Vehicle platooning is often regarded as one of the most promising solutions for solving the road congestion issues. Experiments have successfully proven that longitudinal platooning can be safely achieved in real world applications using a platoon of several vehicles. Lateral control however, has received far less attention. The main difficulty with lateral platooning lies in the fact that the two possible vehicle following methods both have their own limitations. The direct vehicle following approach suffers from corner cutting, whereas with path following using path reconstruction is often unreliable and difficult to achieve in platooning applications. In this thesis work a new method is proposed which combines the strengths of both vehicle following methods, and at the same time negate their drawbacks.

During the exposure process of a wafer scanner, problems occur due to reticle heating. In this project, the consequences of reticle heating in terms of image aberrations are discussed along with how they could be solved with feedback controlled cooling. The problem is defined in terms of a performance index and constraints, which fits the Model Predictive Control (MPC) framework. The principle of MPC is explained and results are compared to the current state of the practice. The results show a significant improvement in performance at the cost of acceptable constraint violations.

Over the past years, the automotive industry has seen a constantly increasing level of automation of automotive vehicles. This increasing level of automation contributes to an increasing safety in traffic and a reduction of traffic congestions due tio faster response times and higher reliability, with respect to the human driver. The automation of a vehicle over the longitudinal and lateral degree of freedom requires additional safety measures to ensure safety of the passengers during automated maneuvers. In a lateral control scenario, the fault detection and isolation of faults occuring in the steering system belongs to these set of safety measures. By detecting and isolating faults of interest in this system, a decision process follows which will, for example, either compensate for the acting fault or bring the vehicle to a safe standstil in the case that the fault surpasses a non-acceptable threshold.

This work presents a set of novel methods for fault detection and isolation for a generalized set of linear time-invariant and parameter-varying systems. The faults under investigation comprise of an additive fault acting as an offset on the system and a multiplicative fault acting non-linearly on a set of known signals. Furthermore, the system is subjected to exogenous disturbances. The first challenge imposed is the lack of isolability of the additive and multiplicative fault using conventional linear estimation techniques. The second challenge imposed is the design of a fault detection mechanism for linear parameter-varying systems subject to exogenous faults and disturbances.

The first contribution of this research is a moving least-squares based approach as an extension to an existing nullspace computation based parity-space fault detection filter. This novel combination allows a decoupled estimation of the additive and multiplicative fault. The second contribution is an extension to the first contribution by attacking one of the largest sources of error: the dynamical content of the parity-space filter. The estimation performance of the combined fault detection and isolation filter of both contributions is provided with a guaranteed performance bound, providing not only an intuitive tool to push down the estimation error, but also shows possible trajectories for future work. The third and final theoretical contribution is the adaption of the linear time-invariant parity space method for a linear parameter-varying environment. A convex quadratic optimization problem is used which has an exact analytical solution for approximately rejecting the parameter-varying effects of the system.

A contribution of the more practical aspect of this thesis, is a demonstration of the applicability of the developed framework on real-life problems, both in a simulation setting and an experimental setting. The theoretical framework has been applied in a practical case study in fault detection and isolation for lateral control of autonomous vehicles. In this application, there is a particular interest in detecting and isolating the additive and multiplicative fault, acting on the steering mechanism of the vehicle, for compensation or decision making of the autonomous system in faulty scenarios. The experimental results confirm the theorems, showing that the faults can be detected and isolated both in theory and in practice.

This dissertation aims to develop a rigorous distributed approach to decision making using scenario-based techniques for large-scale networks of interconnected uncertain dynamical systems (called agents). A scenario program is a finite-dimensional optimization problem in which an objective function is minimized under constraints that are associated with finitely many, independently and identically distributed (i.i.d.), scenarios of a random parameter. Theoretical and practical interest in scenario programs originates from the fact that these problems are typically efficiently solvable while being closely related to robust and chance-constrained programs. In the former, the constraint is enforced for all admissible random parameters, whereas in the latter, the constraint is enforced up to a given level of probability. However, finding solutions of the resulting large-scale scenario optimization problem for uncertain networked systems poses several difficulties, e.g., computational cost for a central control unit.
The main contribution of this dissertation is the design of a technique to decompose a large-scale scenario program into small-scale distributed scenario programs for each agent. Building on existing results in literature, we provide novel guarantees to quantify the robustness of the resulting solutions in a distributed framework. In this setting, each agent needs to exchange some information with its neighboring agents that is necessary due to the statistical learning features of the proposed setup. However, this inter-agent communication scheme might give rise to some concerns about the agents' private information. We therefore present a novel privatized distributed framework, based on the so-called differential privacy concept, such that each agent can share requested information while preserving its privacy. In addition, a soft communication scheme based on a set parameterization technique, along with the notion of probabilistically reliable set, is introduced to reduce the required communication burden. Such a reliability measure is incorporated into the feasibility guarantees of agent decisions in a probabilistic sense. The theoretical guarantees of the proposed distributed scenario-based decision making framework coincide with the centralized counterpart, however the scaling of the results with the number of agents remains an issue.

