Echtzeitfähige Schätzung dynamischer Zustände im Sattelzug

Moderne PKW sind umfangreich mit Sensorik sowie darauf basierenden Fahrassistenzsystemen ausgestattet und entwickeln sich stetig weiter hin zu autonomen Fahrzeugen. Eine ähnliche Si- tuation ist bei der Zugmaschine von Sattelzügen zu vernehmen. Eine genauere Betrachtung des Sattelaufliegers offenbart, dass dieser lediglich in einem sehr geringen Maße mit elektronischen Komponenten ausgestattet ist. Dies hat zur Folge, dass aufgrund fehlender fahrdynamischer und sicherheitsrelevanter Informationen der Sattelauflieger sich potenziell zum Hindernis bei der Ent- wicklung autonom fahrender Sattelzüge entwickeln kann. Um dem vorzubeugen, werden in dieser Arbeit Ansätze erarbeitet, mit denen auf Grundlage von heute im Sattelauflieger serienmäßig verfügbarer Sensorik zusätzliche Informationen über fahrdynamische Zustände des Aufliegers generiert werden können. Da die direkte messtechnische Erfassung einiger Zustände teilweise nur mit enormem technischen Aufwand realisierbar ist, kommen für diese häufig Schätzverfahren in Betracht. Grundsätzlich eignen sich zur Schätzung dynamischer Zustände verschiedene Methoden. In der Fahrzeugtechnik wurden in der Vergangenheit überwiegend modellbasierte Ansätze herangezogen. Diese haben zur Eigenschaft, dass sie physikalisch interpretierbar sind, erfordern gleichzeitig jedoch ein tiefgreifen- des Systemverständnis. In der jüngeren Vergangenheit kommen vermehrt datenbasierte Methoden zum Einsatz, motiviert durch die erfolgreiche Anwendung in anderen Forschungsgebieten wie der Bildverarbeitung, der Signalverarbeitung und der Robotik. Datenbasierte Methoden sind in der Lage, komplexe Zusammenhänge auf Grundlage von Messdaten zu erlernen, ohne dass eine detaillierte physikalische Kenntnis über das zugrundeliegende System erforderlich wäre. Darunter leidet die Interpretierbarkeit des Lösungswegs. Im Fokus dieser Arbeit stehen die Analyse und der Vergleich von modell- und datenbasierten Methoden zur echtzeitfähigen Schätzung des Knick- winkels und der Reifenquerkräfte im Sattelauflieger. Die Ansätze bedienen sicher keiner über den Serienstandard hinausgehender Sensoren und erfordern keine Kommunikation zur Zugmaschine. Die gewählten Zielgrößen liefern erweiterte Kenntnisse über den dynamischen Fahrzustand sowie den Lasteintrag auf das Fahrwerk und dienen zur Überwachung des Verschleißzustands sicherheits- relevanter Fahrwerkskomponenten. In einem ersten Schritt erfolgen die Untersuchungen in der Simulation. Dafür und für den modellbasierten Ansatz wird ein Sattelzugquerdynamikmodell ent- wickelt und in Anpassung an ein Versuchsfahrzeug parametriert. In einem zweiten Schritt werden die Erkenntnisse aus der Simulation auf das Versuchsfahrzeug übertragen und in experimentellen Versuchsfahrten auf einer Teststrecke sowie im öffentlichen Straßenverkehr validiert.Modern passenger cars are extensively equipped with sensors as well as driver assistance systems based on them and are constantly developing towards autonomous vehicles. A similar situation can be found in the truck unit of truck-semitrailer combinations. However, if the semitrailer is examined more closely, it can be seen that it is hardly equipped with electronic components. As a result, the semitrailer can potentially become an obstacle in the development of autonomously driving truck-semitrailer combinations due to missing dynamic and safety relevant information. In order to prevent this, in the present work approaches are being developed that can be used to generate additional information about the dynamic states of the semitrailer. These are solely based on sensors that are available as standard in a semitrailer today. As the direct measurement of some states can only be realized with enormous technical effort, state estimation methods are often considered. In general, various methods are suitable for estimating dynamic states. In the past, model-based approaches were mainly used in vehicle technology. These have the property that they can be interpreted physically, but at the same time require a profound understanding of the system. More recently, data-based methods have been increasingly used, motivated by their successful application in other research areas such as image processing, signal processing, and robotics. Data-based methods are able to learn complex physical relationships based on measured data without requiring detailed knowledge of the underlying system. As an outcome, the interpretability of the approach suffers. The focus of this work is the analysis and comparison of model-based and data-based methods for real-time estimation of the hitch angle and the lateral tire forces in a semitrailer which supports condition monitoring of safety relevant chassis components. These do not require additional beyond serial sensors or communication to the truck. The chosen target variables provide extended knowledge about the dynamic state and the impact on the chassis. In a first step, the investigations are carried out in simulations. For this and for the model-based approach, a truck-semitrailer lateral dynamics model is developed and parameterized in adaptation to a test vehicle. In a second step, the findings from the simulation are transferred to the test vehicle and validated in experimental test drives on a test track as well as in public road traffic

