results are combined in order to restore images from anisoplanatic aberrations. The difference between the the algorithms developed during this research is the complexity in the spatial domain and in the Fourier domain. It was found that adaptive limited support in the spatial domain increases the im- age quality of the estimated object. A novel approach for multi-frame deconvolution in the Fourier domain has shown to be a promising modification. The use of weighted multi-frame deconvolution in the Fourier domain has lead to improved image quality.

The primary objective of this work is to research and develop a dynamic model and an advanced control strategy for a reversible solid oxide fuel cell in a grid to ensure load tracking whilst maintaining fuel utilisation and temperature dynamics within a safe range. This report presents the successful development of the dynamic model and corresponding controller. To do so, modeling and control are discussed in two separate chapters where model requirements, specifications and conception are presented followed by the control techniques, control objectives and controller synthesis. A RSOFC model has successfully been developed and thoroughly validated against other literature. To do so, a set of fitting parameters has been distilled from literature to create a good overlap with other studies and are combined in a manner that they can be implemented in other work. Steady state dynamics and the transient dynamics have shown a good match to the available literature. Additionally, the model offers useful insights into RSOFC (transient) dynamics with relation to temperature effects, fuel composition and cell support structure, which have not been documented before. At last the RSOFC stack, together with the developed model has been built in a plug-and-play manner that it can be implemented and adjusted by others, to be used in combination with other models. For control an output-feedback adaptive nonlinear model predictive controller has been developed, which is an advanced version of the well established (non)linear model predictive controller. The development of the temperature controller is given wherein the structure of the MPC is presented together with its trajectory, adaptive constraints and tuning variables. After completing the development of the controller, the controller was simulated together with the dynamic model as part of a micro-grid. The simulations were split up into short and long term scenarios and showed satisfactory results as all the set control objectives were achieved.

Proton therapy is an increasingly popular form of radiotherapy that uses high energy protons for the treatment of cancer. It results in a lower integral dose than the classical photon radiotherapy. This makes it suitable for the treatment of tumors close to critical organs such as in the head and neck. To minimize alignment errors the head and neck are immobilized during treatment. Current immobilization methods are uncomfortable and unable to correct local misalignments between the head and the body. Therefore, an adaptive immobilization device is designed that can correct the local misalignments of the head and neck.

Reset control is a "simple" nonlinear control strategy that has the potential of being widely adopted and improving the performance of systems traditionally controlled with PIDs. Lack of suitable methods for proving stability, that are in line with the current industrial practice, hampers the wider acceptance of reset control. In this thesis, novel sufficient conditions for stability of reset control systems, that can be evaluated using measured frequency response function of a system to be controlled, are derived using the hybrid passivity and finite-gain framework. A method for analysing the hybrid passivity and finite-gain parameters of reset systems, that can be extended to other classes of nonlinear systems, is developed. Additionally, a variant of the “Constant in Gain Lead in Phase” reset element, that facilitates the use of the proposed method for the stability analysis, is introduced. Stability of several precision positioning systems with reset controllers, designed for different objectives, is studied to demonstrate the applicability of the proposed hybrid passivity and finite-gain approach for the stability analysis of reset control systems. Guidelines for design of reset systems such that their stability can be concluded using the hybrid passivity and finite gain method are shown. This thesis presents a new view on the stability of reset systems and addresses the need for frequency-domain tools for stability analysis of nonlinear control systems in precision mechatronics applications.

