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A Novel Observer Approach for Self Sensing of Single-Coil Digital Valves

IFAC-PapersOnLine 2017年第1期50卷 782-787页

作者： Braun T. Reuter J. Rudolph J. Institute of System Dynamics Konstanz University of Applied Sciences Konstanz 78462 Germany Chair of Systems Theory and Control Engineering Saarland University Campus A5 1 Saarbrücken 66123 Germany

Observer-based self sensing for digital (on–off) single-coil solenoid valves is investigated. Self sensing refers to the case where merely the driving signals used to energize the actuator (voltage and coil current) are available to obtain estimates of both the position and velocity. A novel observer approach for estimating the position and velocity from the driving signals is presented, where the dynamics of the mechanical subsystem can be neglected in the model. Both the effect of eddy currents and saturation effects are taken into account in the observer model. Practical experimental results are shown and the new method is compared with a full-order sliding mode observer. © 2017

关键词： Observer design Self sensing actuators Singular perturbation analysis

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An Input-Output Framework for Submanifold Stabilization

An Input-Output Framework for Submanifold Stabilization

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作者： Montenbruck, Jan Maximilian Arcak, Murat Allgower, Frank Department of Electrical Engineering and Computer Sciences University of California at Berkeley BerkeleyCA94720 United States Institute for Systems Theory and Automatic Control University of Stuttgart Stuttgart70174 Germany

We study submanifold stabilization problems from an input-output perspective, where plant and controller are relations on their sets of input-output signals. In contrast to the classical input-output approaches, we consider signals whose integral $p$-fold distance to a submanifold is finite. For feedback interconnections of relations on such signals, we develop a framework to show that the distance of the output of the plant to the desired submanifold remains bounded. Within this framework, we present a small-gain theorem, a feedback theorem for conic relations, and a feedback theorem for passive relations. We connect our findings to multiplier theory and present applications to synchronization and pattern generation. © 1963-2012 IEEE.

关键词： Feedback

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Development and test of a lane change prediction algorithm for automated driving

Development and test of a lane change prediction algorithm f...

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3. Internationale ATZ-Fachtagung Fahrerassistenzsysteme, 2017

作者： Wissing, Christian Glander, Karl-Heinz Haß, Carsten Nattermann, Till Bertram, Torsten TU Dortmund University Institute of Control Theory and Systems Engineering Dortmund44227 Germany ZF TRW Active and Passive Safety Düsseldorf40547 Germany

ISBN: (纸本)9783658190583

Due to a large number of integrated advanced driver assistance systems (ADAS) the driver nowadays can hand over the driving task to the vehicle in specific, monotone driving scenarios. Short reaction times and the constant awareness of the computer reduces the number of accidents and thus increases safety. Currently available ADAS still need to be constantly monitored by the driver in case a situation appears that cannot be handled properly by the system. © Springer Fachmedien Wiesbaden GmbH 2017.

关键词： Advanced driver assistance systems

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Explicit Input Signal Design for Stable Linear Ensemble systems

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IFAC-PapersOnLine 2017年第1期50卷 3051-3056页

作者： Tie L. Zhang W. Zeng S. Li J.-S. School of Automation Science and Electrical Engineering Beihang University (Beijing University of Aeronautics and Astronautics) Beijing 100191 China Department of Electrical and Systems Engineering Washington University St. Louis MO 63130 United States Institute for Systems Theory and Automatic Control University of Stuttgart Germany

The control of ensemble systems arising in multiple science and engineering domains has recently received increased attention due to a number of newly emerging applications. While structural controllability of linear ensemble systems has been extensively studied, the problem of constructing an actual input signal that will steer an ensemble system between arbitrarily given ensemble states remains a challenging task. In this paper, we consider a class of continuous-time stable linear ensemble systems and design explicit control laws to steer such ensembles between states of interest. The control is synthesized using the discrete-time counterpart of the original continuous-time ensemble system. Simulations are provided to demonstrate the effectiveness of the developed design method. © 2017

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Ten quick tips for deep learning in biology

arXiv

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arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

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3d simulations of ion thruster accelerator grid erosion accounting for charge exchange ion space charge 54th

3d simulations of ion thruster accelerator grid erosion acco...