Optimized Tuning of an EKF for State and Parameter Estimation in a Semitrailer

The Extended Kalman Filter (EKF) is a well-known method for state and parameter estimation in vehicle dynamics. However, for tuning the EKF, knowledge about the process and measurement noise is needed, which is usually unknown. Tuning the noise parameters manually is very time consuming, especially for systems with many states. Automated optimization based on the filtering errors promises less application time and better estimation performance, but also requires computing resources. This work presents two approaches for estimating the noise parameters of an EKF: A particle swarm optimization (PSO) and a gradient-based optimization. The EKF is applied to a nonlinear vehicle model of a tractor-semitrailer for estimating the steering and articulation angle as well as lateral and vertical tire forces based on real measurement data with different trailer loadings. Both methods are compared to each other to achieve the best estimation performance

Control-relevant Model Selection for Multiple-Mass Systems

Physically motivated parametric models are the basis of several techniques related to control design. Industrial model-based controller tuning methods include pole placement, symmetric optimum and damping optimum. The challenge is that the resulting model-based controller is satisfactory only if the underlying model is appropriate. Typically, a set of potential models is known a priori, but it is not known, which model should be used. So, the critical question in model-based controller tuning is that of model selection. Existing approaches for model selection are mostly based on maximizing accuracy, but there is no reason why the most accurate model should also be the optimal model for control design. Given the overall aim to design a high-performance controller, in this paper the best model is considered as the one that has the potential to give a model-based controller the highest performance. The proposed method identifies parametric candidate models for control design. Then, a nonparametric model is used to predict the actual performance of the various controllers on the real system. A validation with two industry-like testbeds shows success of the method

Data-Based Energy Demand Prediction for Hybrid Electrical Vehicles

To achieve a resource-efficient automotive traffic, modern driver assistance systems minimize the vehicle’s energy demand through speed optimization algorithms. Based on predictive route data, the required energy for upcoming operation points has to be determined. This paper presents a method to predict the energy demand of a hybrid electrical vehicle. Within this method, data-based approaches, such as neural networks, Gaussian processes, and look-up tables, are applied and assessed regarding their ability to predict the behavior of separate powertrain parts. The applied approaches are trained using measured data of a test vehicle. As a result, for every separate powertrain part, the best-suited data-based approach is selected to obtain an optimal energy demand prediction method. On a validation data set, this method is able to predict the transmission ratio of the gearbox causing a rmse of 0.426. The combustion engine’s torque prediction results in an rmse of 19.01 Nm and the electric motor torque prediction to 19.11 Nm. The root mean square error of the motor voltage results to 1.211 V

Energy Demand Prediction in Hybrid Electrical Vehicles for Speed Optimization

Targeting a resource-efficient automotive traffic, modern driver assistance systems include speed optimization algorithms to minimize the vehicle’s energy demand, based on predictive route data. Within these algorithms, the required energy for upcoming operation points has to be determined. This paper presents a model-based approach, to predict the energy demand of a parallel hybrid electrical vehicle, which is suitable to be used in speed optimization algorithms. It relies on separate models for the individual power train components, and is identified for a real test vehicle. On route sections of 5 to 7 km the averaged root mean square error for the state of charge prediction results to 0.91% while the required amount of fuel can be predicted with an averaged root mean square error of 0.05 liters

Procedural outcome & risk prediction in young patients undergoing transvenous lead extraction—a GALLERY subgroup analysis

