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PID2018 Benchmark Challenge: Model-based Feedforward Compensator with A Conditional Integrator

IFAC-PapersOnLine 2018年第4期51卷 888-893页

作者： Yuan, Jie Ates, Abdullah Dehghan, Sina Zhao, Yang Fei, Shumin Chen, YangQuan School of Automation Southeast University Nanjing210096 China Engineering Faculty Computer Engineering Department Inonu University Malatya44280 Turkey Mechatronics Embedded Systems and Automation Lab University of California MercedCA95340 United States School of Science and Engineering Shandong University Jinan250061 China Key Laboratory of Measurement and Control of CSE Ministry of Education School of Automation Southeast University Nanjing210096 China

Since proportional-integral-derivative (PID) controllers absolutely dominate the control engineering, numbers of different control structures and theories have been developed to enhance the efficiency of PID controllers. Thus, it is essential and inspiring to operate different PID control strategies to the PID2018 Benchmark Challenge. In this paper, a novel control strategy is designed for this refrigeration system, where a feedforward compensator and a conditional integrator are utilized to compensate the disturbances and remove the steady-state error in the benchmark problem, respectively. The simulation results given in the benchmark problem show the straightforward effectiveness of the proposed control structure compared with the existing control methods. © 2018

关键词： controllers Benchmarking Disturbance rejection Electric control equipment Proportional control systems Refrigeration Three term control systems Two term control systems Conditional integrators Feed forward compensation Model based method PID controllers Refrigeration system

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Silicone Oil Damping for Quasi-static Micro Scanners with Electrostatic Staggered Vertical Comb Drives

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IFAC-PapersOnLine 2019年第15期52卷 37-42页

作者： Richard Schroedter Jan Grahmann Klaus Janschek Fraunhofer Institute for Photonic Microsystems Active Microscanner Systems Maria-Reiche-Str.2 01109 Dresden Germany Automation and Control Institute (ACIN) Faculty of Electrical Engineering and Information Technology TU Wien Gußhausstraße 27-29 1040 Wien Austria Institute of Automation Faculty of Electrical and Computer Engineering Technische Universität Dresden 01062 Dresden Germany

Many applications like image projection, distance sensors or spectroscopy require high speed quasi-static operating scanners with the size of several millimeters for the laser beam guidance. Quasi-static micro scanners with electrostatic staggered vertical comb drives of sizes up to 5 mm achieve remarkable mechanical deflections of ±8 degree and eigenfrequencies of several hundred Hertz. Closed-loop control of the scanner position highly reduces parasitic eigenfrequency oscillation when driving an arbitrary trajectory like triangle or saw tooth, but requires position feedback and high performant drive electronics for real-time control. In this contribution we investigate silicone oil to attenuate undesired oscillations by passive mechanic damping. We present experimental results with different viscosity and determine the resulting system parameters such as damping coefficient, eigenfrequency and permittivity, supported by transient simulations. While the permittivity, refractive index and damping increase significantly, the deceasing eigenfrequency results in slower admissible trajectories. The overall performance is assessed and compared to the active damping approach using closed-loop control of the micro scanner operating in air. Finally, a potential packaging technology is presented.

关键词： laser beam guidance quasi-static micro scanner electrostatic staggered vertical comb drive silicone oil viscous passive damping permittivity close-loop trajectory control

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PID2018 Benchmark Challenge: learning feedforward control

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IFAC-PapersOnLine 2018年第4期51卷 663-668页

作者： Zhao, Yang Dehghan, Sina Ates, Abdullah Yuan, Jie Zhou, Fengyu Li, Yan Chen, YangQuan School of Control Science and Engineering Center for Robotics Shandong University JinanShandong250061 China University of California Merced MercedCA95340 United States School of Automation Southeast University Nanjing210096 China Engineering Faculty Computer Engineering Department Inonu University Malatya44280 Turkey