Legged locomotion is a discrete event system (DES) due to the ever changing contact states of each leg. As such, it requires a nonlinear modelling method to predict the trajectory of a legged robot. One such robot has been focused on throughout this thesis; the six legged "Zebro''. With its half-circular legs, the Zebro is well suited for traversing uneven terrain and even climbing up small steps, making it the robot of choice for a lunar mission in the near future. A previous attempt at modelling the trajectory of a Zebro kinematically fell short when it came to modelling a curved path, with the reasoning behind this being how the Zebro's legs can visibly be seen slipping over the ground as it walks, the effect of which is never taken into account. A combination of kinematics and dynamics was used for the model in this thesis. The reason for this is that the Zebro's legs are actuated using position controlled motors, so the information available is the leg's angle and the speed at which the leg is rotating, rather than the torque. Therefore, kinematics were used to estimate the vertical motion of the Zebro due to the rotational speed of each leg, which could then be used to estimate the normal contact force on each leg. The traction forces could then be estimated using the normal force and a slippage model which has been experimentally identified in collaboration with a different research team at the TU Delft. With these forces, and the Newton-Euler equations of motion, the Zebro's path could be predicted. Applying a slippage model to a half-circular leg, rather than a wheel, required a new method of calculating the slip ratios which was based on Pacejka's formula, but adapted to also account for the case where a leg is standing on its toe. Furthermore, a contact detection algorithm was designed to kinematically predict which leg(s) lift off of the ground during a touchdown event. This was used to kinematically model the orientation of the Zebro during a tetrapod gait and was shown to correctly predict the complicated contact transitions during said gait. Another product of the research in this thesis is a new and improved algorithm for a turning tripod gait, which achieves smoother turning than beforehand by guaranteeing a smooth contact transition between two leg groups. That being said, it cannot yet be applied to the tetrapod gait, so the current gait scheduling algorithm is still required for a turning tetrapod gait. The results of the simulations showed the Zebro walking as expected, both in a straight line and when turning, but the simulations could not be validated quantitatively due to current events regarding COVID-19. For a qualitative review of the model, photographs were taken of the Zebro during walking gaits to compare to the simulations and showed that, while straightforward walking was predicted well, the turning circle of the model was significantly sharper than in reality. The reason for this is most likely a problem in the calculation of the slip ratios, since they were consistently unrealistically low, therefore requiring further research in future.

A challenging task in synchronization of multi-agent systems is steering the network towards a coherent solution when the dynamics of the constituent systems are heterogeneous and residing in a possibly large uncertainty set. In this situation, synchronization can be achieved via adaptive protocols (with adaptive feedback gains, or adaptive coupling gains, or both). However, as state-of-the-art synchronization methods adopt a distributed observer architecture, they require to communicate extra local observer variables among neighbors, in addition to the neighbors' states (or outputs). The distinguishing feature of this work is to show that synchronization in heterogeneous and uncertain multi-agent systems is possible without the need for any distributed observer. This can be achieved by designing adaptive distributed synchronization protocols, based on homogenization reasoning. Specifically, ideal gains are defined (feedback and coupling gains) that could lead all the heterogeneous agents to a desired homogeneous behavior and thus synchronization. However, since these gains are unknown in view of the unknown dynamics, we design adaptive laws for these gains that lead the agents toward synchronization. In this thesis, different control protocols are designed to address both leaderless and leader-follower synchronization of linear multi-agent systems. The protocols are then extended to achieve synchronization over a special class of nonlinear multi-agent systems, whose units are modeled as Kuramoto-like systems. Throughout this work, convergence of the synchronization error to zero is shown via Lyapunov analysis, and numerical examples demonstrate the effectiveness of the proposed protocols.