One of the phases of the installation of Offshore Wind Turbines is the installation of the foundation for the turbine. Herein, a monopile is one of the most frequently used foundation-types. This monopile is a high steel cylinder, over 60 meters high, which supports the wind turbine to the seabed. Currently, monopiles are lifted with large boom cranes on jack-up ships. SeaState5 has developed a more efficient method for monopile installation by using the Grasshopper-T (GH-T), a new crane which skids the monopiles from the deck and places it onto the seabed. The system reduces the weight and footprint for jack-up ships.

This thesis consists of mechanical modeling the GH-T for the purpose of estimating the forces within the lifting system. A non-linear modeling method; Euler-Lagrange
(EL) method with constraints is used to capture the systems dynamic, non-smooth and non-linear behavior over the entire range of motions of the upending process. Secondly, for the purpose of simplification, yet capturing the mentioned behavior, the system is linearized around several areas of operation, analyzing local detectablity and observability. Thirdly, attempts are made to design linear observers and disturbance estimator to both estimate the states of the system and any disturbance applied on the monopile, like hydrodynamic forces. Finally, a non-linear observer is proposed to estimate the states along the entire upending process of the monopile. Experiments for model validation are performed on SeaState5’s 1:25 scale prototype for which a sensor setup has been designed.

Simulations show that linear observers based on the linearized model are able to estimate the states, and compute forces of interest, within a very small area of operations. The designed linear disturbance estimator proves to be ineffective in estimating disturbances and is only valid for the same small neighborhood around the equilibrium point. This motives the need for an observer capable of estimation over a broad range of motions. Therefore, a non-linear observer is designed. For the non-linear observer, the state recovery proves to be efficient and robust enough to cope with sensor noise and the high range of motions of the upending procedure. Experimental results show the model is able to capture the non-linear behavior of the real system and estimate forces of interest within acceptable margins, showing promises for an online state estimation using the non-linear observer.

The ever-increasing road transport demand in the developing and the developed world has resulted in road-traffic networks nearing maximum capacity. Traditional approaches to this problem, such as increasing road capacity, do not offer long-term solutions. Combined with rising environmental concerns, the demand for smarter solutions has never been higher. One such solution, which has gained significant ground in the past decades, is automated vehicle platooning.

A vehicle platoon is an interconnected dynamical system consisting of automated vehicles driving in close proximity, coordinating their movement through measurements and sometimes inter-vehicular communication. By allowing driving in close proximity, platoons have the potential to increase road capacity, whilst reducing fuel consumption. Platoons are subject to safety and performance requirements. In order to meet these requirements the vehicle platoon needs be string stable, such that effects of disturbances are not amplified in the upstream direction of the string of vehicles. String-stable platoons also help preventing ghost traffic jams, typically caused by human driver behaviour. Advancements in vehicle platooning research have mostly been concerned with longitudinal automation, and consequently with longitudinal string stability. However, driving at small inter-vehicular gaps also requires lateral automation. Naturally, this means that string stability in the lateral sense is required.

Therefore, the objective of this MSc thesis is to develop a lateral control method for automated vehicle platoons that yield string-stable behaviour. In the first part, an error model based on a vehicle-following control strategy, which uses vehicle path following, is derived. This model is then used to describe the path-following problem within a platoon. In the second part, a control strategy is proposed using the H∞ framework, such that path following and lateral string stability are guaranteed a-priori.

The robustness properties and the performance of the designed controller are analysed by means of frequency-domain analysis and time-domain simulations. Finally, path following and the string-stability properties of the controller have been validated experimentally. The experiments performed confirm the theoretical analysis, thereby showing that lateral string stability is obtainable using the proposed method.