The design and application of learning feedforward controllers (LFFC) for the one-staged refrigeration cycle model described in the PID2018 Benchmark Challenge is presented, and its effectiveness is evaluated. The control system consists of two components: 1) a preset PID component and 2) a learning feedforward component which is a function approximator that is adapted on the basis of the feedback signal. A B-spline network based LFFC and a low-pass filter based LFFC are designed to track the desired outlet temperature of evaporator secondary flux and the superheating degree of refrigerant at evaporator outlet. Encouraging simulation results are included. Qualitative and quantitative comparison results evaluations show that, with little effort, a high-performance control system can be obtained with this approach. Our initial simple attempt of low-pass filter based LFFC and B-spline network based LFFC give J=0.4902 and J=0.6536 relative to the decentralized PID controller, respectively. Besides, the initial attempt of a combination controller of our optimized PI controller and low-pass filter LFFC gives J=0.6947 relative to the multi-variable PID controller. © 2018

关键词： controllers Benchmarking Electric control equipment Evaporators Feedforward control Interpolation Learning systems Low pass filters Proportional control systems Refrigeration Three term control systems Combination controllers Conditional integrations Decentralized PID controller Feed forward controllers High performance control systems PID 2018 Benchmark Challenge Quantitative comparison Vapour compression refrigeration systems

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An Early Stage Researcher's Primer on Systems Medicine Terminology

Network and Systems Medicine

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Network and Systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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Scheduling for Re-entrant Hybrid Flowshop Based on Wolf Pack Algorithm

Scheduling for Re-entrant Hybrid Flowshop Based on Wolf Pack...

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2018 International Conference on Advanced Materials, Intelligent Manufacturing and automation, AMIMA 2018

作者： Han, Zhonghua Tian, Xutian Ma, Xiaofu Lin, Shuo Faculty of Information and Control Engineering Shenyang Jianzhu University Liaoning110168 China Digital Factory Department Shenyang Institute of Automation CAS Shenyang110016 China Information and Control Engineering Faculty Shenyang Jianzhu University Liaoning110168 China Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24060 United States

In order to solve the scheduling problems of Re-entrant Hybrid Flowshop (RHFS), this paper investigates the mathematical programming model of RHFS, and proposes the wolf pack based algorithm (WPA) as a global optimization method. Multi-Sticking Crape Masking procedure scheduling problems in painting workshop of a bus manufacturing enterprise are of typical features of RHFS. We regard it as an application objective of the proposed algorithm, applying WPA and multiple evolutionary algorithms to solve the problems. The results show that the algorithm can solve re-entrant scheduling problems of hybrid flowshop more effectively compared with other conventional methods. © Published under licence by IOP Publishing Ltd.

关键词： Global optimization

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Preface

Lecture Notes in Networks and Systems

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Lecture Notes in Networks and Systems 2023年 764 LNNS卷 v-vi页

作者： Milrad, Marcelo Otero, Nuno Sánchez-Gómez, María Cruz Mena, Juan José Durães, Dalila Sciarrone, Filippo Alvarez-Gómez, Claudio Rodrigues, Manuel Vittorini, Pierpaolo Gennari, Rosella Di Mascio, Tania Temperini, Marco De la Prieta, Fernando Linnaeus University Växjö Sweden University of Greenwich London United Kingdom Department of Didactics Organization and Research Method University of Salamanca Salamanca Spain University of Minho Braga Portugal Mercatorum University Rome Italy Facultad de Ingeniería y Ciencias Aplicadas Universidad de los Andes Santiago Chile Department of Life Health and Environmental Sciences University of L’Aquila L’Aquila Italy Faculty of Computer Science Free University of Bozen-Bolzano Bolzano Italy Department of Information Engineering Computer Science and Mathematics University of L’Aquila L’Aquila Italy Dipartimento di Ingegneria Informatica Sapienza Universita’ di Roma Rome Italy Department of Computer Science and Automation Control University of Salamanca Salamanca Spain

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Modal regression based structured low-rank matrix recovery for multi-view learning

arXiv

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arXiv 2020年

作者： Xu, Jiamiao Wang, Fangzhao Peng, Qinmu You, Xinge Wang, Shuo Jing, Xiao-Yuan Philip Chen, C.L. School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute China State Key Laboratory of Software Engineering School of Computer Wuhan University China Department of Computer and Information Science Faculty of Science and Technology University of Macau 99999 China Dalian Maritime University Dalian116026 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100080 China