Biochemical reactions play a crucial role and tell us many about the behavior of the biological regulation processes . We will apply several methods of order reduction to describe the overall dynamics in a more compact way. For modelling a set of biochemical reactions is rewritten as first order differential equations. This set of first order differential equations defines the state space model. This rewriting is based on mass-action kinetics and Michaelis-Menten (MM) theory. With the Stoichiometry matrix the conservation laws and flux distribution in steady state can respectively be deduced. The systems we are looking at can be distinguished in many different biochemical regulatory networks . Examples of these networks can be found in gene expression, protein production and/or hormone production. The system that includes all given biochemical reactions of the network can be seen as a Coherent Feedforward Loop or CFFL. In synthetic biology these CFFLs are studied to gain insight into the desired production/expression: think about medicine production, agriculture and manufacturing. In biochemistry mostly a set of biochemical reactions can be given as a family or combination of CFFLs. To realize this a so called AND-gate or toehold switch is used. First of all we use conservation laws to reduce the system in order equal to the dimension of the left nullspace. Afterwards we have the option to reduce the system even more by applying the Quasi Steady State Approach in a given network like the CFFL. This method suggests that some species concentrations will reach its steady states much sooner than other species concentrations (if we look at slow timescale). Therefore it is assumed that some species already have their steady state at the beginning of the experiment. This is the so called classical QSSA. Another way to reduce the system order is by applying the Kron reduction order method. This method assumes a complexes network that reduces the complexes and thus the number of species. Here the concept of complex balancedness will determine whether the steady states for both models will be the same. Eventually we will also deal with alternative modelling where the cycles and feedback mechanisms will be replaced by more simple ones. Then afterwards mass-action kinetics along with classical QSSA can be applied. To get an optimal reduction order model the way in which parameters within the model are estimated can be discussed by optimization techniques. Furthermore we will see how the system can be transformed if we also have to do with in-and outflows. It actually means that we will need to add an extra term . One term will be in matrix-vector form while the other method merely uses vector-scalar notation. We will also look at the relation between these two forms. A future challenge would be to make an auto based system that directly converts the given system into its reduced order form. Here the best reduction order model will be selected automatically and applied in the best determined sequence.

Recent activities in the research on swarm robotics have emerged from the application of concepts from swarm intelligence into multi-robot systems (MRSs) that model the realistic interaction between robots in the system and the environment. Fundamentally, the literature on swarm robotics is biologically inspired by systems as insect colonies, flocks of birds, schools of fish and bacteria colonies. Recently, the flocking formation control behaviour in multi-agent systems (MASs) has encouraged astounding attention among the researchers. Researchers from various disciplines including physics, biophysics, computer science and control engineering have been fascinated by the emergence of flocking, swarming and schooling in MASs under local interactions. In this research, we focus on flocking algorithms for MRSs. The flocking phenomenon is characterized as a form of collective behaviour of a swarm of robots with a distributed architecture that involves locality of the computation, sensing, communication and effector capabilities. Flocking algorithms have the potential to introduce selfhealing, self-organizing and self-configuring capabilities in the functioning of distributed MRSs. However, despite an exhaustive list concerning flocking formation control algorithms is given in the literature, most of the existing results deal with simple mathematical modelled robots. In practice, mobile robots embrace more complex nonlinear dynamic mathematical models and involve non-holonomic constraints. Therefore, it is of scientific and practical interest to study the effectiveness of the flocking algorithms for such complex nonlinear systems involving non-holonomic constraints. This thesis study, expanding novel features on the existing literature, presents a connectivity-preserving artificial potential function (APF)-based flocking algorithm for formation control of mobile networked non-holonomic Euler-Lagrange (EL) dynamical agents under a proximity graph interaction architecture involving a limited sensing radius. In specific, we consider three algorithms: (i) flocking; (ii) (virtual) leader-following flocking; (iii) flocking with obstacle avoidance. Proximity graphs are viewed as a useful and decent mathematical tool to incorporate the practical time-varying communication topology of MRSs in flocking algorithms. The preservation of the network connectivity is of significant importance for the flock stability and synchronization (i.e. consensus) since they firmly depend on it. The use of APF, to encode the local interaction rules for achieving global performance, is inspired by the observations and models of the biologists. APF-based flocking control algorithms are mainly interesting as they are not limited to higher-level models and can be exploited for more advanced nonlinear dynamic models and control strategies for flocking and collision avoidance purposes. The aforementioned algorithm setting improves the practical relevance of the problems to be addressed in this study and meanwhile, it poses technical challenges to the design of the flocking control algorithm and theoretical stability proof, respectively. In all proposed algorithms in this study, being the first author in the literature to study flocking algorithms for non-holonomic EL systems in specific, novel theoretical results for this class of systems, exploiting nonlinear control theory concepts where a nonnegative lower bounded ”energy-like” Lyapunov function candidate is defined, are obtained. Advanced numerical simulation studies and some performance metrics are presented as a complement to the analytical framework in order to verify the effectiveness of the theoretical results.

