In Upper Motor Neuron Lesion (UMNL) following stroke, patients can experience increased joint impedance, resisting joint rotation and hindering functional movement. This heightened impedance in UMNL is driven by both exaggerated reflexes and increased intrinsic muscle activation through co-contraction, hypertonus, or synergies. The simultaneous presence of these mechanisms complicates clinical distinction, especially given their theorised interplay, where increased intrinsic activation would further heighten reflex responses. Separate quantification of this intrinsic and reflexive impedance and their interaction, can aid in further investigation of the pathophysiology of post-stroke joint impairment and its treatment.

This work presents the investigation of an Open Loop System Identification (OL-SID) protocol, to perform this separate quantification of intrinsic and reflexive impedance for the elbow joint. Perturbation experiments were performed with 16 healthy subjects, using multisine positional perturbations and measuring the elbow torque response. An impedance model consisting of both intrinsic and reflexive parameters was fit to the estimated frequency response function (FRF), relating perturbation angle to joint torque. It was assessed how background muscle activation, as well as the frequency and velocity of the perturbation signal, influenced the modelled intrinsic stiffness, intrinsic damping, and reflex velocity-gain.

For this, three different biceps muscle activation levels were requested from the participants in different trials; 0%, 10%, and 30% of Maximum Voluntary Contraction (MVC), as confirmed by online EMG measurements. Participants were requested to not actively resist perturbations, but only to comply with the requested biceps activation level. Furthermore, three rotational multisine perturbations with a max. amplitude of 2 degrees were applied; Wide Bandwidth - High Velocity, Narrow Bandwidth - Low Velocity, and Wide Bandwidth - Low Velocity. Cross-combination of biceps activation levels and perturbation signal resulted in 9 impedance quantifications per participant.

Increased biceps activation resulted in a significant increase of intrinsic stiffness, intrinsic damping, and the reflex-gain. This confirmed the expected relationship between muscle activation and intrinsic impedance, as well as the theorised relation between intrinsic activation and the reflex response. Unexpectedly, differences in used perturbation bandwidth or velocity showed no clear influence on identified reflex gain. This contradicts findings of reflex suppression during high-bandwidth force perturbations in tasks that require resisting these perturbations, as well as during high-velocity binary or unidirectional joint stretches. This discrepancy shows that joint system identification results are highly dependent on perturbation type and subject task, emphasising the need to align the experimental design with the clinical question at hand.

Despite some shortcomings regarding low coherence of the estimated FRFs, and necessary further research on perturbation signal properties and their effect on the reflex response, the results of this study are promising. The observed trends in fitted parameters with increased activation levels in line with physiological expectations, indicate the ability of this technique to validly identify reflexive and intrinsic joint impedance. This distinction is highly valuable for advancing investigation of the pathophysiology and clinical presentation of UMNL post-stroke, in the pursuit of adequate treatment for different patients.

This study aims to systematically design and implement a voltage regulation control loop (VRCL) for an unspecified power management unit (PMU) to maintain the desired load voltage for electronics worn by group-housed rodents. This is crucial for analyzing the spontaneous behaviour of group-housed rodents, focusing on social interactions and environmental exploration in a simulated natural environment.

The study explores a potential high-level design of a power link, selecting resonant inductive coupling for wireless power transfer (WPT), enabling continuous power supply over larger areas (for instance, 5 by 5 meters). A literature review reveals a research gap in integrating encircling and underneath configurations for sufficient uniform power distribution. A block diagram of the WPT system is provided, outlining the transmitter and headstage receiver components. Subsequently, a hybrid layout is suggested and significant challenges like optimizing driving current and minimizing angular and vertical misalignments are addressed.

