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IEEE General Meeting Power& Energy Society

作者： Federico Milano Dept. of EE-Systems TAU Tel Aviv Israel Laboratory of Robotics and Systems in Engineering and Science (LARSyS) University of Lisbon Lisbon Portugal Department of Electrical and Computer Engineering University of Macau Macao China

This paper proposes a technique to evaluate the numerical stability and accuracy of the Partitioned-Solution Method (PSA) for the time domain integration of the Differential Algebraic Equations (DAEs) that are used for power system angle and voltage stability analyses. The partitioned approach consists in solving differential equations and algebraic constraints separately. While efficient, this technique introduces a “mismatch” between state and algebraic variables. The paper states the formal analogy of such a mismatch with a time delay of algebraic variables. Then a Small-signal Stability Analysis (SSSA) for Delay Differential Algebraic Equation (DDAE) is applied to define the numerical stability of the PSA as a function of the integration time step. Results are tested using the IEEE 14-bus system as well as a 1,479-bus model of the all-island Irish system.

关键词： Power system stability Numerical stability Stability analysis Mathematical model Numerical models Delay effects Delays

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A Novel Extended Potential Field controller for Use on Aerial Robots

A Novel Extended Potential Field Controller for Use on Aeria...

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IEEE International Conference on Automation Science and engineering

作者： Alexander C. Woods Hung M. La Quang P. Ha Advanced Robotics and Automation Laboratory Department of Computer Science and Engineering University of Nevada Reno 89557 USA School of Electrical Mechanical and Mechatronic Systems University of Technology Sydney Australia

ISBN: (纸本)9781509024100

Unmanned Aerial Vehicles (UAV), commonly known as drones, have many potential uses in real world applications. Drones require advanced planning and navigation algorithms to enable them to safely move through and interact with the world around them. This paper presents an extended potential field controller (ePFC) which enables an aerial robot, or drone, to safely track a dynamic target location while simultaneously avoiding any obstacles in its path. The ePFC outperforms a traditional potential field controller (PFC) with smoother tracking paths and shorter settling times. The proposed ePFC's stability is evaluated by Lyapunov approach, and its performance is simulated in a Matlab environment. Finally, the controller is implemented on an experimental platform in a laboratory environment which demonstrates the effectiveness of the controller.

关键词： Drones Navigation Robot sensing systems Target tracking Stability analysis

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Attitude synchronization of multiple rigid bodies in SE(3) over proximity networks

Attitude synchronization of multiple rigid bodies in SE(3) o...

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第35届中国控制会议

作者： Juan Deng Zhixin Liu Lin Wang John S.Baras Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceCAS Department of Automation Shanghai Jiao Tong Universityand the Key Laboratory of System Control and Information ProcessingMinistry of Education Department of Electrical and Computer Engineering and the Institute for Systems Research University of MarylandCollege Park

ISBN: (纸本)9781509009107

This paper investigates the attitude synchronization of a group of moving rigid bodies whose states including positions and rotation matrices evolve in the Lie group SE(3).The commonly used circular neighborhood is adopted,and the neighbor graphs are described by proximity *** order to avoid introducing discussions on the abrupt changes of neighbor graphs,a dwell time is *** distributed sampled-data control law for the angular velocity is *** the assumptions on the initial states,we provide sufficient conditions for the attitude synchronization of multiple rigid body *** with the existing results,our condition depends only on the neighborhood radius,the moving velocity and the dwell time.

关键词： rigid body SE(3) attitude synchronization sampled-data control proximity network

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Global and Asymptotically Efficient Identification of Nonlinear Rational systems via a Two-step Method

Global and Asymptotically Efficient Identification of Nonlin...