This book on complexity science comprises a collection of chapters on methods and principles from a wide variety of disciplinary fields — from physics and chemistry to biology and the social sciences. In this two-part volume, the first part is a collection of chapters introducing different aspects in a coherent fashion, and providing a common basis and the founding principles of the different complexity science approaches; the next provides deeper discussions of the different methods of use in complexity science, with interesting illustrative applications. The fundamental topics deal with self-organization, pattern formation, forecasting uncertainties, synchronization and revolutionary change, self-adapting and self-correcting systems, and complex networks. Examples are taken from biology, chemistry, engineering, epidemiology, robotics, economics, sociology, and neurology.@en

This book on complexity science comprises a collection of chapters on methods and principles from a wide variety of disciplinary fields — from physics and chemistry to biology and the social sciences. In this two-part volume, the first part is a collection of chapters introducing different aspects in a coherent fashion, and providing a common basis and the founding principles of the different complexity science approaches; the next provides deeper discussions of the different methods of use in complexity science, with interesting illustrative applications. The fundamental topics deal with self-organization, pattern formation, forecasting uncertainties, synchronization and revolutionary change, self-adapting and self-correcting systems, and complex networks. Examples are taken from biology, chemistry, engineering, epidemiology, robotics, economics, sociology, and neurology.@en

Portable Oxygen Concentrators (POCs) are devices that produce oxygen-enriched air, by selectively filtering nitrogen out of ambient air with a cyclic process called Pressure Swing Adsorption (PSA). The current control method is to adjust the timings of the process by means of lookup tables, such that the POC operates as efficient as possible. The aim of this thesis is to determine whether Model Predictive Control (MPC) is a viable alternative to control the POC, and is able to cope with the constraints and variations of the system. First, a high-fidelity model has been made of the POC, used for simulation of the device and controller design. Comparisons with other suitable models of POCs have shown that the dynamics inside the POC have been modeled correctly. Because this model is too complex to serve as a predictive model, a simplified batch model has been created for that purpose. This hybrid automaton consists of 13 linear models, and encompasses the cycle-to-cycle dynamics of the plant. Finally, a switched linear MPC strategy has been designed and implemented on the high-fidelity model. Simulations show that this control strategy is suited to control the POC, although further research is needed to cope with system degradation.

The world of molecular biology is composed by a complex network of interactions that are analogous to electric circuits. They govern the functions required for life, from metabolism to locomotion. In these networks, the presence of network motifs were identified, recurring elements supposedly kept by evolution. One of them is called the feedforward loop and has the function of a sign-sensitive delay element or noise-filter. Moreover, different combinations of several types of feedforward loops were identified in the transcription networks of Escherichia coli and Saccharomyces cerevisiae, called complex feedforward loops. From this finding a question arises: do different types of combined feedforward loops have a specific function? Would this identified function be useful in synthetic biology applications? Answering these questions is the ultimate goal of a research direction in systems biology, studied at the Institute of Complex Molecular Systems at Eindhoven University of Technology. However, biological experiments are difficult to setup and conduct in a suitable manner to generate relevant results. Therefore, it would be highly effective to be able to predict the nonlinear dynamical behaviour of these (combined) feedforward loops. Nevertheless, in order to be able to achieve this, first a single feedforward loop must be fully modelled, calibrated and analysed. This master thesis focuses on this goal and is composed of three main elements: modelling, parameter estimation and structural analysis. The modelling section comprises of the methodology derived in order to transpose the biochemical reactions into equations and perform model reduction on the feedforward loop built at the ICMS. Then, a hybrid parameter estimation method was applied successfully and made it possible to perform numerical simulations of the system. Lastly, the focus was directed to structural analysis and obtaining insights about the behaviour of the network without knowledge of the parameters. This included the adaptation of metabolic network analysis tools, elementary flux mode analysis and flux balance analysis to be used on gene expression networks. As a result, it was possible to link the nonlinearity of the steady-states observed in the experimental data with the accumulation of certain compounds.

