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Time-domain Dynamic State Estimation for Unbalanced Three-phase Power systems

Journal of Modern Power systems and Clean Energy 2023年第2期11卷 446-454页

作者： Martin Pfeifer Felicitas Mueller Steven de Jongh Frederik Gielnik Thomas Leibfried Sören Hohmann Institute of Control Systems(IRS) KarlsruheGermany Institute of Electrical Energy Systems and High Voltage Technology(IEH)at the Karlsruhe Institute of Technology(KIT) 76131 KarlsruheGermany

In this paper,we present a time-domain dynamic state estimation for unbalanced three-phase power *** dynamic nature of the estimator stems from an explicit consideration of the electromagnetic dynamics of the network,i.e.,the dynamics of the electrical *** enables our approach to release the assumption of the network being in quasi-steady ***,based on the line dynamics,we derive a graphbased dynamic system *** handle the large number of interacting variables,we propose a port-Hamiltonian modeling *** on the port-Hamiltonian model,we then follow an observer-based approach to develop a dynamic *** estimator uses synchronized sampled value measurements to calculate asymptotic convergent estimates for the unknown bus voltages and *** design and implementation of the estimator are illustrated through the IEEE 33-bus *** simulations verify the estimator to produce asymptotic exact estimates,which are able to detect harmonic distortion and sub-second transients as arising from converterbased resources.

关键词： Dynamic state estimation power system harmonic observer port-Hamiltonian system static state estimation transient

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Enabling Industrial Security via Certificate Management Concepts in the Life Cycle of a Production Gray-Box 50

Enabling Industrial Security via Certificate Management Conc...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Madsen, Marwin Geib, Benedikt Barth, Mike Institute of Control Systems Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (纸本)9781665464543

The increasing IT/OT-Security threat for industrial plants is leading to new regulations such as the EU Cyber Resiliance Act. At the same time, innovations such as flexible production systems are being driven forward, resulting in changes to the automation architecture and therefore also to possible attack vectors. Existing security concepts must be reviewed for these developments, whereby the use of certificates and therefore the use of a public key infrastructure is fundamental for many security mechanisms. This paper analyzes especially new system architectures like modular production cells or interconnected machine parts with respect to security. For this purpose, different architectural principles such as Gray- and Black-Box are used as a basis and placed in the context of current specifications of certificate management. © 2024 IEEE.

关键词： Industrial plants

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Actively pulse shaped hybrid rare-earth-doped silica fiber/Ho3+:YAG MOPA system at 2048 nm

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Optics Express 2025年第10期33卷 20467-20482页

作者： Rupp, Marius Lorenz, Dominik Goth, Katharina Deutsch, Johannes Schneider, Julian Romano, Clément Eichhorn, Marc Kieleck, Christelle Gutleuthausstraße 1 Ettlingen76275 Germany Institute of Control Systems Karlsruhe Institute of Technology Fritz-Haber-Weg 1 Karlsruhe76131 Germany

In this study, an efficient hybrid fiber and solid-state master oscillator power amplifier (MOPA) system is introduced. This system is composed of an actively pulse-shaped Ho3+- and Tm3+-doped silica fiber MOPA at 2048 nm. A subsequent Ho3+:YAG power amplifier stage enables the efficient scaling of the pulse energy. By utilizing a novel cascaded crystal configuration, designed with a numerical simulation model, the Ho3+:YAG stage has been optimized for operation at 2048 nm wavelength. This operation at wavelengths much shorter than the typical emission peaks around 2090 nm showcases the possibility of using Ho3+:YAG as a broadband amplification medium. With a signal input power of 24 W at the crystal stage, we achieved an output power of 81.6 W. This corresponds to a gain of 5.3 dB with a slope efficiency of 68 % with respect to the launched pump at 1908 nm. The fiber/bulk hybrid approach enabled an increase in the energy of the rectangular-shaped nanosecond pulses from an initial 0.48 mJ to 1.63 mJ at 50 kHz, surpassing the pulse scaling limitations for standard thulium-doped silica fibers. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Rare earth-doped fibers

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Intention-Aware Decision-Making for Mixed Intersection Scenarios 17

Intention-Aware Decision-Making for Mixed Intersection Scena...