Low-rank Multi-view Subspace Learning (LMvSL) has shown great potential in cross-view classification in recent years. Despite their empirical success, existing LMvSL based methods are incapable of well handling view discrepancy and discriminancy simultaneously, which thus leads to the performance degradation when there is a large discrepancy among multi-view data. To circumvent this drawback, motivated by the block-diagonal representation learning, we propose Structured Low-rank Matrix Recovery (SLMR), a unique method of effectively removing view discrepancy and improving discriminancy through the recovery of structured low-rank matrix. Furthermore, recent low-rank modeling provides a satisfactory solution to address data contaminated by predefined assumptions of noise distribution, such as Gaussian or Laplacian distribution. However, these models are not practical since complicated noise in practice may violate those assumptions and the distribution is generally unknown in advance. To alleviate such limitation, modal regression is elegantly incorporated into the framework of SLMR (term it MR-SLMR). Different from previous LMvSL based methods, our MR-SLMR can handle any zero-mode noise variable that contains a wide range of noise, such as Gaussian noise, random noise and outliers. The alternating direction method of multipliers (ADMM) framework and half-quadratic theory are used to efficiently optimize MR-SLMR. Experimental results on four public databases demonstrate the superiority of MR-SLMR and its robustness to complicated noise. Copyright © 2020, The Authors. All rights reserved.

关键词： Gaussian noise (electronic)

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Prospects for the use of wireless reading of prosthetic control signals and methods for generating commands

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IOP Conference Series: Materials Science and engineering 2021年第1期1019卷

作者： D Malakhov V Zhmud V Avrmachuk D Gryaznov J Nosek L Dimitrov Department of Automation Novosibirsk State Technical University Novosibirsk Russia Department of Complex Information Security of Computer Systems Tomsk State University of Control Systems and Radioelectronics Tomsk Russia Department of Mechatronics and Computer Engineering (MTI) Faculty of Mechatronics Informatics and Interdisciplinary Studies Technical University of Liberec Liberec Czech Republic Vice rector Technical University of Sofia Sofia Bulgaria

The paper provides a rationale for the use of low-power wireless communication to create non-invasive bionic limb prostheses. The need for such a connection is dictated, on the one hand, by the expediency of using sensors located in various parts of the body, often very remote from each other, on the other hand, by the desire to free the disabled person's body from the wires that would envelop it like a spider web. For each of these positions, arguments are given.

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Prospects for the development of functional expansion of individual low-cost prosthetic limbs for people with disabilities with intelligent sensors

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IOP Conference Series: Materials Science and engineering 2021年第1期1019卷

作者： D Gryaznov V Zhmud V Avrmachuk D Malakhov J Nosek L Dimitrov Department of Automation Novosibirsk State Technical University Novosibirsk Russia Department of Complex Information Security of Computer Systems Tomsk State University of Control Systems and Radioelectronics Tomsk Russia Department of Mechatronics and Computer Engineering (MTI) Faculty of Mechatronics Informatics and Interdisciplinary Studies Technical University of Liberec Liberec Czech Republic Vice rector of Technical University of Sofia Sofia Bulgaria

Despite the development of medicine, as well as robotics aimed at helping people with disabilities, most disabled people with missing limbs cannot hope for a full life with the help of bionic prostheses, since these products are made strictly individually, and their control requires expensive surgeries to implant sensors. In addition, such prostheses themselves are very expensive. An alternative solution is to reduce the cost of such prostheses as much as possible and abandon the implantable sensors of signals of the human nervous system. The paper proposes a method for solving such a problem using a variety of sensors that remove signals from the skin surface without invasive surgery.

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Hierarchic neighbors embedding

arXiv

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arXiv 2019年

作者： Liu, Shenglan Yu, Yang Liu, Yang Qiao, Hong Feng, Lin Feng, Jiashi School of Computer Science and Technology Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian Liaoning116024 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore

Manifold learning now plays a very important role in machine learning and many relevant applications. Although its superior performance in dealing with nonlinear data distribution, data sparsity is always a thorny knot. There are few researches to well handle it in manifold learning. In this paper, we propose Hierarchic Neighbors Embedding (HNE), which enhance local connection by the hierarchic combination of neighbors. After further analyzing topological connection and reconstruction performance, three different versions of HNE are given. The experimental results show that our methods work well on both synthetic data and high-dimensional real-world tasks. HNE develops the outstanding advantages in dealing with general data. Furthermore, comparing with other popular manifold learning methods, the performance on sparse samples and weak-connected manifolds is better for HNE. Copyright © 2019, The Authors. All rights reserved.

关键词： Topology

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