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第35届中国控制会议

作者： Biqiang Mu Er-Wei Bai Wei Xing Zheng Key Laboratory of Systems and Control Institute of Systems ScienceAcademy of Mathematics and Systems ScienceChinese Academy of Sciences Department of Electrical and Computer Engineering University of Iowa School of Computing Engineering and MathematicsWestern Sydney University

ISBN: (纸本)9781509009107

Identification of nonlinear rational systems defined as the ratio of two nonlinear functions of past inputs and outputs is considered in this paper. Although this problem has a long history, there is still lack of a globally consistent identification algorithm for such identification problem. This paper develops a globally consistent algorithm by the following steps: model transformation, bias analysis, noise variance estimation, and compensation. First, the paper studies the prediction error type estimator(nonlinear least square estimators) and the corresponding solving algorithm(Gauss-Newton algorithms). It is shown that the Gauss-Newton algorithm is locally convergent but actually asymptotically efficient by calculating the Crame′r-Rao lower bound under Gaussian observation noises. This motivates that a global and asymptotically efficient estimator can be constructed by combining the proposed globally consistent estimator with the Gauss-Newton algorithm. So, a two-step method is proposed,which consists of first executing the globally consistent algorithm and then applying the Gauss-Newton algorithm with the consistent estimate serving as the initial value. A simulation example is provided to verify the good performance of the proposed two-step method.

关键词： Nonlinear rational systems nonlinear least squares Gauss-Newton algorithms local convergence corrected least squares global convergence asymptotical efficiency

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Design of distributed observers with arbitrarily large communication delays

Design of distributed observers with arbitrarily large commu...

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Annual Conference of Industrial Electronics Society

作者： Kexin Liu Jinhu Lü Zongli Lin Academy of Mathematics and Systems Science Key Laboratory of Systems and Control Beijing China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia Charlottesville VA USA

The design of networked observers has recently received much consideration. This paper is concerned with the design of distributed observers with arbitrarily large communication time delays. A traditional centralized observer assumes access to the full output the plant. In the real world situations, the output is often measured by distributed senors connected through a communication network. We propose the design of a set of distributed observers with time delays in the communication channels. Each observer is regarded as an agent, which obtains local information of the plant and communicates with other observers through an undirected communication network, with arbitrarily large time delays. Conditions are established under which the states of all observers converge exponentially to the plant state. Numerical results are proposed to illustrate our results.

关键词： Observers Delay effects Delays Communication networks Eigenvalues and eigenfunctions Communication channels Network topology

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A Simultaneous Iterative Learning control and Dynamic Modeling Approach for A Class of Nonlinear systems

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IFAC-PapersOnLine 2015年第28期48卷 368-373页

作者： Hou, Zhongsheng Yu, Qiongxia Xu, Jian-Xin Yin, Chenkun Advanced Control Systems Laboratory Beijing Jiaotong University Beijing100044 China Department of Electrical and Computer Engineering National University of Singapore 117576 Singapore

This paper presents a novel simultaneous iterative learning control and dynamic modeling (SILCDM) approach. For a class of unknown and repeatable nonlinear discrete-time systems, a model-free iterative learning control (ILC) method is applied first. Meanwhile, by using the data generated during the repetitive operations, a novel iterative learning parameter estimation algorithm is proposed to calibrate the unknown time-varying parameter in the nonlinear system with known structure simultaneously. After the model parameter is well identified, the accurate dynamic model of the controlled plant is obtained. With this identified model, the model-free ILC method is then switched to a model-based optimal ILC method in order to get much better control performance. The theoretical analysis shows that the proposed pproach guarantees the convergence of both the system state and the parameter identification. The effectiveness of the SILCDM is further demonstrated through simulations. © 2015

关键词： Two term control systems

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Add-on module of Active Disturbance Rejection for set-point tracking of motion control systems

Add-on module of Active Disturbance Rejection for set-point ...

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Conference Record of the IEEE Industry Applications Society Annual Meeting (IAS)

作者： Wenchao Xue Rafal Madonski Zhiqiang Gao Yi Huang Krzysztof Lakomy Key laboratory of Systems and Control Chinese Academy of Sciences Beijing P. R. China Chair of Control and Systems Engineering Poznan University of Technology Poland Department of Electrical and Computer Engineering Cleveland State University Cleveland Ohio U.S.A.