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54th AIAA/SAE/ASEE Joint Propulsion Conference, 2018

作者： Chaplin, Vernon H. Polk, James E. Katz, Ira Anderson, John R. Williams, George J. Soulas, George C. Yim, John T. NASA Jet Propulsion Laboratory California Institute of Technology Electric Propulsion Group 4800 Oak Grove Dr PasadenaCA91109 United States NASA Jet Propulsion Laboratory California Institute of Technology Propulsion and Materials Engineering Section 4800 Oak Grove Dr PasadenaCA91109 United States NASA Jet Propulsion Laboratory California Institute of Technology Contamination Control Engineering Group 4800 Oak Grove Dr PasadenaCA91109 United States NASA Glenn Research Center Electric Propulsion Systems Branch 21000 Brookpark Rd ClevelandOH44135 United States

ISBN: (数字)9781624105708

ISBN: (纸本)9781624105708

Accelerator (accel) grid sputtering by ions formed through charge-exchange (CEX) reactions between beam ions and residual neutral gas is a critical life-limiting mechanism for gridded ion thrusters. The three-dimensional ion optics code CEX3D is designed to simulate this grid erosion for a single beamlet, with a particular emphasis on non-axisymmetric features such as the "pits and grooves" erosion commonly observed on the accel grid downstream face in two-grid thrusters. The treatment of CEX ions in the code was recently upgraded with a new particle-in-cell (PIC) module to account for the influence of these ions’ space charge on the electrostatic potential downstream of the grids. In order to achieve reasonable computation times while resolving the Debye length near the grids and avoiding gross violations of the Courant-Friedrichs-Lewy (CFL) condition, macroparticle velocities in the PIC calculation are limited through a rescaling procedure that preserves ion trajectories and space charge density. The code accounts for beam divergence, finite momentum transfer in CEX collisions, and radial losses of CEX ions from the beam;these effects are important for determining the CEX ion flux to the accel grid because the calculated potential downstream of the grids can become very flat. The upgraded code has been used to simulate operation of NASA’s Evolutionary Xenon Thruster (NEXT) during the 51 kHr Long Duration Test a selection of results is presented and compared with experimental data. © 2018, American institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Ion exchange

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Interval consensus for multiagent networks

arXiv

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arXiv 2018年

作者： Fontan, Angela Shi, Guodong Hu, Xiaoming Altafini, Claudio Division of Automatic Control Department of Electrical Engineering Linköping University LinköpingSE-58183 Sweden Research School of Engineering Australian National University CanberraACT0200 Australia Department of Optimization and Systems Theory Royal Institute of Technology StockholmSE-100 44 Sweden Division of Automatic Control Department of Electrical Engineering Linköping University LinköpingSE-58183 Sweden

The constrained consensus problem considered in this paper, denoted interval consensus, is characterized by the fact that each agent can impose a lower and upper bound on the achievable consensus value. Such constraints can be encoded in the consensus dynamics by saturating the values that an agent transmits to its neighboring nodes. We show in the paper that when the intersection of the intervals imposed by the agents is nonempty, the resulting constrained consensus problem must converge to a common value inside that intersection. In our algorithm, convergence happens in a fully distributed manner, and without need of sharing any information on the individual constraining intervals. When the intersection of the intervals is an empty set, the intrinsic nonlinearity of the network dynamics raises new challenges in understanding the node state evolution. Using Brouwer fixed-point theorem we prove that in that case there exists at least one equilibrium, and in fact the possible equilibria are locally stable if the constraints are satisfied or dissatisfied at the same time among all nodes. For graphs with sufficient sparsity it is further proven that there is a unique equilibrium that is globally attractive if the constraint intervals are pairwise disjoint. Copyright © 2018, The Authors. All rights reserved.

