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Asian control Conference

作者： Chengda Lu Min Wu Xin Chen Weihua Cao Chao Gan Jinhua She School of Software and Electrical Engineering Swinburne University of Technology Melbourne Vic Australia School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan

ISBN: (纸本)9781509015740;9781509015733

In a drilling process, stick-slip vibrations are one of the main resources resulting in unstable motions or drillstring failures. In this paper, combined with a LuGre bit-rock interaction, the drillstring system is modeled as lumped parameters to capture the main features of stick-slip vibrations. Then, a novel control configuration involving five modules is developed in order to track a desired speed while suppressing the vibration, where the equivalent-input-disturbance method is skilfully employed to suppress such vibrations. In such five modules, the state observer is combined with the friction estimator to obtain the equivalent friction torques, which are corresponding to downhole frictions, on the surface control platform. In addition, the internal model and the state feedback are utilized to get the speed tracking torques so as to guarantee the desired tracking precision. Based on the dual conversion and two quadratic performances, the closed-loop stability is guaranteed by appropriate design of the controller and observer's gain matrices. Simulation is conducted to compare our method with an LQR method, showing our method's superiority and effectiveness.

关键词： Friction Vibrations Observers Torque State feedback

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Load Curve Modeling Based on Behavior Analysis of Distributed Power Customers

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IOP Conference Series: Materials Science and Engineering 2019年第1期486卷

作者： Xinyang Han Yu Zhang Yuqiao Jiao Yue Zhang Yanjing Qu Min Ding State Grid Energy Research Institute Beijing 102200 P. R. China 027-8883018 School of Automation China University of Geosciences Wuhan 430074 P. R. China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System Wuhan 430074 P. R. China State Grid Shanghai Municipal Electric Power Company Shanghai 200122 P. R. China

As renewable power generation directly affects the customers' traditional electricity behavior and then offsets the power load, this paper proposes a load curve modeling method for renewable power customers based on the behavior analysis. Firstly, customers' active behavior is represented by the quantity of active customer households. Based on the analysis of customer behaviors, a modeling method for the quantity of active customer households is proposed based on Markov Chain Monte Carlo method. Then, with the inputs as the quantity of active customer households and time of photovoltaic power generation, an inference model based on fuzzy logic is proposed to get the quantity of customer household starting electrical appliances. By combing the average usage time of electrical appliances, load characteristics are analyzed based on usage state of electrical appliance of distributed power customers. Finally, the simulation results verify the effectiveness of the proposed method.

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A weighted-resampling based transfer learning algorithm

A weighted-resampling based transfer learning algorithm

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International Joint Conference on Neural Networks (IJCNN)

作者： Xiaobo Liu Zhentao Liu Guangjun Wang Zhihua Cai Harry Zhang School of Automation China University of Geosciences Wuhan China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China School of Computer Science China University of Geosciences Wuhan China Faculty of Computer Science University of New Brunswick NB Canada

ISBN: (纸本)9781509061839

Transfer learning has attracted more and more attention, and many scholars proposed some useful strategies. Boosting is the main strategy for transfer learning. In boosting, resampling is preferred over reweighting, and it can be applied to any base learner. In this paper, we propose a weighted-resampling method for transfer learning, called TrResampling. Firstly, resampling is applied to the data with heaven weight in the source domain, and the resampled data is used with the target data as the training data to build a classifier. Then the TrAdaBoost algorithm is used to adjust the weights of source data and target data. We discuss Decision Tree, Naive Bayes, and SVM as the base learner in TrResampling, and choose the suitable for TrResampling. In order to illustrate the performance of the proposed algorithm, we compare TrResampling with the state-of-the-art algorithm TrAdaBoost and the base learner Decision Tree, experimental results on UCI data sets indicate that TrResampling is superior to TrAdaBoost and Decision Tree on many data sets.

关键词： Training data Boosting Machine learning algorithms Support vector machines Decision trees Training Electronic mail

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Distributed computation of linear matrix equations: An optimization perspective

arXiv

引用

arXiv 2017年

作者： Zeng, Xianlin Liang, Shu Hong, Yiguang Chen, Jie Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Ministry of Education Beijing100081 China

This paper investigates the distributed computation of the well-known linear matrix equation in the form of AXB = F, with the matrices A, B, X, and F of appropriate dimensions, over multiagent networks from an optimization perspective. In this paper, we consider the standard distributed matrix-information structures, where each agent of the considered multi-agent network has access to one of the sub-block matrices of A, B, and F. To be specific, we first propose different decomposition methods to reformulate the matrix equations in standard structures as distributed constrained optimization problems by introducing substitutional variables;we show that the solutions of the reformulated distributed optimization problems are equivalent to least squares solutions to original matrix equations;and we design distributed continuous-time algorithms for the constrained optimization problems, even by using augmented matrices and a derivative feedback technique. Moreover, we prove the exponential convergence of the algorithms to a least squares solution to the matrix equation for any initial condition. Copyright © 2017, The Authors. All rights reserved.

