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第41届中国控制会议

作者： Jianhao Li Xiaowei Jiang Hao Chen Shaoying Wang Xianhe Zhang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Fujian Provincial Key Laboratory of Intelligent Identification and Control of Complex Dynamic System College of Science Binzhou University School of Electrical Engineering and Automation Hubei Normal University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,we focus on the tracking performance(TP) limitations of networked control systems(NCS s).The control system considered is a multi-input and multi-output(MIMO) discrete-time network control system with time delay,channel noise,packet dropouts,and logarithmic quantization communication *** modeling the channel,we assumed the channel noise to be additive white Gaussian noise(AWGN) and used a binary random process to model packet dropouts and a logarithmic quantizer to model the quantization *** explicit expression of the TP limitations of NCSs utilizing the two-degree-of-freedom(TDOF) controller is then derived using the frequency-domain approach,coprime decomposition,and Youla parameterization *** results show that the essential characteristics of the plant(locations and directions of nonminimum phase zeros and unstable poles) and network constraints(time delay,packet dropouts,quantization,and noise power spectral density) are closely related to the TP limitations.

关键词： Networked control systems tracking performance limitations packet dropouts logarithmic quantization

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Multibody simulation for tracking error reduction in solar tracking robots

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AIP Conference Proceedings 2023年第1期2872卷

作者： Aldo Hernández-Díaz Diego A. Flores-Hernández Alberto Luviano-Juárez Sergio Isai Palomino-Resendiz Itzamá López-Yáñez National Polytechnic Institute - UPIITA Applied Dynamic Systems Laboratoy Mexico City 07340 Mexico National Polytechnic Institute - UPIITA Advanced Robotics Laboratory Mexico City 07340 Mexico National Polytechnic Institute - ESIME-ZAC Department of Control and Automation Mexico City 07738 Mexico National Polytechnic Institute - CIDETEC Mexico City 07738 Mexico

Currently, the use of renewable energy sources such as solar energy, collected by photovoltaic technology, has gained ground in the energy sector. Its use has been popularised due to the fact that it is considered a clean energy, that is a type of energy is produced without burning fossil fuels which are a cause of global warming, among other environmental damages. In order to optimize the energy harvesting through photovoltaic technology, it is necessary to maintain a perpendicular incidence of the sun rays with the photovoltaic module, leading to the use of solar tracking systems that demand the application of trajectory tracking control strategies that minimize the tracking errors while ensuring the lowest energy consumption of the robotic system. In this work, a comprehensive multibody simulation was carried out to obtain a concrete appreciation of the behavior of the tracking system prior to an experimental implementation. This type of simulations allows us to simulate complete systems formed by solid links and rigid joints that were previously designed, and with a calculated analytical model (Kinematic and dynamic). Furthermore, the obtained simulator allows us to apply a variety of control strategies that are represented by means of 3D simulations which are very similar to the actual experimental results, so that it would be possible to estimate the ªtracking error results° without the necessity of a physical implementation. From the kinematic and dynamic models of a solar tracker of two degrees of freedom, released in an azimuthal-elevation configuration, a PID controller and a passivity-based controller were implemented in Matlab-Simscape®, obtaining simulations of a solar trajectory tracking minimizing the error between the reference and the solar tracker.

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Recovering compressed images for automatic crack segmentation using generative models

arXiv

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arXiv 2020年

作者： Huang, Yong Zhang, Haoyu Li, Hui Wu, Stephen Key Laboratory of Intelligent Disaster Prevention and Mitigation for Civil Infrastructures Ministry of Industry and Information Technology Harbin Heilongjiang150090 China Ministry of Education Key Laboratory of Structural Dynamic Behavior and Control School of Civil Engineering Harbin Institute of Technology Harbin China Institute of Statistical Mathematics Research Organization of Information and Systems 10-3 Midori-cho Tachikawa Tokyo190-8562 Japan Graduate University for Advanced Studies SOKENDAI 10-3 Midori-cho Tachikawa Tokyo190-8562 Japan

In a structural health monitoring (SHM) system that uses digital cameras to monitor cracks of structural surfaces, techniques for reliable and effective data compression are essential to ensure a stable and energy-efficient crack images transmission in wireless devices, e.g., drones and robots with high definition cameras installed. Compressive sensing (CS) is a signal processing technique that allows accurate recovery of a signal from a sampling rate much smaller than the limitation of the Nyquist sampling theorem. The conventional CS method is based on the principle that, through a regularized optimization, the sparsity property of the original signals in some domain can be exploited to get the exact reconstruction with a high probability. However, the strong assumption of the signals being highly sparse in an invertible space is relatively hard for real crack images. In this paper, we present a new approach of CS that replaces the sparsity regularization with a generative model that is able to effectively capture a low dimension representation of targeted images. We develop a recovery framework for automatic crack segmentation of compressed crack images based on this new CS method and demonstrate the remarkable performance of the method taking advantage of the strong capability of generative models to capture the necessary features required in the crack segmentation task even the backgrounds of the generated images are not well reconstructed. The superior performance of our recovery framework is illustrated by comparing with three existing CS algorithms. Furthermore, we show that our framework is extensible to other common problems in automatic crack segmentation, such as defect recovery from motion blurring and *** Codes 62P30, 97M50 Copyright © 2020, The Authors. All rights reserved.