关键词： Fixed point arithmetic

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Input optics systems of the KAGRA detector during O3GK (vol 2023, 023F01, 2023)

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PROGRESS OF THEORETICAL AND EXPERIMENTAL PHYSICS 2023年第5期2023卷

作者： Akutsu, T. Ando, M. Arai, K. Arai, Y. Araki, S. Araya, A. Aritomi, N. Asada, H. Aso, Y. Bae, S. Bae, Y. Baiotti, L. Bajpai, R. Barton, M. A. Cannon, K. Cao, Z. Capocasa, E. Chan, M. Chen, C. Chen, K. Chen, Y. Chiang, C-I Chu, H. Chu, Y-K Eguchi, S. Enomoto, Y. Flaminio, R. Fujii, Y. Fujikawa, Y. Fukunaga, M. Fukushima, M. Furuhata, T. Gao, D. Ge, G-G Ha, S. Hagiwara, A. Haino, S. Han, W-B Hasegawa, K. Hattori, K. Hayakawa, H. Hayama, K. Himemoto, Y. Hiranuma, Y. Hirata, N. Hirose, E. Hong, Z. Hsieh, B-H Huang, G-Z Huang, H-Y Huang, P. Huang, Y-C Huang, Y-J Hui, D. C. Y. Ide, S. Ikenoue, B. Imam, S. Inayoshi, K. Inoue, Y. Ioka, K. Ito, K. Itoh, Y. Izumi, K. Jeon, C. Jin, H-B Jung, K. Jung, P. Kaihotsu, K. Kajita, T. Kakizaki, M. Kamiizumi, M. Kanbara, S. Kanda, N. Kang, G. Kataoka, Y. Kawaguchi, K. Kawai, N. Kawasaki, T. Kim, C. Kim, J. Kim, J. C. Kim, Ws Kim, Y-M Kimura, N. Kita, N. Kitazawa, H. Kojima, Y. Kokeyama, K. Komori, K. Kong, A. K. H. Kotake, K. Kozakai, C. Kozu, R. Kumar, R. Kume, J. Kuo, C. Kuo, H-S Kuromiya, Y. Kuroyanagi, S. Kusayanagi, K. Kwak, K. Lee, H. K. Lee, H. W. Lee, R. Leonardi, M. Li, K. L. Lin, L. C-C Lin, C-Y Lin, F-K Lin, F-L Lin, H. L. Liu, G. C. Luo, L-W Majorana, E. Marchio, M. Michimura, Y. Mio, N. Miyakawa, O. Miyamoto, A. Miyazaki, Y. Miyo, K. Miyoki, S. Mori, Y. Morisaki, S. Moriwaki, Y. Nagano, K. Nagano, S. Nakamura, K. Nakano, H. Nakano, M. Nakashima, R. Nakayama, Y. Narikawa, T. Naticchioni, L. Negishi, R. Quynh, L. Nguyen Ni, W-T Nishizawa, A. Nozaki, S. Obuchi, Y. Ogaki, W. Oh, J. J. Oh, S. H. Ohashi, M. Ohishi, N. Ohkawa, M. Ohta, H. Okutani, Y. Okutomi, K. Oohara, K. Ooi, C. Oshino, S. Otabe, S. Pan, K-C Pang, H. Parisi, A. Park, J. Arellano, F. E. Pena Pinto, I. Sago, N. Saito, S. Saito, Y. Sakai, K. Sakai, Y. Sakuno, Y. Sato, S. Sato, T. Sawada, T. Sekiguchi, T. Sekiguchi, Y. Shao, L. Shibagaki, S. Shimizu, R. Shimoda, T. Shimode, K. Shinkai, H. Shishido, T. Shoda, A. Somiya, K. Son, E. J. Sotani, H. Sugimoto, R. Suresh, J. Suzuki, T. Suzuki, T. Tagoshi, H. Takahashi, H Gravitational Wave Science Project National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Department of Physics The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Research Center for the Early Universe The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Institute for Cosmic Ray Research KAGRA Observatory The University of Tokyo 5-1-5 Kashiwa-no-Ha Kashiwa City Chiba 277-8582 Japan Accelerator Laboratory High Energy Accelerator Research Organization (KEK) 1-1 Oho Tsukuba City Ibaraki 305-0801 Japan Earthquake Research Institute The University of Tokyo 1-1-1 Yayoi Bunkyo-ku Tokyo 113-0032 Japan Department of Mathematics and Physics Graduate School of Science and Technology Hirosaki University 3 Bunkyo-cho Hirosaki Aomori 036-8561 Japan Kamioka Branch National Astronomical Observatory of Japan 238 Higashi-Mozumi Kamioka-cho Hida City Gifu 506-1205 Japan The Graduate University for Advanced Studies (SOKENDAI) 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Korea Institute of Science and Technology Information 245 Daehak-ro Yuseong-gu Daejeon 34141 Republic of Korea National Institute for Mathematical Sciences 70 Yuseong-daero 1689 Beon-gil Yuseong-gu Daejeon 34047 Republic of Korea International College Osaka University 1-1 Machikaneyama-cho Toyonaka City Osaka 560-0043 Japan School of High Energy Accelerator Science The Graduate University for Advanced Studies (SOKENDAI) 1-1 Oho Tsukuba City Ibaraki 305-0801 Japan Department of Astronomy Beijing Normal University Xinjiekouwai Street 19 Haidian District Beijing 100875 China Department of Applied Physics Fukuoka University 8-19-1 Nanakuma Jonan Fukuoka City Fukuoka 814-0180 Japan Department of Physics Tamkang University No. 151 Yingzhuan Road Danshui Dist. New Taipei City 25137 Taiwan Department of Physics