The DelFly Nimble is a type of tailless flapping-wing micro air vehicles (FWMAVs) that has received an increasing amount of attention. FWMAVs show efficient and agile flight possibilities at small scale. The aerodynamics and dynamics of these flapping vehicles are challenging and not fully understood. In this work, a strategy to implement a 3D dynamic model and a trajectory control algorithm is proposed for the DelFly Nimble by the use of a quasi-steady state framework. The design of a suitable tracking control algorithm is the essence of this task. A globally defined smooth nonlinear geometric framework of the flapping-wing vehicle’s rigid body dynamics is introduced as a basis for the analysis. This grants an unambiguous coordinate-free dynamic model in which problem of singularities are avoided. The Nimble has four inputs used to control the six translational and rotational degrees of freedom. A nonlinear tracking controller is chosen on the special Euclidean group 푆퐸(3) for the underactuated aerial vehicle, where position and yaw trajectory tracking are achieved. The full system is classified into the coupled attitude and position subsystems. Using the Lyapunov Stability theorem, the nonlinear controller is shown to achieve almost global asymptotic tracking of the attitude error dynamics of the Nimble and almost global asymptotic tracking of the position error dynamics of the center of mass of the Nimble, enabling sufficient tracking of aggressive maneuvers. Finally, the dynamic model and the controller are examined with numeric simulations. From the results can be concluded that the nonlinear control design allows for aggressive aerobatic maneuvers while maintaining stability of the closed-loop system, provided that the control inputs and damping forces remain moderate.

The ZeBRo (Dutch abbreviation: Zesbenige Robot, six-legged robot), is a walking robot designed by the TU Delft, with its foundations on the RHex. The current version of the ZeBRo project, the DeciZebro, is made for the research to swarming in robotics, and is about the size of an A4-paper. To counter these shortcomings of CPG (a method to determine gait movement), Max-Plus algebra can be used for the timing of the legs of walking robots. This can be done by modeling a walking cycle as two discrete events. These events are the times of the lift-off and touchdown and can be determined by using a Max-Plus Discrete Event System (DES) framework. The main purpose of these events is to determine two separate periods; the stance period, and the swing or flight period. Max-Plus algebra is defined as a semi-ring over the union of real numbers and minus infinity. The standard binary operations are taking the maximum and addition which can be used for scalar and matrix operations. The Max-Plus linear system contain the gait matrix A, and the vector v(k) containing the lift-off and touchdown times of vector instance k. One of the strengths of this method is the ability to change gaits, by simply changing the A-matrix. This then results in the switching Max-Plus linear (SMPL) system: This SPML system determined in only contains gaits with successive recirculating leg-groups, where the synchronization of the lift-off of a certain leg-group I relies on the touchdown of the leg-group i-1. This allows for gaits like walking for humans, or a slow trot for horses, where a leg-group remains on the ground until the previous leg-group has recirculated. To increase the number of possible gaits, the synchronization of successive leg-groups is extended to not only contain the previous leg-groups' touchdown times, but also their lift-off times. This extension allows for gaits containing multiple rotation leg-groups at the same time, making gaits like the gallop possible. Because of the desired stable situation of grounded legs, the synchronization is chosen to not extend the timing of the touchdown, as delays should not hinder the return of the legs to their grounded position. The performance of different gaits is tested on different surfaces, to determine the influence of the amount of legs on the ground and determining whether gait changes could influence the success rate of the system regarding traversing rough terrain. Inertia measurement units (IMU) are used for detecting rough terrain, and tests show that the body movement of a ZeBRo on both flat and rough terrain are heavily influenced by the gaits used. Because of several limitations regarding the actuation of the legs, gaits containing moments of static instability could not be tested. However, the framework for implementation regarding the Max-Plus is lain and tested using simulations.

