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Instant MPC for linear systems and dissipativity-based stability analysis

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arXiv 2019年

作者： Yoshida, Keisuke Inoue, Masaki Hatanaka, Takeshi Department of Applied Physics and Physico-Informatics Keio University 3-14-1 Hiyoshi Kohoku-ku Yokohama Kanagawa223-8521 Japan Department of Systems and Control Engineering Osaka University 2-1 Yamada-oka Suita Osaka565-0871 Japan

This letter is devoted to the concept of instant model predictive control (iMPC) for linear systems. An optimization problem is formulated to express the finite-Time constrained optimal regulation control, like conventional MPC. Then, iMPC determines the control action based on the optimization process rather than the optimizer, unlike MPC. The iMPC concept is realized by a continuous-Time dynamic algorithm of solving the optimization;the primal-dual gradient algorithm is directly implemented as a dynamic controller. On the basis of the dissipativity evaluation of the algorithm, the stability of the control system is analyzed. Finally, a numerical experiment is performed in order to demonstrate that iMPC emulates MPC and to show its less computational burden. Copyright © 2019, The Authors. All rights reserved.

关键词： Model predictive control

Experimental analysis of acoustic emission signals emitted by surface partial discharges in various dielectric liquids

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Experimental analysis of acoustic emission signals emitted b...

IEEE International Conference on Dielectrics (ICD)

作者： Daria Wotzka Michał Kozioł Łukasz Nagi Ireneusz Urbaniec Department of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland

Paper considers measurements and analysis of acoustic emission signals emitted by surface partial discharges generated in various dielectric liquids under laboratory conditions. Natural and synthetic ester and mineral oil were investigated. The influence of supply voltage on the no of acoustic emission signals and on acoustic emission amplitude, within a measuring second and in supply voltage period (20 ms) were determined and discussed.

关键词： Voltage measurement Partial discharges Oils Dielectric measurement Minerals Acoustic emission Dielectric liquids

Ten quick tips for deep learning in biology

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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

Solving Lur'e equations through zeroing neural networks

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Information Sciences 2025年 718卷

作者： Yu He Xiaojuan Wang Yafei Tie Huanqi Yang Theodore E. Simos Spyridon D. Mourtas Vasilios N. Katsikis School of Computer Science and Artificial Intelligence Huanghuai University Zhumadian 463000 China Henan International Joint Laboratory of Behavior Optimization Control for Smart Robots Henan 463000 China Henan Key Laboratory of Smart Lighting Zhumadian 46300 China Hangzhou Dianzi University School of Mechanical Engineering Er Hao Da Jie 1158 Xiasha Hangzhou 310018 China Center for Applied Mathematics and Bioinformatics Gulf University for Science and Technology West Mishref 32093 Kuwait Section of Mathematics Dept. of Civil Engineering Democritus Univ. of Thrace Xanthi 67100 Greece Department of Economics Division of Mathematics-Informatics and Statistics-Econometrics National and Kapodistrian University of Athens Sofokleous 1 Street 10559 Athens Greece Laboratory “Hybrid Methods of Modelling and Optimization in Complex Systems” Siberian Federal University Prosp. Svobodny 79 660041 Krasnoyarsk Russia

Solving Lur'e equations plays a critical role in addressing linear-quadratic optimal control (LQOC) problems, especially in cases where the control cost matrices are singular. This paper introduces, for the first time, two novel zeroing neural network (ZNN) models—ZNNLE and ZNNLE-LQOC—specifically designed to solve the Lur'e equation system and the LQOC problem, respectively. The proposed models extend the applicability of the ZNN methodology to these challenging scenarios by offering robust and efficient solutions to time-varying matrix equations. Theoretical analyses confirm the validity of both models, while numerical simulations and practical applications demonstrate their effectiveness. Moreover, a comparative study with an enhanced alternating-direction implicit (ADI) method highlights the superior performance of the ZNNLE-LQOC model in solving LQOC problems.

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Regulation of nonholonomic systems: A smooth, time-invariant approach ⁎

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IFAC-PapersOnLine 2019年第16期52卷 150-155页

作者： Bowen Yi Romeo Ortega Weidong Zhang Science and Technology on Underwater Vehicle Laboratory Harbin Engineering University Harbin 150001 China Department of Automation Shanghai Jiao Tong University Shanghai 200240 China Laboratoire des Signaux et Systèmes CNRS-CentraleSupélec Gif-sur-Yvette 91192 France Department of Control Systems and Informatics ITMO University Saint Petersburg 197101 Russia

In this paper we propose an energy pumping-and-damping technique to regulate nonholonomic systems described by kinematic models. The controller design follows the widely popular interconnection and damping assignment passivity-based methodology, with the free matrices partially structured. Two asymptotic regulation objectives are considered: drive the state to zero or drive the systems total energy to a desired constant value. In both cases, the control laws are smooth, time-invariant, state-feedbacks. For the nonholonomic integrator we give an almost global solution for both problems, with the objectives ensured for all system initial conditions starting outside a set that has zero Lebesgue measure and is nowhere dense. For the general case of higher-order nonholonomic systems in chained form, a local stability result is given. Simulation results comparing the performance of the proposed controller with other existing designs are also provided.