BackgroundThe prevalence of young patients with cardiac implantable electronic devices (CIED) is steadily increasing, accompanied by a rise in the occurrence of complications related to CIEDs. Consequently, transvenous lead extraction (TLE) has become a crucial treatment approach for such individuals.ObjectiveThe purpose of this study was to examine the characteristics and procedural outcomes of young patients who undergo TLE, with a specific focus on identifying independent risk factors associated with adverse events.MethodsAll patients in the GALLERY (GermAn Laser Lead Extraction RegistrY) were categorized into two groups based on their age at the time of enrollment: 45 years or younger, and over 45 years. A subgroup analysis was conducted specifically for the younger population. In this analysis, predictor variables for all-cause mortality, procedural complications, and procedural failure were evaluated using multivariable analyses.ResultsWe identified 160 patients aged 45 years or younger with a mean age of 35.3 ± 7.6 years and 42.5% (n = 68) female patients. Leading extraction indication was lead dysfunction in 51.3% of cases, followed by local infections in 20.6% and systemic infections in 16.9%. The most common device to be extracted were implantable cardioverter-defibrillators (ICD) with 52.5%. Mean number of leads per patient was 2.2 ± 1.0. Median age of the oldest indwelling lead was 91.5 [54.75–137.5] months. Overall complication rate was 3.8% with 1.9% minor and 1.9% major complications. Complete procedural success was achieved in 90.6% of cases. Clinical procedural success rate was 98.1%. Procedure-related mortality was 0.0%. The all-cause in-hospital mortality rate was 2.5%, with septic shock identified as the primary cause of mortality. Multivariable analysis revealed CKD (OR: 19.0; 95% CI: 1.84–194.9; p = 0.018) and systemic infection (OR: 12.7; 95% CI: 1.14–142.8; p = 0.039) as independent predictor for all-cause mortality. Lead age ≥ 10 years (OR: 14.58, 95% CI: 1.36–156.2; p = 0.027) was identified as sole independent risk factor for procedural complication.ConclusionTLE in young patients is safe and effective with a procedure-related mortality rate of 0.0%. CKD and systemic infection are predictors for all-cause mortality, whereas lead age ≥ 10 years was identified as independent risk factor for procedural complications in young patients undergoing TLE

Wilde about Ulysses: Deleuzian assemblages and the importance of being Oscar

While Oscar Wilde’s influence on James Joyce has been explored by many scholars, the flamboyant playwright, novelist, journalist, and critic’s importance to Ulysses remains elusive. We suggest that Wildean avatars—Deleuzian ‘assemblages’ (Deleuze, 1980: 340)—both open the novel in the form of Buck Mulligan in ‘Telemachus’ and close its public narrative in the shape of D. B. Murphy in ‘Eumaeus’, before triggering the private resolutions of the main cast members in ‘Ithaca’ and ‘Penelope’. These assemblages bookend the novel’s opening and closing, and highlight overlooked queer themes in Ulysses, particularly the socially constructed nature of identity. Mulligan and Murphy, highly costumed and performative, not only establish Wilde’s foundational importance to Ulysses but epitomize the queering influence that permeates it, a note that has only recently begun to be heard

The Digital Tea Leaves of Election 2000: The Internet and the Future of Presidential Politics

While the Internet may not have played the transformational role in the election of the U.S. President in 2000 that some predicted, this new medium of political communications suggested what an Internet-driven transformation in political communications might look like. After setting the stage by discussing the use of information and communications technologies (ICTs) by Sen. John McCain in the primary campaign, the researchers evaluate the Web sites of four major candidates for President of the United States over the course of the general election. Additionally, this article serves as a digital archive of Web pages caught at what many believe is the nascent stage of what might come to be the dominant medium for political communications in the decades to come

Image Hiding Scheme Based on the Atrial Fibrillation Model

An image communication scheme based on the atrial fibrillation (AF) model is presented in this paper. Self-organizing patterns produced by the AF model are used to hide and transmit secret visual information. A secret image is encoded into the random matrix of initial cell excitation states in the form of a dot-skeleton representation. Self-organized patterns produced by such initial cell states ensure a secure and efficient transmission of secret visual images. Procedures for digital encoding and decoding of secret images, as well as the sensitivity of the communication scheme to the perturbation of the AF model’s parameters are discussed in the paper