Search

Vehicle Platooning has emerged in recent years as a promising method to reducethe fuel consumption of heavy trucks in long-distance transportation. Platoon-ing lowers the fuel consumption by forming strings of vehicles driving very closebehind each other, thus reducing the air drag. However, in order to form aplatoon, vehicles have to follow a common route. As vehicles are scattered overa road network, some sort of high-level coordination and route planning is re-quired to unlock the full potential of this technique. In this thesis, we evaluatevarious methods to minimize the total fuel consumption of a large-scale systemby guiding vehicles to fuel-ecient paths.We develop a comprehensive mathematical formulation of the problem andpresent a global solution approach to the problem. The approach is implementedand the investigation of the fuel reduction potential shows promising results.However, an exponential increase in computational complexity makes it unsuitedfor problems involving more than 20 trucks, even on small road networks.As a consequence, we then promote a locally distributed approach whichis able to eciently solve large problems. We implement such a distributedalgorithm and run large simulations on a model of the German autobahn roadnetwork. Results show that signicant savings can be achieved for even a fewhundred vehicles. We also investigate the in uence of the number of trucks onthe total fuel savings and examine the eect of limiting the maximum traveltime of trucks.

We consider the problem of distributed primary and secondary frequency control in inverter-based microgrids with meshed topology. A dynamic phasor modeling approach is presented that accurately represents the dynamics of the grid while being suitably simple to be used in control design. Then, we propose a novel frequency-domain, fixed structure, multivariable control design method based on a convex approximation of quadratic constraints. The method is then used to design a distributed controller for primary and secondary frequency control in an inverter-based distribution grid that guarantees stability and satisfies various performance goals.

Understanding hierarchical self-assembly of biological structures requires real time measurement of the self-assembly process over a broad range of length- and timescales. The success of high-speed atomic force microscopy (HS-AFM) in imaging small scale molecular interactions has fueled attempts to introduce this method as a routine technique for studying biological and artificial self-assembly processes. Current state of the art HS-AFM scanners achieve their high imaging speed by trading achievable field of view for bandwidth. This limits their suitability when studying larger biological structures. In ambient conditions, large range scanners with lower resonance frequencies offer a solution when combined with first principle model-based schemes. For imaging molecular self-assembly process in fluid however, such traditional control techniques are less suited. In liquid, the time-varying changes in the behavior of the complex system necessitates frequent update of the compensating controller. Recent developments in data-driven control theory offer a model-free, automatable approach to compensate the complex system behavior and its changes. Here we present a data-driven control design method to extend the imaging speed of a conventional AFM tube scanner by one order of magnitude. This enabled the recording of the self-assembly process of DNA tripods into a hexagonal lattice at multiple length scales.

The scope of this thesis encompasses two main subjects: fixed-structure data-driven control design on one side, and control design in power systems on the other. The overall goal is to identify challenging and relevant problems in power systems, to express them as rigorous specifications from the viewpoint of control systems, and to solve them by developing and applying advanced methods in robust control. This work aims to combine expertise from both fields to open up a holistic perspective and bridge the gap between control and power systems. First, the derivation of a novel fixed-structure, data-driven frequency-domain control design method for multivariable systems is described. A key feature of the method is that only the frequency response of the plant is required for the design, and no parametric model is required. The designed controllers are fully parametrized in terms of matrix polynomial functions and can take a centralized, decentralized or distributed structure. The controller performance is formulated as H_2 and H_infinity constraints on any loop transfer function. A convex formulation of the optimization problem is derived, and it is shown that the solution converges to a locally optimal solution of the original problem. The versatility of the design method is demonstrated in various simulation examples, as well as in experiments on two electromechanical setups. Next, a frequency-domain modeling approach for power grids is discussed. A model based on dynamic phasors is developed that represents the electromagnetic and electromechanic dynamics of lines, inverters, synchronous machines and constant power loads. It also offers a modular structure that makes it straightforward to combine white-, grey- and blackbox models in a single framework. Then, the control design method and dynamic phasor model are applied in two relevant power systems case studies. First, the design of a decentralized current controller for parallel, grid-connected voltage source inverters in a typical distribution grid is considered. It is shown how performance specifications can be formulated as frequency-domain constraints in order to attenuate the resonances introduced by the output filters and coupling effects, and to provide robustness against model uncertainties and grid topology changes. The controllers for all VSIs are designed in a single step, and stability and performance is guaranteed by design. Furthermore, an approach for plug-and-play control design is presented. The results are validated in numerical simulation as well as in power-hardware-in-the-loop experiments. The second study concerns the design of a distributed controller that combines primary and secondary frequency and voltage control for an islanded, meshed low-voltage grid with any number of voltage source inverters and synchronous generators in a single framework. No assumption on the R/X-ratio of the lines is made, and it is shown how advanced control specifications such as proportional active power sharing, zero frequency steady-state error and decoupling can be formulated as constraints on the norm of weighted sensitivity functions. Furthermore, the communication delays of the distributed controller are considered exactly during the design. The controller is implemented in numerical simulation, and results show significantly improved performance as compared to the classical hierarchical structure.