关键词： Nonholonomic Systems Passivity-Based control Interconnection Damping Assignment Energy Pumping Damping

A comparative study above two self-tuning controllers with aplication to the control of synchronous generator excitation system

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arXiv 2018年

作者： Filip, Ioan Curiac, Daniel Prostean, Octavian Szeidert, Iosif "Politehnica" University of Timisoara Faculty of Control Engineering and Computer Sciences Department of Automation and Industrial Informatics

This paper presents two self-tuning control structures synthesized through the minimization of two criterion functions. It is described the computation methodology of the control laws, both being particularized for the case of the synchronous generator's excitation control. The parameters estimator is considered the recursive least square error (RLSE) algorithm. In order to validate the considered control structures, two comparative study cases by computer simulation are presented. Copyright © 2018, The Authors. All rights reserved.

关键词： Adaptive control systems

Correction: Candidate variants in DNA replication and repair genes in early-onset renal cell carcinoma patients referred for germline testing

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BMC genomics 2023年第1期24卷 388页

作者： Elena V Demidova Ilya G Serebriiskii Ramilia Vlasenkova Simon Kelow Mark D Andrake Tiffiney R Hartman Tatiana Kent James Virtucio Gail L Rosen Richard T Pomerantz Roland L Dunbrack Erica A Golemis Michael J Hall David Y T Chen Mary B Daly Sanjeevani Arora Cancer Prevention and Control Program Fox Chase Cancer Center 333 Cottman Avenue Philadelphia PA 19111 USA. Kazan Federal University Kazan 420008 Russia. Program in Cancer Signaling and Microenvironment Fox Chase Cancer Center Philadelphia PA 19111 USA. Department of Biochemistry and Molecular Biophysics University of Pennsylvania Philadelphia PA 19104 USA. Arcadia University Glenside PA USA. Department of Biochemistry & Molecular Biology Sidney Kimmel Cancer Center Thomas Jeferson University Philadelphia PA 19107 USA. Ecological and Evolutionary Signal-Processing and Informatics Laboratory Department of Electrical and Computer Engineering College of Engineering Drexel University Philadelphia PA 19104 USA. Department of Cancer and Cellular Biology Lewis Katz School of Medicine Philadelphia PA 19140 USA. Department of Clinical Genetics Fox Chase Cancer Center 333 Cottman Avenue Philadelphia PA 19111 USA. Department of Surgical Oncology Fox Chase Cancer Center Philadelphia PA 19111 USA. Cancer Prevention and Control Program Fox Chase Cancer Center 333 Cottman Avenue Philadelphia PA 19111 USA. Mary.Daly@fccc.edu. Department of Clinical Genetics Fox Chase Cancer Center 333 Cottman Avenue Philadelphia PA 19111 USA. Mary.Daly@fccc.edu. Cancer Prevention and Control Program Fox Chase Cancer Center 333 Cottman Avenue Philadelphia PA 19111 USA. Sanjeevani.Arora@fccc.edu. Department of Radiation Oncology Fox Chase Cancer Center Philadelphia PA 19111 USA. Sanjeevani.Arora@fccc.edu.

Spectral design of robust delayed resonator by double-root assignment

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IFAC-PapersOnLine 2018年第14期51卷 72-77页

作者： Kue, Matj Vyhlídal, Tomá Michiels, Wim Boussaada, Islam Department of Instrumentation and Control Engineering Faculty of Mechanical Engineering and Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Technická 4 Prague 6 16000 Czech Republic Department of Computer Science KU Leuven Celestijnenlaan 200A HeverleeB-3001 Belgium L2S-CNRS Universite Paris Saclay CentraleSupelec-Universite Paris Sud 3 rue Joliot-Curie Gif-sur-Yvette cedex91192 France

A robust alternative of the delayed resonator is proposed using a spectral approach. By double root assignment at the excitation frequency which is employed to widening the stop-band in the active absorber frequency response, the performance sensitivity to the mismatch between the design and true excitation frequency is considerably decreased. Additionally the overall scheme is supplemented by a control loop which improves the stability margin. The design is validated by simulations and the results are compared with the classical delayed resonator. © 2018

关键词： Resonators Frequency response Robust control Absorber frequency Acceleration feedback Active vibration controls Double root assignment Excitation frequency Performance sensitivity Spectral methods Stability margins

Vision-based Navigation Using Deep Reinforcement Learning

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arXiv 2019年

作者： Kulhánek, Jonáš Derner, Erik de Bruin, Tim Babuška, Robert Cognitive Robotics Faculty of 3mE Delft University of Technology Delft2628 CD Netherlands Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague16636 Czech Republic Department of Control Engineering Faculty of Electrical Engineering Czech Technical University in Prague Prague16627 Czech Republic

— Deep reinforcement learning (RL) has been successfully applied to a variety of game-like environments. However, the application of deep RL to visual navigation with realistic environments is a challenging task. We propose a novel learning architecture capable of navigating an agent, e.g. a mobile robot, to a target given by an image. To achieve this, we have extended the batched A2C algorithm with auxiliary tasks designed to improve visual navigation performance. We propose three additional auxiliary tasks: predicting the segmentation of the observation image and of the target image and predicting the depth-map. These tasks enable the use of supervised learning to pre-train a large part of the network and to reduce the number of training steps substantially. The training performance has been further improved by increasing the environment complexity gradually over time. An efficient neural network structure is proposed, which is capable of learning for multiple targets in multiple environments. Our method navigates in continuous state spaces and on the AI2-THOR environment simulator outperforms state-of-the-art goal-oriented visual navigation methods from the literature. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning