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Relaxed conditions for uniform complete observability and controllability of LTV systems with bounded realizations

IFAC-PapersOnLine 2017年第1期50卷 3598-3605页

作者： Batista P. Petit N. Silvestre C. Oliveira P. Institute for Systems and Robotics Laboratory for Robotics and Engineering Systems Instituto Superior Técnico Universidade de Lisboa Portugal MINES ParisTech PSL Research University CAS – Centre automatique et systémes 60 bd St Michel Paris 75272 France Department of Electrical and Computer Engineering Faculty of Science and Technology of the University of Macau Macau Department of Mechanical Engineering Instituto Superior Técnico Universidade de Lisboa Portugal

Uniform complete observability and controllability are key concepts for the design of state observers and controllers for linear time-varying systems. While the definitions of these properties are simple, it is more often that not very difficult to show they hold for a particular system. This paper presents alternative simpler sufficient conditions. Examples are shown that illustrate the improvement over previous results, the fact that the new conditions are not necessary, and a practical application. © 2017

关键词： linear time-varying systems uniform complete controllability uniform complete observability

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Machine discovery of partial differential equations from spatiotemporal data

arXiv

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arXiv 2019年

作者： Yuan, Ye Li, Junlin Li, Liang Jiang, Frank Tang, Xiuchuan Zhang, Fumin Liu, Sheng Goncalves, Jorge Voss, Henning U. Li, Xiuting Kurths, Jürgen Ding, Han School of Artificial Intelligence and Automation State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology Wuhan430074 China School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan430074 China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm11428 Sweden School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta30332 United States School of Power and Mechanical Engineering Wuhan University Wuhan430074 China Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom Luxembourg Centre for Systems Biomedicine University of Luxembourg BelvauxL-4367 Luxembourg Citigroup Biomedical Imaging Center Weill Cornell Medical College New York10021 United States Research Domain IV - Transdisciplinary Concepts & Methods Potsdam Institute for Climate Impact Research Potsdam14473 Germany School of Mechanical Science and Engineering State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology Wuhan430074 China

The study presents a general framework for discovering underlying Partial Differential Equations (PDEs) using measured spatiotemporal data. The method, called Sparse Spatiotemporal System Discovery (S3d), decides which physical terms are necessary and which can be removed (because they are physically negligible in the sense that they do not affect the dynamics too much) from a pool of candidate functions. The method is built on the recent development of Sparse Bayesian Learning;which enforces the sparsity in the to-be-identified PDEs, and therefore can balance the model complexity and fitting error with theoretical guarantees. Without leveraging prior knowledge or assumptions in the discovery process, we use an automated approach to discover ten types of PDEs, including the famous Navier-Stokes and sine-Gordon equations, from simulation data alone. Moreover, we demonstrate our data-driven discovery process with the Complex Ginzburg-Landau Equation (CGLE) using data measured from a traveling-wave convection experiment. Our machine discovery approach presents solutions that has the potential to inspire, support and assist physicists for the establishment of physical laws from measured spatiotemporal data, especially in notorious fields that are often too complex to allow a straightforward establishment of physical law, such as biophysics, fluid dynamics, neuroscience or nonlinear optics. Copyright © 2019, The Authors. All rights reserved.

关键词： Nonlinear optics

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Cellular Computational Extreme Learning Machine Network Based Frequency Predictions in a Power System

Cellular Computational Extreme Learning Machine Network Base...

引用

International Joint Conference on Neural Networks

作者： Iroshani Jayawardene Ganesh K. Venayagamoorthy Real-Time Power and Intelligent Systems Laboratory Holcombe Department of Electrical and Computer Engineering Clemson University

ISBN: (纸本)9781509061839

Frequency is one of the most important characteristics in power system monitoring, control and protection. Frequency variations can be observed with significant changes in operating conditions. High penetration levels of renewable energy pose variability and uncertainty challenges for grid operation. It is essential to have innovative methodologies to take necessary actions to overcome these challenges. Power system frequency prediction provides an insight to better system control and protection. In this paper, a cellular computational extreme learning machine network (CCELMN) based frequency prediction approach is presented. Results are compared with those obtained with independent ELM models and persistence model and shown to outperform.

关键词： Cellular computational extreme machine learning network Frequency Prediction Power system Renewable energy

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On active decoupling control for horizontal-lateral direction of hypersonic flight based on HIFOO 21

On active decoupling control for horizontal-lateral directio...

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21st AIAA International Space Planes and Hypersonics Technologies Conference, Hypersonics 2017

作者： Ma, Shunjian Zhu, Kai Sun, Mingwei Wang, Zenghui Chen, Zengqiang Dept. of Automation and Intelligent Science Nankai University College of Computer and Control Engineering Tianjin300350 China Beijing Aerospace Technology Institute Tianjin Key Laboratory of Intelligent Robotics Tianjin300350 China Beijing Aerospace Technology Institute Beijing100074 China Department of Electrical and Mining Engineering University of South Africa FL1710 South Africa

ISBN: (数字)9781624104633

ISBN: (纸本)9781624104633

Nowadays, hypersonic flight vehicles are widely studied. Since their high nonlinearity, strong coupling and uncertain factors, the conventional three-channel independent control design method is often unable to work well. In this paper, an active disturbance rejection controller (ADRC) is utilized to cancel internal uncertainties and exogenous disturbances for longitudinal dynamics. Then, the MIMO robust controller is obtained by choosing proper sensitivity functions and weighting functions to reduce horizontal-lateral direction coupling. The conventional H robust controllor is high-order, which is difficult for practitioners to implement. With frequency transformation, both ADRC for the longitudinal dynamics and MIMO robust controller for the horizontal-lateral dynamics are reformed into the static output feedback structure to reduce the high order of controller. Finally, all parameters in the controllers can be optimized by HIFOO (H-Infinity Fixed Order Optimization). The Winged-Cone, a 6-freedom NASA hypersonic vehicle model, is used as a benchmark, and Monte Carlo simulations demonstrate the effectiveness of the proposed method. © 2017, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Controllers

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A Review of Neural Network Based Machine Learning Approaches for Rotor Angle Stability Control

arXiv

引用

arXiv 2017年

作者： Yousefian, Reza Kamalasadan, S. Department of Electrical and Computer Engineering Power Energy and Intelligent Systems Laboratory University of North Carolina-Charlotte.

This paper reviews the current status and challenges of Neural Networks (NNs) based machine learning approaches for modern power grid stability control including their design and implementation methodologies. NNs are widely accepted as Artificial Intelligence (AI) approaches offering an alternative way to control complex and ill-defined problems. In this paper various application of NNs for power system rotor angle stabilization and control problem is discussed. The main focus of this paper is on the use of Reinforcement Learning (RL) and Supervised Learning (SL) algorithms in power system wide-area control (WAC). Generally, these algorithms due to their capability in modeling nonlinearities and uncertainties are used for transient classification, neuro-control, wide-area monitoring and control, renewable energy management and control, and so on. The works of researchers in the field of conventional and renewable energy systems are reported and categorized. Paper concludes by presenting, comparing and evaluating various learning techniques and infrastructure configurations based on efficiency. Copyright © 2017, The Authors. All rights reserved.

关键词： Supervised learning

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Deep EndoVO: A recurrent convolutional neural network (RCNN) based visual odometry approach for endoscopic capsule robots

arXiv

引用

arXiv 2017年

作者： Turan, Mehmet Almalioglu, Yasin Araujo, Helder Konukoglu, Ender Sitti, Metin Physical Intelligence Department Max Planck Institute for Intelligent Systems Germany Computer Engineering Department Bogazici Univesity Turkey Institute for Systems and Robotics Universidade de Coimbra Portugal Computer Vision Laboratory Department of Information Technology and Electrical Engineering ETH Zurich Switzerland

Ingestible wireless capsule endoscopy is an emerging minimally invasive diagnostic technology for inspection of the GI tract and diagnosis of a wide range of diseases and pathologies. Medical device companies and many research groups have recently made substantial progresses in converting passive capsule endoscopes to active capsule robots, enabling more accurate, precise, and intuitive detection of the location and size of the diseased areas. Since a reliable real time pose estimation functionality is crucial for actively controlled endoscopic capsule robots, in this study, we propose a monocular visual odometry (VO) method for endoscopic capsule robot operations. Our method lies on the application of the deep Recurrent Convolutional Neural Networks (RCNNs) for the visual odometry task, where Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are used for the feature extraction and inference of dynamics across the frames, respectively. Detailed analyses and evaluations made on a real pig stomach dataset proves that our system achieves high translational and rotational accuracies for different types of endoscopic capsule robot trajectories. Copyright © 2017, The Authors. All rights reserved.

关键词： Vision

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Influencing behavior of electricity consumers to enhance participation in demand response

Influencing behavior of electricity consumers to enhance par...

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2017 North American Power Symposium, NAPS 2017

作者： Ballenger, Anna P. Herath, Pramod Caceres, Maria Navarro Venayagamoorthy, Ganesh K. Corchado, Juan Manuel Real-Time Power and Intelligent Systems Laboratory Holcombe Department of Electrical and Computer Engineering Clemson University ClemsonSC29634 United States School of Engineering University of Kwazulu-Natal Durban South Africa University of Salamanca c/Espejo s/n Salamanca37007 Spain

ISBN: (纸本)9781538626993

Demand response (DR) provides an opportunity for consumers to change their electricity usage during peak periods by applying time-based rates or other forms of financial incentives. Currently the majority of DR resources are based on commercial loads in which substantial amounts of DR resources for residential purposes are wasted. Despite the multiple advantages of demand response to such residential customers, they are often reluctant to participate in such programs because of privacy concerns or because they are unaware of time-based rates. In this study, a customer dashboard was developed to enhance and persuade customers to participate in demand response programs. The online dashboard was used to increase awareness of all customer types, which is essential for ensuring a significant increase of DR participants. Incorporating flexibility, ease-of-use, and rewards programs will attract customers to use the dashboard thus expanding customer awareness to consciously or subconsciously change energy consumption behaviors. Making such cultural changes in customer behaviors leads to a long term solution for participation in demand response programs. With enhanced DR participation a sustainable power system operation and environment can be realized. © 2017 IEEE.

关键词： Sales

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Molybdenum Disulfide: Scalable Fabrication of Molybdenum Disulfide Nanostructures and their Assembly (Adv. Mater. 43/2020)

引用

Advanced Materials 2020年第43期32卷

作者： Yun Huang Kang Yu Huaizhi Li Kai Xu Zexi Liang Debora Walker Paulo Ferreira Peer Fischer Donglei (Emma) Fan Materials Science and Engineering Program Texas Materials Institute The University of Texas at Austin Austin TX 78712 USA The Department of Advanced Electron Microscopy Imaging and Spectroscopy International Iberian Nanotechnology Laboratory Braga 4715‐330 Portugal Electrical and Computer Engineering University of Illinois at Urbana‐Champaign Urbana IL 61801 USA School of Engineering and Applied Sciences Harvard University Cambridge MA 02138 USA Mechanical Engineering Department and IDMEC Instituto Superior Técnico University of Lisbon Av. Rovisco Pais Lisboa 1049‐001 Portugal Max‐Planck‐Institute for Intelligent Systems Heisenbergstr. 3 Stuttgart 70569 Germany Institute of Physical Chemistry University of Stuttgart Pfaffenwaldring 55 Stuttgart 70569 Germany Walker Department of Mechanical Engineering The University of Texas at Austin Austin TX 78712 USA

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Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

引用

Nature methods 2024年第10期21卷 1959页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

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Outer-Linearization-Based Optimization Algorithm for Decentralized Control Design in Flexure-Linked H-Gantry

Outer-Linearization-Based Optimization Algorithm for Decentr...

引用

IEEE/ASME International Conference on Advanced intelligent Mechatronics

作者： Jun Ma Si-Lu Chen Wenyu Liang Xiaocong Li Chek Sing Teo Arthur Tay Abdullah Al Mamun Kok Kiong Tan Department of Electrical and Computer Engineering National University of Singapore Singapore Zhejiang Provincial Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Chinese Academy of Sciences Ningbo Zhejiang China Department of Electrical and Computer Engineering National Universit of Singapore Singapore Mechatronics Group Singapore Institute of Manufacturing Technology Singapore

The dual-drive H-gantry (DHG) is widely used for high-speed high-precision Cartesian motion control applications. Compared with the rigid-linked gantry design, the flexure-linked design is able to prevent the damage of the cross-arm because a small degree of rotation angle is allowed. Nevertheless, the flexure-linked design possibly induces the resonance of the gantry because of the chattering of control signals. To maintain the precision tracking of two carriages and minimize the chattering of control efforts, we aim to seek the most suitable flexure joints among the available ones and optimize the decentralized feedback controller parameters under parametric uncertainties. The mechatronic design problem is formulated as an H_2 guaranteed cost control problem. All the stabilizing feedback controller gains are parameterized over a convex set, from here, numerical procedures are developed to obtain the global optimum by means of an outer-linearization-based optimization algorithm. Experiments are carried out and the results successfully validate the optimality and the robustness of the proposed design approach.

关键词： Decentralized control Optimization Uncertainty Feedforward systems Adaptive control Robustness Actuators

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