A better understanding in the biological clock is important to help reduce stress on shift workers. Moreover, in the field of medication it will allow for more effective treatments as certain types of medication are more effective when applied at certain moments in the 24-hour cycle. The mammalian biological clock is controlled by a tiny area in the brain called the suprachiasmatic nucleus (SCN). The neurons in the SCN are able to synchronize their clock gene expression to each other through coupling. However, it is unclear how different types of coupling affect the synchronizing property of the SCN. This research explores how two types of coupling, namely gap junction coupling and neuromodulator diffusion shape the synchronous behaviour of a network of SCN neurons. Since data collection in the biological system is challenging, this research is done using a modeling approach. A mathematical single SCN neuron model was used and scaled to a network representation. The individual neuron models were coupled using a gap junction and neuromodulator models. In this research, it was found that gap junction coupling is a precise and strictly local form of coupling. It can synchronize phase errors on the level of action potentials. However in a network there is a maximum number of neurons that can be synchronized, assuming that the number of gap junctions per neuron is limited. Therefore, it was concluded that in large SCN networks gap junction coupling is not able to completely synchronize the network. Neuromodulator coupling is imprecise, slow and global. It can effectively synchronize large phase errors, however, as phase errors become small, neuromodulator coupling loses its synchronizing ability. Therefore, it can only synchronize the daily pattern and cannot synchronize on the level of action potentials. Furthermore, it was shown that neuromodulator diffusion can synchronize SCN networks of arbitrary size. As a final conclusion, the combination between gap junctions and neuromodulator diffusion is able to synchronize a network completely, perfectly, and rapidly. The neuromodulator component ensures complete synchronization while the gap junctions guarantee perfect synchronization (in the absence of noise). Furthermore, the combination ensures effective synchronization for both small and large phase errors. Therefore, the combination of these two types of coupling is vital in the establishment of network with versatile synchronization properties.

The integration of variable renewable energy sources (RESs) in the electrical power grid leads to larger and faster variations in the power demanded from controllable power sources. This is a problem, because flexibility of (base load) power plants is limited. Solid oxide reversible cells(SORCs) can be used as load-shifting devices to reduce these power variations by converting electricity to hydrogen (solid oxide electrolysis cell (SOEC) mode) when power demand is low and converting hydrogen to electricity (solid oxide fuel cell (SOFC) mode) when power demand is high. However, the introduction of SORCs is challenging. It is a promising long-term energy storage technology, but it is in its development stage. Apart from prohibitive costs, challenges also lie within durability and efficiency under dynamic operation. Development of control strategies is essential for maintaining optimal operating conditions. Therefore, this study researches the ability of SORCs to operate in a mixed power grid by developing an SORC model and power disturbance rejection controller which ensures safe operating conditions. A dynamic 0D SORC model was developed. It describes a single cell at the center of a large stack of identical cells, which makes it representative for large-scale SORCs. The model is based on SOFC models and uses the current density to indicate the operating mode of the SORC. The benefit of this approach is that one continuous model describes both operating modes. Validation of the model is based on comparison of static cell voltage-current density curves from literature and from a small stack experiment. Open-loop analysis of the model showed that the system is stable and can be decoupled. It also showed that development of gain-scheduling controllers was necessary to handle the exothermic, hydrogen consuming SOFC mode and endothermic, hydrogen producing SOEC mode. This motivated the design of gain-scheduling H-infinity tuned proportional-integral (PI) controller, which were used to control the positive electrode, electrolyte, negative electrode (PEN) structure temperature and fuel channel composition by manipulating the air and fuel flow rate, respectively. Two methods were compared for specifying the performance of the controller. The first method was based on the desired closed-loop bandwidths and the second method was based on the bandwidth of the disturbance. The first method was superior to the second method, because the obtainable closed-loop bandwidths are faster than the bandwidth of the disturbance. This study shows that gain-scheduling PI controllers allow SORCs to be used for load shifting applications in a mixed power grid. Further research is needed to validate the dynamics of the model and to identify the influence of balance of plant (BOP) dynamics on controller performance.

