Bislang existieren keine unter Praxisbedingungen einsetzbaren Messmethoden für eine verzögerungsfreie Bestimmung der für die Prozessführung wichtigen Zustandsgrößen in Biogasanlagen. Die Arbeit „Anwendung des erweiterten KALMAN-Filters zur Zustandsbeobachtung in Biogasanlagen“ hat zum Ziel, den Stand der Technik in der Biogaserzeugung dahingehend weiter zu entwickeln, dass mittels einer softwarebasierten Systemlösung die für eine fundierte Einschätzung des Prozesszustands einer Biogasanlage benötigten Zustandsgrößen sowohl im Fermenter als auch im Zulauf bestimmt werden können. Das praktische Interesse besteht insbesondere darin, dass die Bestimmung unter Verwendung von standardmäßig an Biogasanlagen verfügbaren Messsystemen sowie unter den anlagentechnischen Randbedingungen erfolgen kann. Grundlage für eine Systemlösung zur Zustandsbeobachtung in Biogasanlagen ist ein mathematisches Modell, das die relevanten Teilprozesse der Methangärung abbildet und stellvertretend für das Realstoffsystem mit mathematischen Methoden der Prozesszustands- und Parameterschätzung untersucht werden kann. Die entsprechend des Stands der Technik verfügbaren Prozessmodelle zur Beschreibung anaerober biologischer Abbauprozesse ermöglichen die Berechnung der Prozesszustandsgrößen der anaeroben Flüssigphase, sofern die Zusammensetzung des zugeführten Substrats bekannt ist. Diese ist jedoch in technischen Anlagen zur Biogaserzeugung in der Regel unbekannt, da die eingesetzten Substrate den nachwachsenden Rohstoffen sowie den biologischen Rest- und Abfallstoffen zuzuordnen sind, die herkunftsbedingt eine wechselnde Zusammensetzung und Verfügbarkeit aufweisen. Praxistaugliche, verzögerungsfreie Messmethoden für die Substratcharakterisierung stehen derzeit ebenfalls nicht zur Verfügung, so dass diese Modelle bislang nur eingeschränkte praktische Anwendbarkeit aufweisen. Zielstellung der Arbeit ist die Entwicklung einer Systemlösung, die es auf der Grundlage der mathematischen Beschreibung des Prozesses ermöglicht, die Prozesseingangsgrößen (Substratmenge und Substratzusammensetzung) und die Prozesszustandsgrößen (anaerobe Milieubedingungen) aus den Prozessmessgrößen (Gasmenge und Gaszusammensetzung) zu berechnen. Dabei sind unter praktischen Bedingungen auftretende Informationsverluste in den Messdaten infolge Biogasentschwefelung, Gaszwischenspeicherung und Leistungssteuerung des BHKW zu berücksichtigen, die zu keiner Beeinträchtigung der Anwendbarkeit führen dürfen. Mit Messwerten, die im Rahmen von zwei Versuchsreihen am Realstoffsystem bestimmt worden sind, wurde der Prozess der Methangärung für zwei spezielle Anwendungsfälle simuliert und einer Bewertung in Bezug auf die Qualität der Zustandsbeobachtung unterzogen. Die berechneten Verläufe ergaben eine hinreichend genaue Übereinstimmung mit den Verläufen der analytisch bestimmten Prozesszustands- und Prozesseingangsgrößen. Darauf aufbauend können dann Systeme zur Bewertung des Prozesszustands und zur Prozessregelung eingesetzt und zur Optimierung der Prozessführung in Biogasanlagen angewendet werden. / Suitable control of anaerobic digestion for biogas production requires well-founded knowledge about the process states. Standard measurement categories in small and medium scaled biogas plants contain gas analysis (amount and composition) and pH measurement. The actual process and input states of the liquid phase are usually unknown. This work presents a methodology for estimation of the unknown states based on standard measurement equipment. The system solution consists of two parts, a model describing the dynamics of the process and a two-stage identification of process states and model parameters. Within the first stage quadratic differences between simulated and measured values are minimized by the least square method; second stage minimizes the covariance matrix of the estimation error using the extended Kalman filter. Application of the system solution provides an high potential to increase efficiency of biogas production process and utilization of renewable resources.