关键词： Continuous time systems

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Training and testing object detectors with virtual images

arXiv

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arXiv 2017年

作者： Tian, Yonglin Li, Xuan Wang, Kunfeng Wang, Fei-Yue Department of Automation University of Science and Technology of China Hefei230027 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Automation Beijing Institute of Technology Beijing100081 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Academy of Intelligent Industries Qingdao266000 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Research Center for Computational Experiments and Parallel Systems Technology National University of Defense Technology Changsha410073 China

In the area of computer vision, deep learning has produced a variety of state-of-the-art models that rely on massive labeled data. However, collecting and annotating images from the real world has a great demand for labor and money investments and is usually too passive to build datasets with specific characteristics, such as small area of objects and high occlusion level. Under the framework of Parallel Vision, this paper presents a purposeful way to design artificial scenes and automatically generate virtual images with precise annotations. A virtual dataset named ParallelEye is built, which can be used for several computer vision tasks. Then, by training the DPM (Deformable Parts Model) and Faster R-CNN detectors, we prove that the performance of models can be significantly improved by combining ParallelEye with publicly available real-world datasets during the training phase. In addition, we investigate the potential of testing the trained models from a specific aspect using intentionally designed virtual datasets, in order to discover the flaws of trained models. From the experimental results, we conclude that our virtual dataset is viable to train and test the object detectors. Copyright © 2017, The Authors. All rights reserved.

关键词： Object detection

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Adaptive droop control with self-adjusted virtual impedance for three-phase inverter under unbalanced conditions 50

Adaptive droop control with self-adjusted virtual impedance ...

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50th IEEE International Symposium on Circuits and systems, ISCAS 2017

作者： Lu, Zelun Li, Wenxuan Li, Zhen Chen, Xi Lu, Herbert H. C. Dong, Ning Liu, Xiangdong School of Automation Key Laboratory for Intelligent Control and Decision on Complex Systems Beijing Institute of Technology BeijingI0008I China Global Energy Interconnection Research Institute North America Santa Clara 5451 Great America Parkway CA95054 United States School of Electrical Electrical and Computer Engineering University of Western Australia CrawleyWA6009 Australia

ISBN: (纸本)9781467368520

Three-phase inverter is a very important interface in microgrid. Droop control and virtual impedance are widely used to improve the power sharing capability and stability of such system, which however becomes ineffective under unbalanced conditions and may even cause potential stability problems. This paper proposes a fully self-adjusted virtual impedance design to guarantee the decoupling of active and reactive power. The arbitrary unbalanced information is acquired through the pseudo-inverse impedance matrix modeled by full-dq technique. Based on the unbalanced impedance identified, the adaptive droop control is achieved for not only accurate power sharing but also reliable voltage support with autonomous unbalanced voltage compensation. Simulations results verify the accurate identification of unbalanced impedance and effectiveness of the proposed method on the unbalanced compensation. © 2017 IEEE.

关键词： Electric inverters

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On the nonexistence of event-based triggers that preserve Gaussian state in presence of package-drop

On the nonexistence of event-based triggers that preserve Ga...

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American control Conference

作者： Enoch Kung Junfeng Wu Dawei Shi Ling Shi Electronic and Computer Engineering Hong Kong University of Science and Technology Department of Automatic Control The Royal Institute of Technology (KTH) State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology

ISBN: (纸本)9781509045839

State estimation is a core objective in cyber-physical systems. In the state estimation problem over linear systems, the Kalman filter is the standard solution. The filter is the format on which the solutions to subsequent estimation problems are based. Among these problems are the estimation problem in the presence of packet drops and estimation problem involving event-based triggers. We study in this paper both phenomena simultaneously. In an attempt to find the Kalman-like filter, which proves the Gaussianity of the state and offers a set of update equations, our paper shows that no such filter exists. More precisely, one cannot find an event-based trigger such that under possible packet drops, the state variable remains a Gaussian variable. This conclusion can be reasonably extended to a more general setting.