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17th IEEE International Symposium on Applied Computational Intelligence and Informatics, SACI 2023

作者： Varga, Balint Yang, Dongxu Hohmann, Soren Institute of Control Systems Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (纸本)9798350321104

This paper presents a white-box intention-aware decision-making for the handling of interactions between a pedestrian and an automated vehicle (AV) in an unsignalized street crossing scenario. Moreover, a design framework has been developed, which enables automated parameterization of the decision-making. This decision-making is designed in such a manner that it can understand pedestrians in urban traffic and can react accordingly to their intentions. That way, a human-like response to the actions of the pedestrian is ensured, leading to a higher acceptance of AVs. The core notion of this paper is that the intention prediction of the pedestrian to cross the street and decision-making are divided into two subsystems. On the one hand, the intention detection is a data-driven, black-box model. Thus, it can model the complex behavior of the pedestrians. On the other hand, the decision-making is a white-box model to ensure traceability and to enable a rapid verification and validation of AVs. This white-box decision-making provides human-like behavior and a guaranteed prevention of deadlocks. An additional benefit is that the proposed decision-making requires low computational resources only enabling real world usage. The automated parameterization uses a particle swarm optimization and compares two different models of the pedestrian: The social force model and the Markov decision process model. Consequently, a rapid design of the decision-making is possible and different pedestrian behaviors can be taken into account. The results reinforce the applicability of the proposed intention-aware decision-making. © 2023 IEEE.

关键词： Decision making

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End-to-End Trainable Deep Neural Network for Radar Interference Detection and Mitigation

End-to-End Trainable Deep Neural Network for Radar Interfere...

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2023 IEEE International Radar Conference, RADAR 2023

作者： Klemp, Marvin Chen, Shengyi Wagner, Royden Lauer, Martin Institute of Measurement and Control Systems Karlsruhe Institute of Technology Karlsruhe76131 Germany Institute of Communication Acoustics Ruhr-Universität Bochum Bochum44801 Germany

ISBN: (纸本)9781665482783

The automotive radar sensor is an essential sensor for intelligent transportation systems. However, the increasing number of such systems leads to mutual interference between sensors. Radar interference can be a severe disturbance, where targets within the victim radar are not perceived anymore if no countermeasures are taken. To prevent fatal injuries caused by this disturbance, several classical and deep-learning based methods to detect and mitigate radar interference have been proposed. We present an end-to-end trainable deep neural network for radar interference detection and mitigation. We evaluate different radar interference detection and mitigation methods on a large scale urban driving dataset using simulated radar interference. To our knowledge, this is the first end-to-end trainable architecture for interference detection and mitigation in the time domain. We show that our method achieves state-of-the-art results in interference detection and mitigation. Code for our method, reference methods, simulation, and the evaluation is available at https://***/KIT-MRT/ridam. © 2023 IEEE.

关键词： Large dataset

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Data-Driven Online Estimation of Driver Model Parameters for Vehicle Trajectory Prediction

Data-Driven Online Estimation of Driver Model Parameters for...

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International Conference on Intelligent Transportation

作者： Johannes Fischer Christoph Stiller Institute of Measurement and Control Systems Karlsruhe Institute of Technology (KIT) Karlsruhe Germany

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Fast and accurate trajectory prediction is crucial for the development and validation of automated driving systems. Using driver models for prediction is a promising approach to achieve this since they can produce realistic driving behavior at low computational cost. To produce high-quality predictions, the driver model parameters need to be adapted to the current traffic situation and observed driving behavior online. Our work combines data-driven methods with driver models to obtain realistic short-term trajectory predictions. We propose to train machine learning models to predict the driver model parameters that best capture the observed behavior of other vehicles. We use attention-based architectures to process sequential input data and predict the driver model parameters as a weighted sum of prototypes, thus ensuring that the predicted driving model parameters are realistic. Compared to particle filter-based state-of-the-art methods, our approach profits from the rich representational capabilities of learned models and the high online runtime efficiency of driver models. We show that our approach outperforms state-of-the-art methods for online driver model parameter estimation on a real-world traffic dataset.

关键词： Adaptation models Runtime Parameter estimation Computational modeling Prototypes Predictive models Data models Trajectory Driver behavior Vehicles

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Panoptic Segmentation from Stitched Panoramic View for Automated Driving

Panoptic Segmentation from Stitched Panoramic View for Autom...

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IEEE Symposium on Intelligent Vehicle

作者： Christian Kinzig Henning Miller Martin Lauer Christoph Stiller Institute of Measurement and Control Systems Karlsruhe Institute of Technology (KIT) Karlsruhe Germany

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

Precise object detection is crucial in automated driving. In contrast to lidar and radar sensors, cameras provide high-resolutional measurements at comparatively low cost. A state-of-the-art method for object detection using camera images is panoptic segmentation, which combines semantic and object instance information. Current public datasets use multiple cameras to cover a larger area of the environment. But, the limited field of view occludes objects. As a results, on the one hand, the correct dimensions of objects cannot be captured and, on the other hand, false detections can occur. Objects can also be detected multiple times in the overlapping image area. To track dynamic objects, duplicate detections must be filtered. Rather than directly segmenting all camera images individually, we first stitch them into a horizontal panorama. Using a stitched surround view avoids detection difficulties at the boundaries of the individual images. For this purpose, we leverage the EfficientPS pre-trained network architecture and adapt it for use with panoramic images. In our evaluation, we demonstrate the improvement in panoptic quality of a stitched surround view. In addition, we separately compare the panoptic quality in the overlapping image areas between the panorama and the individual images. Finally, we show further advantages of panoramic images in terms of inference time in runtime analysis.