KAGRA, the underground and cryogenic gravitational-wave detector, was operated for its solo observation from February 25 to March 10, 2020, and its first joint observation with the GEO 600 detector from April 7 to April 21, 2020 (O3GK). This study presents an overview of the input optics systems of the KAGRA detector, which consist of various optical systems, such as a laser source, its intensity and frequency stabilization systems, modulators, a Faraday isolator, mode-matching telescopes, and a high-power beam dump. These optics were successfully delivered to the KAGRA interferometer and operated stably during the observations. The laser frequency noise was observed to limit the detector sensitivity above a few kilohertz, whereas the laser intensity did not significantly limit the detector sensitivity.

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Roadmap on Quantum Thermodynamics

arXiv

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arXiv 2025年

作者： Campbell, Steve D’Amico, Irene Ciampini, Mario A. Anders, Janet Ares, Natalia Artini, Simone Auffèves, Alexia Oftelie, Lindsay Bassman Bettmann, Laetitia P. Bonança, Marcus V.S. Busch, Thomas Campisi, Michele Cavalcante, Moallison F. Correa, Luis A. Cuestas, Eloisa Dag, Ceren B. Dago, Salambô Deffner, Sebastian Del Campo, Adolfo Deutschmann-Olek, Andreas Donadi, Sandro Doucet, Emery Elouard, Cyril Ensslin, Klaus Erker, Paul Fabbri, Nicole Fedele, Federico Fiusa, Guilherme Fogarty, Thomás Folk, Joshua Guarnieri, Giacomo Hegde, Abhaya S. Hernández-Gómez, Santiago Hu, Chang-Kang Iemini, Fernando Karimi, Bayan Kiesel, Nikolai Landi, Gabriel T. Lasek, Aleksander Lemziakov, Sergei Monaco, Gabriele Lo Lutz, Eric Lvov, Dmitrii Maillet, Olivier Mehboudi, Mohammad Mendonça, Taysa M. Miller, Harry J.D. Mitchell, Andrew K. Mitchison, Mark T. Mukherjee, Victor Paternostro, Mauro Pekola, Jukka Perarnau-Llobet, Martí Poschinger, Ulrich Rolandi, Alberto Rosa, Dario Sánchez, Rafael Santos, Alan C. Sarthour, Roberto S. Sela, Eran Solfanelli, Andrea Souza, Alexandre M. Splettstoesser, Janine Tan, Dian Tesser, Ludovico Van Vu, Tan Widera, Artur Halpern, Nicole Yunger Zawadzki, Krissia School of Physics University College Dublin Belfield Dublin 4 Ireland Centre for Quantum Engineering Science and Technology University College Dublin Dublin 4 Ireland School of Physics Engineering and Technology University of York York United Kingdom York Centre for Quantum Technologies University of York York United Kingdom Boltzmanngasse 5 ViennaA-1090 Austria Institute of Physics and Astronomy University of Potsdam Potsdam14476 Germany Department of Physics and Astronomy University of Exeter ExeterEX4 4QL United Kingdom Department of Engineering Science University of Oxford Parks Road OxfordOX1 3PJ United Kingdom Università degli Studi di Palermo Dipartimento di Fisica e Chimica - Emilio Segrè via Archirafi 36 PalermoI-90123 Italy MajuLab CNRS-UCA-SU-NUS-NTU International Joint Research Laboratory France Centre for Quantum Technologies National University of Singapore Singapore117543 Singapore NEST Istituto Nanoscienze-CNR and Scuola Normale Superiore Pisa56127 Italy School of Physics