关键词： State estimation Packet drops Event-based triggers State estimation Event-based Filters State variable TRIGGERS Gauss

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External consensus of networked multi-agent systems with nonlinear dynamics and random network delay

External consensus of networked multi-agent systems with non...

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Asian control Conference

作者： Qi Lei Jia-Hui Li Guo-Ping Liu Min Wu School of Information Science and Engineering University of Central South University Changsha China School of Engineering University of South Wales Pontypridd U.K. School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control Intelligent Automation for Complex Systems Wuhan China

ISBN: (纸本)9781509015740;9781509015733

In this paper, the external consensus problem of networked multi-agent systems with fixed undirected topology, random network delay and nonlinear dynamics is considered. The unknown nonlinear dynamics existing in the systems can be described as RBF-ARX model. Besides, the random network delay can be compensated by introducing the prediction strategy. The RBF-ARX model is used as prediction model to design the output predictor, which is to generate the prediction sequence of control input and agent's output. Then, the designed selector chooses the proper value from the available sequences corresponding to the value of the network delay. Based on the mentioned above, a distributed external consensus control algorithm is proposed to make the considered systems with external reference input achieve consensus. Finally, an example is given to illustrate the validity of the proposed algorithm.

关键词： Delays Nonlinear dynamical systems Algorithm design and analysis Prediction algorithms Predictive models Network topology Radial basis function networks

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Application of Hilbert-Huang decomposition to reduce noise and characterize for NMR FID signal of proton precession magnetometer

arXiv

引用

arXiv 2017年

作者： Huan, Liu Haobin, Dong Liu, Zheng Ge, Jian Bai, Bingjie Zhang, Cheng School of Automation China University of Geosciences Hubei Wuhan430074 China School of Engineering University of British Columbia Okanagan Campus KelownaBCV1X 1V7 Canada Institute of Geophysics and Geomatics China University of Geosciences Hubei Wuhan430074 China Hubei Key Laboratory of Advance Control and Intelligent Automation for Complex Systems Hubei Wuhan430074

The parameters in a nuclear magnetic resonance (NMR) free induction decay (FID) signal contain information that is useful in magnetic field measurement, magnetic resonance sounding (MRS) and other related applications. A real time sampled FID signal is well modeled as a finite mixture of exponential sequences plus noise. We propose to use the Hilbert-Huang Transform (HHT) for noise reduction and characterization, where the generalized Hilbert-Huang represents a way to decompose a signal into so-called intrinsic mode function (IMF) along with a trend, and obtain instantaneous frequency data. Moreover, the HHT for an FID signal's feature analysis is applied for the first time. First, acquiring the actual untuned FID signal by a developed prototype of proton magnetometer, and then the empirical mode decomposition (EMD) is performed to decompose the noise and original FID. Finally, the HHT is applied to the obtained IMFs to extract the Hilbert energy spectrum, to indicate the energy distribution of the signal on the frequency axis. By theory analysis and the testing of an actual FID signal, the results show that, compared with general noise reduction methods such as auto correlation and singular value decomposition (SVD), combined with the proposed method can further suppress the interfered signals effectively, and can obtain different components of FID signal, which can use to identify the magnetic anomaly, the existence of groundwater etc. This is a very important property since it can be exploited to separate the FID signal from noise and to estimate exponential sequence parameters of FID signal. Copyright © 2017, The Authors. All rights reserved.

关键词： Singular value decomposition

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Disturbance Rejection based on Equivalent-Input-Disturbance Approach for Nonlinear Time-Delay systems

Disturbance Rejection based on Equivalent-Input-Disturbance ...

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Asian control Conference

作者： Fang Gao Min Wu Jinhua She Mingxing Fang Youwu Du Fang Wang College of Physics and Electronic Information Anhui Normal University Wuhu Anhui China School of Automation China University of Geosciences Wuhan Hubei China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan Hubei China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan School of Electrical and Information Engineering Zhengzhou University of Light Industry Zhenzhou Henan China

ISBN: (纸本)9781509015740;9781509015733

A new disturbance rejection method to achieves good control performance for a nonlinear time-delay system is presented in this paper. This method effectively reject any disturbances. First, the configuration of the control system is constructed based on the equivalent-input-disturbance method. Then, a sufficient condition for global uniform ultimate boundedness and feedback controller are developed using linear matrix inequality. Finally, the effectiveness of the method is proved by a numerical simulation.

关键词： Matrix decomposition Observers Symmetric matrices Adaptive control Closed loop systems Steady-state

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