关键词： Image segmentation Runtime Radar measurements Semantics Object detection Network architecture Cameras

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MAP-Former: Multi-Agent-Pair Gaussian Joint Prediction

MAP-Former: Multi-Agent-Pair Gaussian Joint Prediction

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IEEE Symposium on Intelligent Vehicle

作者： Marlon Steiner Marvin Klemp Christoph Stiller Institute of Measurement and Control Systems Karlsruhe Institute of Technology (KIT) Karlsruhe Germany

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

There is a gap in risk assessment of trajectories between the trajectory information coming from a traffic motion prediction module and what is actually needed. Closing this gap necessitates advancements in prediction beyond current practices. Existing prediction models yield joint predictions of agents’ future trajectories with uncertainty weights or marginal Gaussian probability density functions (PDFs) for single agents. Although, these methods achieve high accurate trajectory predictions, they only provide little or no information about the dependencies of interacting agents. Since traffic is a process of highly interdependent agents, whose actions directly influence their mutual behavior, the existing methods are not sufficient to reliably assess the risk of future trajectories. This paper addresses that gap by introducing a novel approach to motion prediction, focusing on predicting agent-pair covariance matrices in a "scene-centric" manner, which can then be used to model Gaussian joint PDFs for all agent-pairs in a scene. We propose a model capable of predicting those agent-pair covariance matrices, leveraging an enhanced awareness of interactions. Utilizing the prediction results of our model, this work forms the foundation for comprehensive risk assessment with statistically based methods for analyzing agents’ relations by their joint PDFs.

关键词： Analytical models Uncertainty Intelligent vehicles Focusing Probability density function Predictive models Trajectory

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Shared Telemanipulation with VR controllers in an Anti Slosh Scenario

Shared Telemanipulation with VR Controllers in an Anti Slosh...

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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Grobbel, Max Varga, Balint Hohmann, Sören Fzi - Forschungszentrum Informatik Karlsruhe76131 Germany Institute of Control Systems Karlsruhe Institute of Technology Karlsruhe76131 Germany

ISBN: (纸本)9798350337020

Telemanipulation has become a promising technology that combines human intelligence with robotic capabilities to perform tasks remotely. However, it faces several challenges such as insufficient transparency, low immersion, and limited feedback to the human operator. Moreover, the high cost of haptic interfaces is a major limitation for the application of telemanipulation in various fields, including elder care, where our research is focused. To address these challenges, this paper proposes the usage of nonlinear model predictive control for telemanipulation using low-cost virtual reality controllers, including multiple control goals in the objective function. The framework utilizes models for human input prediction and task-related models of the robot and the environment. The proposed framework is validated on an UR5e robot arm in the scenario of handling liquid without spilling. Further extensions of the framework such as pouring assistance and collision avoidance can easily be included. © 2023 IEEE.

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Cooperative Decision-Making in Shared Spaces: Making Urban Traffic Safer Through Human-Machine Cooperation 21

Cooperative Decision-Making in Shared Spaces: Making Urban T...

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21st IEEE International Symposium on Intelligent systems and Informatics, SISY 2023

作者： Varga, Balint Yang, Dongxu Martin, Manuel Hohmann, Soren Institute of Control Systems Karlsruhe Institute of Technology Karlsruhe Germany Department Interactive Analysis and Diagnosis Fraunhofer IOSB Karlsruhe Germany

ISBN: (纸本)9798350343366

In this paper, a cooperative decision-making is presented, which is suitable for intention-aware automated vehicle functions. With an increasing number of highly automated and autonomous vehicles on public roads, trust is a very important issue regarding their acceptance in our society. The most challenging scenarios arise at low driving speeds of these highly automated and autonomous vehicles, where interactions with vulnerable road users likely occur. Such interactions must be addressed by the automation of the vehicle. The novelties of this paper are the adaptation of a general cooperative and shared control framework to this novel use case and the application of an explicit prediction model of the pedestrian. An extensive comparison with state-of-the-art algorithms is provided in a simplified test environment. The proposed approach is also implemented on the experimental vehicle. The results show the superiority of the proposed model-based algorithm and its suitability for real-world applications. © 2023 IEEE.

关键词： Autonomous vehicles

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