Modern day intelligent transportation system (ITS) relies on reliable and accurate estimated traffic parameters. Travel speed, traffic flow, and traffic state classification are the main traffic parameters of interest. These parameters can be estimated through efficient vision-based algorithms and appropriate camera sensor technology. With the advances in camera technologies and increasing computing power, use of monocular vision, stereo vision, and camera sensor fusion technologies have been an active research area in the field of ITS. In this thesis, we investigated stationary monocular and stereo camera technology for traffic parameters estimation. Stationary camera sensors provide large spatial-temporal information of the road section with relatively low installation costs. Two novel scientific contributions for vehicle detection and recognition are proposed. The first one is the use stationary stereo camera technology, and the second contribution is the fusion of monocular and stereo camera technologies. A vision-based ITS consists of several hardware and software components. The overall performance of such a system does not only depend on these single modules but also on their interaction. Therefore, a systematic approach considering all essential modules was chosen instead of focusing on one element of the complete system chain. This leads to detailed investigations of several core algorithms, e.g. background subtraction, histogram based fingerprints, and data fusion methods. From experimental results on standard datasets, we concluded that proposed fusion-based approach, consisting of monocular and stereo camera technologies performs better than each particular technology for vehicle detection and vehicle recognition. Moreover, this research work has a potential to provide a low-cost vision-based solution for online traffic monitoring systems in urban and rural environments.

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<p>  </p> <p>The development of an increasingly interconnected infrastructure and its rapid evolution demands engineering testing solutions capable of investigating realistically and with high accuracy the interactions among the different components of the problem to study. The examination of any of these components without losing the interaction of the other surroundings components is not only realistic, but also desirable. The more interconnected the whole system is, the greater the dependencies. Real-time Hybrid Simulation (RTHS) is a disruptive technology that has the potential to address this type of complex interactions or internal couplings by partitioning the system into numerical (better understood) substructures and experimental (unknown) substructures, which are built physically in the laboratory. These two types of substructures are connected through a transfer system (e.g., hydraulic actuators) to enforce boundary conditions in their common interfaces creating a synchronized cyber-physical system. However, despite the RTHS community has been improving these hybrid techniques, there are still important barriers in their core methodologies. Current control approaches developed for RTHS were validated mainly for linear applications with limited capabilities to deal with high uncertainties, hard nonlinearities, or extensive damage of structural elements due to plasticity. Furthermore, capturing the realistic dynamics of a structural system requires the description of the motion using more than one degree of freedom, which increases the number of hydraulic actuators needed to enforce additional degrees of freedom at boundary condition interface. As these requirements escalate for larger or more complex problems, the computational cost can turn into a prohibitive constraint. </p> <p>In this dissertation, the main research goal is to develop and validate a nonlinear controller with capabilities to control highly uncertain nonlinear physical substructures with complex boundary conditions and its parallel computational implementation for accurate and realistic RTHS. The validation of the proposed control system is achieved through a set of real-time tracking control and RTHS experiments that explore robustness, accuracy performance, and their trade-off </p>

Research in Unmanned Air Vehicles (UAV's) continues to push the limitations of size and weight. As technical advances have made UAV's smaller and less expensive, they have become more flexible and extensive in their roles. To continue using smaller and less expensive components while retaining and even enhancing performance requires more sophisticated processing of sensor data in order for the UAV to accurately determine its state and thereby allow the use of feedback in controlling the aircraft automatically. This work presents a three-stage state-estimation scheme for the class of UAV's know as Miniature Air Vehicles (MAV's). The first stage estimates pitch and roll, the second stage estimates heading, and the third stage produces a position estimate and an estimate of wind speed and direction. All three stages make use of the extended Kalman filter, a framework for using a system dynamic model to predict future states and to update the predictions using weighted sensor measurements as they become available, where the weighting is based on the relative uncertainty of the dynamic model and the sensors. Using the three-stage state esti-mation scheme, significant improvements in the estimation of pitch, roll and heading have been achieved in simulation and flight testing. Performance of the navigation (position and wind) stage is comparable to an existing baseline algorithms for position and wind, and shows additional promise for use in dead reckoning when GPS updates become unavailable.

In this dissertation we present advances in developing an autonomous air vehicle capable of navigating through unknown, indoor environments. The problem imposes stringent limits on the computational power available onboard the vehicle, but the environment necessitates using 3D sensors such as stereo or RGB-D cameras whose data requires significant processing. We address the problem by proposing and developing key elements of a relative navigation scheme that moves as many processing tasks as possible out of the time-critical functions needed to maintain flight. We present in Chapter 2 analysis and results for an improved multirotor helicopter state estimator. The filter generates more accurate estimates by using an improved dynamic model for the vehicle and by properly accounting for the correlations that exist in the uncertainty during state propagation. As a result, the filter can rely more heavily on frequent and easy to process measurements from gyroscopes and accelerometers, making it more robust to error in the processing intensive information received from the exteroceptive sensors. In Chapter 3 we present BERT, a novel approach to map optimization. The goal of map optimization is to produce an accurate global map of the environment by refining the relative pose transformation estimates generated by the real-time navigation system. We develop BERT to jointly optimize the global poses and relative transformations. BERT exploits properties of independence and conditional independence to allow new information to efficiently flow through the network of transformations. We show that BERT achieves the same final solution as a leading iterative optimization algorithm. However, BERT delivers noticeably better intermediate results for the relative transformation estimates. The improved intermediate results, along with more readily available covariance estimates, make BERT especially applicable to our problem where computational resources are limited. We conclude in Chapter 4 with analysis and results that extend BERT beyond the simple example of Chapter 3. We identify important structure in the network of transformations and address challenges arising in more general map optimization problems. We demonstrate results from several variations of the algorithm and conclude the dissertation with a roadmap for future work.