关键词： Compressed sensing

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Stability analysis of human–adaptive controller interactions

Stability analysis of human–adaptive controller interaction...

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AIAA Guidance, Navigation, and control Conference, 2017

作者： Yucelen, Tansel Yildiz, Yildiray Sipahi, Rifat Youse, Ehsan Nguyen, Nhan Department of Mechanical Engineering University of South Florida TampaFL United States Mechanical Engineering Department Systems Laboratory at Bilkent University Ankara Turkey Mechanical and Industrial Engineering Department Complex Dynamic Systems and Control Laboratory Northeastern University BostonMA United States Advanced Control and Evolvable Systems Group Intelligent Systems Division Ames Research Center of the National Aeronautics and Space Administration Moffett FieldCA United States

ISBN: (数字)9781624104503

ISBN: (纸本)9781624104503

In this paper, stability of human in the loop model reference adaptive control architectures is analyzed. For a general class of linear human models with time-delay, a fundamental stability limit of these architectures is established, which depends on the parameters of this human model as well as the reference model parameters of the adaptive controller. It is shown that when the given set of human model and reference model parameters satisfy this stability limit, the closed-loop system trajectories are guaranteed to be stable. © 2017, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Closed loop systems

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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A study of fuzzy hysteresis modeling and control of a shape emory wire actuator

A study of fuzzy hysteresis modeling and control of a shape ...

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21st Biennial Conference on Mechanical Vibration and Noise, presented at - 2007 ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE2007

作者： Mehrabian, Ali Reza Hashemi, S. Vahid Williams, Eric Elahinia, Mohammad Advanced Dynamic and Control Systems Lab. School of Mechanical Engineering University of Tehran P.O.Box: 14875-347 Tehran Iran Dynamic and Smart Systems Laboratory University of Toledo Nitschke Hall 4045 OH 43606 United States

ISBN: (纸本)0791848027

This paper describes the development of fuzzy systems for modeling the hysteresis behavior of shape memory alloy (SMA) actuators. Due to their simplicity and ease of actuation, SMA actuators are very attractive for applications such as miniature robots for micro manufacturing. However, SMAs have not been widely used for motion control applications due to their nonlinear behavior and control difficulties. One approach to design a position controller for SMA systems is to employ an inverse-model of the system in the control loop to compensate the hysteresis properties of the material. Fuzzy systems, due to their nonlinear learning and adaptation abilities, are good candidates for obtaining inverse-models. In this paper two fuzzy modeling approaches are employed and compared to develop a model for a SMA wire actuator. A set of experiments are conducted to generate the training data. The test stand includes a Nickel-Titanium (TiNi) SMA wire, a position sensor, a bias spring and a current amplifier. By comparing the performance of the two employed fuzzy modeling techniques, it is revealed that the approach based on fuzzy Gustafson-Kessel (GK) clustering shows a better performance in the modeling of the hysteresis in the SMA wire. Thus, GK clustering algorithm is employed to develop the inverse-model for the SMA. The reported results demonstrate the ability of the employed fuzzy algorithm for modeling the hysteresis in the system, and the merits of the introduced inverse-model in the control of the position of the SMA. Copyright © 2007 by ASME.

关键词： Shape memory effect

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An optimal approach to active vibration control of smart structures

An optimal approach to active vibration control of smart str...

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18th IASTED International Conference on Modelling and Simulation, MS 2007

作者： Mehrabian, Ali Reza Shahi, Hossein Yousefi-Koma, Aghil Advanced Dynamic and Control Systems Laboratory School of Mechanical Engineering University of Tehran P.O.Box: 14875-347 Tehran Iran

ISBN: (纸本)9780889866638

A solution to the problem of identification and control of smart structures is presented in this paper. Smart structures with build-in sensors and actuators can actively and adaptively change their physical geometry and properties. As a particular example, a representative dynamic model of a typical fighter vertical tail, identified as the smart fin, is considered. Piezoelectric patches, which are mounted on the vertical tail, are employed as actuator. The Frequency Response Function (FRF) of the smart fin is obtained from experiment. The corresponding transfer function is then derived using system identification (ID) techniques, using MATLAB system identification toolbox, which is verified with the experimental data. The model obtained using system ID is then used to tune an optimal PID controller to reduce the vibration of the smart structure. Multiple objectives are considered for designing the active control system based on the frequency response of the open-loop and the closed-loop system. The multi-objective optimization problems are solved using Genetic Algorithms (GAs). The results are then studied and the best PID is chosen as the candidate for active vibration control for the smart fin. The reported results indicate that the designed optimal controller has better performance than an experimentally tuned PID controller.

关键词： Genetic algorithms

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