Because of the increasing e-commerce volume, resulting in increasing demands on the speed of delivery, logistical processes become more and more automated. Order picking is one of the last tasks that is done by humans in warehouses, because humans are flexible with respect to the large variability and changeability in items. However, it is a labour intensive and monotonous task, resulting in fatigue and a shortage of order picking personnel. This motivates the development of automated pick-and-place systems. One of the challenges for such systems is the heterogeneity of items. In warehouses there is a big diversity in items so the system has to be able to deal with all of them. Another challenge is dealing with items that are deformable. Current systems often make use of suction cups but integrating sensors that can be used to handle deformable items is hard. Fingered robotic grippers have more potential in grasping these kind of items, but grasping deformable items is one of the least addressed topics in robotics. Therefore, the objective of this thesis is to design a control strategy for a fingered robotic gripper to grasp and hold deformable items in a pick-and-place task. Inspired by the underlying principles that humans use to execute a pick-and-place task, a multi-level controller is proposed for a three-fingered gripper with capacitive pressure pads. The multi-level controller consists of a low-level computed torque controller and a high-level numerical optimisation based extremum seeking controller. The computed torque controller uses an internal model of the kinematics and dynamics, which is derived with screw theory, to compute the torques required to comply with the fundamental grasping constraint and the setpoint on the gripping force. The controller is tuned in such a way that the grasp quality is maximised, given a constant reference gripping force. Because of the fact that the properties of the items are unknown, an intelligent control system has to be able to determine the gripping force setpoint autonomously. This is the task of the high-level controller, that uses tactile sensors to derive the slip. This slip is used to determine the setpoint on the gripping force that the low-level controller has to follow, while maximising the grasp quality and not damaging the products as a result of applying excessive gripping force. The proposed control strategy is tested and tuned in a simulation environment. The pick-and-place task is executed for the products from a virtual product inventory. The controller is optimised with respect to the control goal on a wide variety of deformable items. Designing controllers according to the proposed principle will increase the diversity of items that can be handled in a pick-and-place environment, while increasing the quality of the grasp and minimising the risk of damaged products.

Traffic congestion on highways is a multi-sectoral phenomenon affecting society, the economy and the environment. It often takes place at specific locations such as on and off-ramps, weaving segments and intersections. The on-ramp merging procedure is considered as one of the main factors that causes traffic congestion on highways. The studies in literature show that the merging procedure can result in adverse traffic scenarios such as the buildup of the vehicles on the ramp which causes a downstream drop in capacity and subsequent blockage of upstream off-ramp traffic flow. Moreover, the on-ramp vehicles need to take the actions of leading and following mainlane vehicles into account during the merging process. On highly congested roads, this merging process becomes even more tedious and undesirable stop-and-go traffic behavior becomes unavoidable. Connected and autonomous vehicles (CAVs) that can provide safe gaps between vehicles along with identifying appropriate merging speed profiles have the potential to reduce traffic accidents and improve traffic efficiency. This thesis introduces a nonlinear model predictive control (NMPC) strategy for autonomous merging control based on a cost function that tracks the desired inter-vehicular gaps for on-ramp and mainlane vehicles, and thus intends to fully exploit the capacity of the road in order to maximize the traffic throughput. The proposed controller aims to optimize both acceleration and steering rate profiles of vehicles, and to guide on-ramp vehicles to merge efficiently, without frequent slowdown or wait for merging gaps at the end of the ramp along with minimal disruption to the mainlane traffic flow. The controller is evaluated under different initial conditions, ranging from low to high traffic conditions. The performance of the controller is compared to that of a baseline scenario, and the results show that the proposed controller increases travel times in the range of 2.46% and 4.17% for different traffic conditions, without disrupting the mainline traffic operation. Additionally, average speed of vehicles is improved in the range of 8.2% and 4.5% under different traffic conditions.