Next, a control loop is systematically designed and implemented. Potential loads and a PMU are
selected, followed by the development and verification of an ideal power converter and its derived
and proposed plant model. Control specifications derived from this model suggest the tuning of the
controller parameters using a tailored model-based control system approach. MATLAB simulations
confirm that the control specifications are met. A non-ideal power converter is then integrated with the control loop, including ideal gate drivers, a voltage-controlled oscillator, a bandgap reference, and a PI controller. Simulation results show that the control loop meets the specifications. Despite limitations in robustness, particularly regarding load and input voltage transient response, the study also highlights the need for verification of the power converter model for load capacitors in the pF-nF range, and identifies discrepancies in overshoot behaviour. Future work includes an analysis of the system’s robustness during controller tuning and the incorporation of a transient controller. The combination of the suggested plant model and model-based tuning approach offer an alternative option for the power converter’s control loop design.

Noise's impact on biochemical systems has long been a focal point of investigation, given its potential to compromise signal accuracy and disrupt system functionality. This paper conducts a comprehensive exploration into the noise characteristics within a set of signal differentiators recognized for their high precision. Noteworthy for their modularity, swift computation, and ease of implementation, these differentiators play a pivotal role in computing concentration changes and bear the potential to regulate the dynamics of biological systems.

This study establishes a comprehensive simulation framework to examine the noise characteristics of these differentiators across diverse input signal scenarios. Furthermore, we also apply noise suppression techniques such as noise filters to mitigate excessive noise and enhance noise performance.

Our findings reveal that these differentiators significantly amplify the system noise level, surpassing both the Poisson level and the original system noise level. Moreover, while noise filters demonstrate notable success in noise reduction, achieving Poisson-level noise without compromising signal integrity remains a challenge.

This investigation yields invaluable insights into the noise properties of biochemical differentiators, shedding light on their inherent limitations. Additionally, it presents a viable pathway to enhance noise behaviour, thereby extending the scope of applications for these differentiators.

Recent progress in multiple micro aerial vehicle (MAV) systems has demonstrated autonomous navigation in static environments. Yet, there are limited works regarding the autonomous navigation of multiple MAVs in dynamic and unknown environments. The challenge arises from the complexity of the motion planning problem, which requires the MAVs to coordinate with each other while avoiding dynamic obstacles. This thesis presents a novel risk-aware decentralized multi-agent motion planning framework to address this issue. For perception, we rely on a particle-based dynamic map, which utilizes particles to represent dynamic obstacles and predict future states of dynamic obstacles. Leveraging this map representation and the shared trajectory from other agents, we evaluate the future collision risk with dynamic obstacles and other agents in a coupled manner. During the planning phase, a risk-aware kino-dynamic A* algorithm tailored to the particle-based map representation is developed, ensuring dynamically feasible paths with risks under a given safe level. Subsequently, spatio-temporal safety corridors with maximum volume are optimized by inflating from path segments, taking map particles as constraints. These corridors act as constraints for the trajectory optimization problem, which is simplified to a convex optimization problem by using Bézier splines. The proposed method is thoroughly evaluated in simulation environments featuring various quantities and shapes of dynamic obstacles. Comparative results with state-of-the-art multi-agent planners that rely on precise obstacle observations demonstrate the efficiency and safety of the proposed method. Furthermore, the effectiveness of the proposed method is validated in a more realistic simulation environment with pedestrians, using depth cameras onboard for perception.

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.

Max-plus-linear (MPL) systems are systems that are linear in max-plus algebra. A generalization of these systems are Max-Min-Plus-Scaling (MMPS) systems. Next to maximization and addition (plus), MMPS systems use the operations minimization and scaling. They are discrete-event (DE) systems, which means that the changing of the states is triggered by the occurrence of events and (part of) the states in the state vector represent time instances. One way to control MMPS systems is by using Model predictive control (MPC). This is a powerful
on-line control strategy that uses a receding horizon. However, an efficient control procedure that works for all time-invariant DE MMPS systems had not yet been described. The goal of this master thesis is to fully design such a framework. To achieve this, the state vector is altered, such that the difference in the states that represent a time instance is included as well. Next to this, the MPC problem on an MMPS system is altered to a Mixed integer quadratic programming (MIQP) problem, in order to optimize it more efficiently. That this framework
works is supported by a stability analysis. Next to that, it is tested on a simulation example of an urban railway line. Based on this example, it is shown that the procedure does indeed work. The thesis ends with several suggestions for future research.