This paper treats the problem of primary and secondary control design in low-inertia power grids with mixed lines and a large amount of inverter-interfaced generation. A dynamic phasor model is developed that represents the electromagnetic and electromechanic dynamics of lines, inverters, synchronous machines and constant power loads. The model offers a straightforward way to combine white-, grey- and black-box models, and its structure lends itself well to control design. In a next step, a novel method to design fixed-structure robust controllers based on the frequency response of multivariable systems and convex optimization is presented. The method offers an intuitive way to define the control performance specifications, and is able to directly design discrete-time controllers. Finally, the potential of the control design method and the dynamic phasor model is demonstrated in a comprehensive example. In three scenarios it is illustrated how the approach can be used to significantly improve frequency and voltage transient performance in low-inertia power grids. Decentralized as well as distributed architectures for primary and secondary control are studied, and results are validated in simulation.

A novel method to design data-driven, fixed-structure controllers with H2 and H∞ performance objectives is presented. The control design problem is transformed into a convex optimization problem with linear matrix inequality constraints, which can be solved efficiently with standard solvers. The method is used to design a data-driven controller for an atomic-force microscope. The closed-loop performance of the calculated controller is validated on a real setup.

OBJECTIVE We aimed to determine the incidence of all vestibular symptoms in a large interdisciplinary tertiary emergency department (ED) and to assess stroke prevalence, and frequency of other life-threatening aetiologies. METHODS In this 1-year retrospective study, we manually screened all medical records of 23,608 ED visits for descriptions of vestibular symptoms. Symptoms were classified according to the International Classification of Vestibular Disorders of the Bárány Society. We evaluated all patients older than 16 years in whom vestibular symptoms were the main or accompanying complaint. We extracted clinical, radiological, and laboratory findings as well as aetiologies from medical records. RESULTS We identified a total of 2596 visits by 2464 patients (11% of ED visits) who reported at least one vestibular symptom. In 1677/2596 visits (64.6%), vestibular symptoms were the main reason for the ED consultation. Vestibular symptoms were classified as dizziness (43.8%), vertigo (33.9%), postural symptoms (6.5%), or more than one symptom (15.8%). In 324/2596 visits (12.5%), cerebrovascular events were the aetiology of vestibular symptoms, and in 355/2596 visits (13.7%), no diagnosis could be established. In 23.8% of visits with vestibular symptoms as the main complaint, the underlying condition was life-threatening. CONCLUSION Frequency and impact of vestibular symptoms in patients visiting the ED were higher than previously reported, and life-threatening aetiologies such as strokes are common. Therefore, awareness among physicians regarding the importance of vestibular symptoms has to be improved.

This paper presents the practical aspects and application of a novel data-driven, fixed-structure, robust control design method. Only the frequency response data of the system is needed for the design, and no parametric model is required. The method can be used to design fully parametrized continuous- or discrete-time matrix transfer function controllers. The control performance is specified as constraints on the $H_{\infty}$ or $H_2$ norm of weighted sensitivity functions, and a convex formulation of the robust design problem is proposed. An application of the presented method is explored on an experimental setup, where a multivariable controller for a gyroscope is designed based only on the measured frequency response of the system.

The frequency-domain data of a multivariable system in different operating points is used to design a robust controller with respect to the measurement noise and multimodel uncertainty. The controller is fully parametrized in terms of matrix polynomial functions and can be formulated as a centralized, decentralized or distributed controller. All standard performance specifications like H2, H∞ and loop shaping are considered in a unified framework for continuous- and discrete-time systems. The control problem is formulated as a convex-concave optimization problem and then convexified by linearization of the concave part around an initial controller. The performance criterion converges monotonically to a local optimum or a saddle point in an iterative algorithm. The effectiveness of the method is compared with fixed-structure controller design methods based on non-smooth optimization via multiple simulation examples.

A dynamic phasor model for low- and medium-voltage distribution grids is presented. This model accurately represents the transient dynamics of this type of grid, and its low order makes it well suited for control design. A novel fixed-structure, robust control design method based on convex optimization is then used to design a decentralized low-order controller that shows a significantly improved transient performance compared to classical droop controllers. The performance of the designed controller is then validated in a simulation example for voltage and frequency control of an islanded medium-voltage distribution grid.