ISBN: (纸本)9781479983988

Active Disturbance Rejection control (ADRC) as a standalone motion solution has been adopted by companies such as Texas Instruments and Danfoss and made available on various proprietary industrial platforms. The idea of ADRC, however, can be integrated with the existing control technologies seamlessly, as shown in this paper. It is shown a modularized ADRC design for set-point tracking of motion control such that better uncertainty rejection can be implemented without any change in the existing proportional-derivative (PD) control with linear observer. We prove that certain integration of the observer's error can serve as an estimation for the “total disturbance” in low frequency range. This enables the estimation and cancellation of the “total disturbance” to be incorporated into the existing control loop. Also, a comparison between the methods with and without such “module” is discussed. The proposed ADRC is implemented and validated with experimental results for a 1-degree of freedom robotic manipulator, where desired set-point tracking performance in position control is achieved under unknown mass variations and sudden external disturbances.

关键词： Observers Motion control Tracking Transient analysis

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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Variable selection in identification of a high dimensional nonlinear non-parametric system

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control Theory and Technology 2015年第1期13卷 1-16页

作者： Er-Wei BAI Wenxiao ZHAO Weixing ZHENG Department of Electrical and Computer Engineering University of Iowa School of Electronics Electrical Engineering and Computer Science Queen's UniversityBelfast U.K. Key Laboratory of Systems and Control Academy of Mathematics and Systems Science National Center for Mathematics and Interdisciplinary Sciences Chinese Academy of Sciences School of Computing Engineering and Mathematics University of Western Sydney

The problem of variable selection in system identification of a high dimensional nonlinear non-parametric system is described. The inherent difficulty, the curse of dimensionality, is introduced. Then its connections to various topics and research areas are briefly discussed, including order determination, pattern recognition, data mining, machine learning, statistical regression and manifold embedding. Finally, some results of variable selection in system identification in the recent literature are presented.

关键词： System identification variable selection nonlinear non-parametric system curse of dimensionality

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Synchronization of Lienard-type oscillators in uniform electrical networks

Synchronization of Lienard-type oscillators in uniform elect...

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American control Conference

作者： Mohit Sinha Florian Dorfler Brian B. Johnson Sairaj V. Dhople Department of Electrical and Computer Engineering at the University of Minnesota (UMN) Minneapolis United States of America Automatic Control Laboratory at ETH Zurich Switzerland Power Systems Engineering Center at the National Renewable Energy Laboratory (NREL) Golden CO United States of America

ISBN: (纸本)9781467386838

This paper presents a condition for global asymptotic synchronization of Lienard-type nonlinear oscillators in uniform LTI electrical networks with series R-L circuits modeling interconnections. By uniform electrical networks, we mean that the per-unit-length impedances are identical for the interconnecting lines. We derive conditions for global asymptotic synchronization for a particular feedback architecture where the derivative of the oscillator output current supplements the innate current feedback induced by simply interconnecting the oscillator to the network. Our proof leverages a coordinate transformation to a set of differential coordinates that emphasizes signal differences and the particular form of feedback permits the formulation of a quadratic Lyapunov function for this class of networks. This approach is particularly interesting since synchronization conditions are difficult to obtain by means of quadratic Lyapunov functions when only current feedback is used and for networks composed of series R-L circuits. Our synchronization condition depends on the algebraic connectivity of the underlying network, and reiterates the conventional wisdom from Lyapunov- and passivity-based arguments that strong coupling is required to ensure synchronization.

关键词： Oscillators Synchronization Integrated circuit interconnections Integrated circuit modeling Lyapunov methods Limit-cycles Mathematical model Oscillators integrated circuit interconnections integrated circuit modelling Lyapunov methods Liapunov function Electric circuits Power grids Mathematical Model current feedback

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