Machine learning models are vulnerable to adversarial attacks that add perturbations to the input data. Here we model and simulate power flow in a power grid test case and generate adversarial attacks for these measurements in three different ways. This is to compare the effect of attacks of different sizes constructed using various levels of knowledge of the model to see how this affects how often the attacks are detected. The three methods being one where the attacker has full knowledge of model, one where the attacker only has access to the measurements of the model, and the third method where the attacker has no knowledge of the model. By comparing these methods through how often they are detected by a residual-based detection scheme, one can argue that a data-driven attack only knowing the measurements is enough to add an error without being detected by the detection scheme. Using a linearized version of a state estimation is shown to be insufficient for generating attacks with full knowledge of the system, and further research is needed to compare the performance of the full knowledge attacks and the data-driven attacks. The attacks generated without knowledge of the system perform poorly and are easily detected.

This thesis presents a method, Ensemble Kalman Filter (EnKF), applied to a highresolution, shallow water equations model (DG ADCIRC-2DDI) of the Lower St. Johns River with observation data at four gauging stations. EnKF, a sequential data assimilation method for non-linear problems, is developed for tidal flow simulation for estimation of state variables, i.e., water levels and depth-integrated currents for overland unstructured finite element meshes. The shallow water equations model is combined with observation data, which provides the basis of the EnKF applications. In this thesis, EnKF is incorporated into DG ADCIRC-2DDI code to estimate the state variables. Upon its development, DG ADCIRC-2DDI with EnKF is first validated by implementing to a low-resolution, shallow water equations model of a quarter annular harbor with synthetic observation data at six gauging stations. Second, DG ADCIRC-2DDI with EnKF is implemented to a high-resolution, shallow water equations model of the Lower St. Johns River with real observation data at four gauging stations. Third, four different experiments are performed by applying DG ADCIRC-2DDI with EnKF to the Lower St. Johns River.

<p>The use of lower airspace for air taxi and cargo applications opens up exciting prospects for futuristic Unmanned Aircraft Systems (UAS). However, ensuring the safety and security of these UAS within densely populated urban areas presents significant challenges. Most modern aircraft systems, whether unmanned or otherwise, rely on the Global Navigation Satellite System (GNSS) as a primary sensor for navigation. From satellite navigations point of view, the dense urban environment compromises positioning accuracy due to signal interference, multipath effects, etc. Furthermore, civilian GNSS receivers are susceptible to spoofing attacks since they lack encryption capabilities. Therefore, in this thesis, we focus on examining the safety and cybersecurity assurance of UAS in dense urban environments, from both theoretical and experimental perspectives. </p> <p>To facilitate the verification and validation of the UAS, the first part of the thesis focuses on the development of a realistic GNSS sensor emulation using a Gazebo plugin. This plugin is designed to replicate the complex behavior of the GNSS sensor in urban settings, such as multipath reflections, signal blockages, etc. By leveraging the 3D models of the urban environments and the ray-tracing algorithm, the plugin predicts the spatial and temporal patterns of GNSS signals in densely populated urban environments. The efficacy of the plugin is demonstrated for various scenarios including routing, path planning, and UAS cybersecurity. </p> <p>Subsequently, a robust state estimation algorithm for dynamical systems whose states can be represented by Lie Groups (e.g., rigid body motion) is presented. Lie groups provide powerful tools to analyze the complex behavior of non-linear dynamical systems by leveraging their geometrical properties. The algorithm is designed for time-varying uncertainties in both the state dynamics and the measurements using the log-linear property of the Lie groups. When unknown disturbances are present (such as GNSS spoofing, and multipath effects), the log-linearization of the non-linear estimation error dynamics results in a non-linear evolution of the linear error dynamics. The sufficient conditions under which this non-linear evolution of estimation error is bounded are derived, and Lyapunov stability theory is employed to design a robust filter in the presence of an unknown-but-bounded disturbance. </p>