As the age of digitization evolves rapidly, there is an ever-increasing demand for improving precision and decreasing production times for industrial automation in general, and semiconductor manufacturing in particular. As these complex machines incorporate flexure-based elements to overcome friction and backlash, structural vibrations pose a new challenge. Hence, the need for controlling and quickly damping these vibrations are paramount. In this thesis, a novel reset-based bandpass filter that employs velocity feedback to achieve finite-time vibration suppression for damped systems is introduced. The development of this filter stems from an energy based mechanistic approach, providing a clear understanding of the underlying mechanism for the improved transient response, which also motivates the use of reset. Systematic tuning rules based on describing functions are also developed to enable design in the frequency-domain, thereby increasing its relevance for industries. Finally, the effectiveness of the Resetting Velocity Feedback framework for improved transient damping is demonstrated experimentally on a single degree-of-freedom flexure stage. The results are compared to a linear bandpass filter and validates the advantages of reset control for achieving better transient damping compared to linear control.

As society's mutual problems grow, there is an increasing demand for understanding the intra- and inter-cultural differences. Rising polarization within national borders and stranded dialogues between nations on mutual problems, daily reach the headlines. It is argued that current models of opinion only scratch the surface of actual human opinion formation with the traditional value based exact approach. Developments in the domain of natural language processing highlight the overlap between real world cultural biases and biased language models. Although within their own domain these biases are seen as problematic, it is argued that it is exactly this associative prejudice that can be used to model human like opinion formation. This idea is emphasized by an conceptual approach on opinion, belief and knowledge, in which only the probability of an association distinguishes between 'objective' and 'subjective'. Through this concept, and by using identified significant cognitive tendencies, a framework is developed for inferring an opinion from text. Using this framework, an opinion model based on a language model is proposed. The contribution of this thesis is two-fold. Firstly, the proposed model provides evidence that agent perception through language is a significant model element, in which the bias in a language model can possibly be exploited to approach cultural perception. Secondly, as the bridge between the two domains has not yet been build, the findings of the results as well as the research process give direction for much needed future research.

Temporomandibular disorders (TMD) affect about 5-12 percentage of individuals with consequences such as jaw noises, clicking, myofascial pain, discomfort, limited mandibular range of motion and stress. Treatments depend on the cause and extent of the damage and part of the joint or jaw affected. When exact aetiology of TMD is unclear, generic treatments (splint therapy) are offered. Different oral activities performed on a daily-basis result in different loading conditions on the joint, possibly triggering TMD. These need to be investigated to know the usage of the masticatory system and the potential damage, in order to perform specific treatments. Our work aims at developing an online algorithm that can classify oral tasks performed by individuals. It can be used during daytime or overnight’s sleep to see how often different activities are performed by subjects. A 4 stage wavelet decomposition was employed to the signals and then subjected to feature extraction to train a support vector machine algorithm with. The prediction accuracy was found to be 90 percent for a group of selected oral activities (static, jaw opening, chewing and maximal voluntary clenching). The algorithm had about 80 percent prediction accuracy when classifying both functional (chewing, jaw opening and static) and parafunctional activities (grinding, incisal biting, maximal voluntary clenching, protrusion and laterotrusion) together. However, 80 percent accuracy is regarded as a set back due to the lack of more data. On reviewing the recognised activities, further research on any overuse of muscles or loading on the jaw joint during each activity can be conducted to give specific treatment and therapy preventing any deteriorating actions. Thus,the developed classification algorithm works as a prototype for future studies on online recognition of oral activities.

The olivocerebellar system plays a crucial role in control of movements of the human body in terms of coordination, precision and timing. Long-term plasticity is directly linked to motor learning and control. In this research, we developed a phenomenological model of the olivocerebellar system with balancing of long-term potentiation (LTP) and long-term depression (LTD) at the parallel fiber-Purkinje cell (PF-PC) synapse. By ranging the PF input over frequencies, we found that PCs can select frequencies in a highly non-linear manner. There is a sharp contrast in synaptic weight change between neighbouring frequencies, which is caused by the temporal spiking property of the inferior olive (IO) cell. This research found a novel signal processing capability of the PC.