Trinity College Dublin College Green Dublin 2 Ireland Gleb Wataghin Physics Institute The University of Campinas Campinas São Paulo13083-859 Brazil Quantum Systems Unit OIST Graduate University Okinawa Onna904-0495 Japan Department of Physics University of Maryland Baltimore County BaltimoreMD21250 United States Quantum Science Institute University of Maryland Baltimore County BaltimoreMD21250 United States Universidad de La Laguna La Laguna38203 Spain Departamento de Física Universidad de La Laguna La Laguna38203 Spain Jülich54245 Germany Department of Physics Indiana University BloomingtonIN47405 United States Department of Physics Harvard University 17 Oxford Street CambridgeMA02138 United States National Quantum Laboratory College ParkMD20740 United States Department of Physics and Materials Science University of Luxembourg LuxembourgL-1511 Luxembourg Donostia International Physics Center San SebastianE-20018 Spain Au

The last two decades has seen quantum thermodynamics become a well established field of research in its own right. In that time, it has demonstrated a remarkably broad applicability, ranging from providing foundational advances in the understanding of how thermodynamic principles apply at the nano-scale and in the presence of quantum coherence, to providing a guiding framework for the development of efficient quantum devices. Exquisite levels of control have allowed state-of-the-art experimental platforms to explore energetics and thermodynamics at the smallest scales which has in turn helped to drive theoretical advances. This Roadmap provides an overview of the recent developments across many of the field’s sub-disciplines, assessing the key challenges and future prospects, providing a guide for its near term progress. Copyright © 2025, The Authors. All rights reserved.

关键词： Temperature

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Kinodynamic Trajectory Optimization for Real-Time Vehicle Guidance

Kinodynamic Trajectory Optimization for Real-Time Vehicle Gu...

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AmE 2017 - Automotive meets Electronics;8th GMM-Symposium

作者： Andreas Homann Martin Keller Christian Goette Karl-Heinz Glander Carsten Hass Torsten Bertram Institute of Control Theory and Systems Engineering ZF TRW Active & Passive Safety Technology

In order to develop automated driving systems an approach to generate trajectories is of essential importance. The trajectory has to satisfy two fundamental requirements. On the one hand collision free driving of the vehicle has to be ensured and on the other hand the limited driving dynamics has to be complied. The real-time capable trajectory optimization including a complex vehicle dynamics model causes high computational costs. In this contribution the trajectory optimization is based on the kinodynamics of a mass point. The road limits and obstacles are included in the optimization problem by a potential field.

关键词： trajectory optimization Vehicle driving Vehicle navigation potential field trajectory Vehicle dynamics Automobile driving vehicle material point

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