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Flocking of Second-Order Multiagent systems with Connectivity Preservation Based on Algebraic Connectivity Estimation

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IEEE Transactions on Cybernetics 2017年第4期47卷 1067-1077页

作者： Fang, Hao Wei, Yue Chen, Jie Xin, Bin School of Automation Beijing Institute of Technology Beijing100081 China Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing100081 China

The problem of flocking of second-order multiagent systems with connectivity preservation is investigated in this paper. First, for estimating the algebraic connectivity as well as the corresponding eigenvector, a new decentralized inverse power iteration scheme is formulated. Then, based on the estimation of the algebraic connectivity, a set of distributed gradient-based flocking control protocols is built with a new class of generalized hybrid potential fields which could guarantee collision avoidance, desired distance stabilization, and the connectivity of the underlying communication network simultaneously. What is important is that the proposed control scheme allows the existing edges to be broken without violation of connectivity constraints, and thus yields more flexibility of motions and reduces the communication cost for the multiagent system. In the end, nontrivial comparative simulations and experimental results are performed to demonstrate the effectiveness of the theoretical results and highlight the advantages of the proposed estimation scheme and control algorithm. © 2017 IEEE.

关键词： Multi agent systems

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Fault Tolerant control for a Class of Nonlinear System based on Active Disturbance Rejection control and RBF Neural Networks 36

Fault Tolerant Control for a Class of Nonlinear System based...

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第36届中国控制会议

作者： Lushan Zhou Liling Ma Junzheng Wang State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology

ISBN: (纸本)9781538629185

In this paper,a fault tolerant control method based on active disturbance rejection control(ADRC) and radial basis function neural network(RBFNN) is proposed for a class of multi-input-multi-output nonlinear system with actuator faults,components faults and sensor *** proposed method does not rely on the plant *** regarding the faults and plant uncertainties as the disturbance,through the observation of extended state observer and the compensation of feedback control signal,this method achieves the fault tolerance control of the plant with component fault and actuator *** sensor faults,in this work,radial basis function neural network is applied to estimate the real output of the *** this output estimation is utilized by active disturbance rejection control to achieve the fault tolerance of ***,the effectiveness of the proposed method is validated by the simulation results of the three-tank system.

关键词： Fault tolerant control active disturbance rejection control radial basis function neural network

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Event-Triggered Nonlinear Model Predictive control with Bounded Disturbances and State-dependent Uncertainties

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IFAC-PapersOnLine 2017年第1期50卷 9308-9314页

作者： Wang M. Sun J. Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing 100081 China

In this paper, two event-triggered nonlinear model predictive control(NMPC) strategies based on Lyapunov function method for discrete-time nonlinear systems with bounded disturbances and state-dependent uncertainties are proposed. The corresponding event-triggered control law is designed and the feasibility is analyzed. Stability of the event-triggered NMPC systems is studied and sufficient stability conditions are obtained. Finally, simulation results show advantages of the two event-triggered strategies on performance and computational burden. © 2017

关键词： event-triggered control,nonlinear model predictive control, Lyapunov function

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Prediction-based Optimal Distributed Filtering with Communication Delay over Sensor Networks

Prediction-based Optimal Distributed Filtering with Communic...

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第三十八届中国控制会议

作者： Jun Hu Guo-Ping Liu Zidong Wang Hongxu Zhang School of Engineering University of South Wales Heilongjiang Provincial Key Laboratory of Optimization Control and Intelligent Analysis for Complex Systems Harbin University of Science and Technology CTGT center Harbin Institute of Technology Department of Computer Science Brunel University London School of Measurement and Communication Harbin University of Science and Technology

In this paper, the prediction-based distributed filtering problem is discussed for a class of time-varying stochastic systems with communication delay and different types of noises over sensor networks. The communication delay is characterized when the state estimations are transmitted between adjacent sensor nodes. In order to compensate the effects induced by the communication delay, the prediction-based idea is employed and then the active compensation estimation is provided when designing the time-varying distributed filter. In particular, both the prediction-based state estimation and its own innovation measurements are utilized in terms of the concerned sensor networks under given topological structure. Subsequently, a locally minimum upper bound of the filtering error covariance is given by determining the filter gain at each time step. Finally, the validity and advantages of the presented prediction-based distributed filtering method are illustrated by some simulations.

关键词： Time-varying stochastic systems Sensor networks Optimal distributed filtering Communication delay Prediction compensation

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Load Frequency control in Power systems via A Sampled-Data controller 37

Load Frequency Control in Power Systems via A Sampled-Data C...

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37th Chinese control Conference, CCC 2018

作者： Shangguau, Xing Chen He, Yong Jiang, Lin School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China University of Liverpool Brownlow Hill LiverpoolL69 3GJ United Kingdom

ISBN: (纸本)9789881563941

In modern power systems, load frequency control (LFC) scheme usually operates in the discrete mode, while the most existing LFC schemes are designed in the continuous mode, such that those LFC schemes do not work usually in their best manner in practice. In this paper, a method of state-feedback controller design of load frequency control (LFC) for one-area system is discussed in continuous-discrete mode via sampled-data control scheme. At first, the model of LFC is constructed in continuous-discrete mode by using the input delay method. Then, a new method is present to design a state-feedback controller. Finally, a case study is given to show the effectiveness and the benefits of the proposed method. © 2018 Technical Committee on control Theory, Chinese Association of Automation.

关键词： State feedback

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Generative Adversarial Networks:Introduction and Outlook

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IEEE/CAA Journal of Automatica Sinica 2017年第4期4卷 588-598页

作者： Kunfeng Wang Chao Gou Yanjie Duan Yilun Lin Xinhu Zheng Fei-Yue Wang IEEE State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Qingdao Academy of Intelligent Industries State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences University of Chinese Academy of Sciences Department of Computer Science and Engineering University of Minnesota Research Center for Computational Experiments and Parallel Systems Technology National University of Defense Technology

Recently, generative adversarial networks(GANs)have become a research focus of artificial intelligence. Inspired by two-player zero-sum game, GANs comprise a generator and a discriminator, both trained under the adversarial learning *** goal of GANs is to estimate the potential distribution of real data samples and generate new samples from that *** their initiation, GANs have been widely studied due to their enormous prospect for applications, including image and vision computing, speech and language processing, etc. In this review paper, we summarize the state of the art of GANs and look into the future. Firstly, we survey GANs' proposal background,theoretic and implementation models, and application ***, we discuss GANs' advantages and disadvantages, and their development trends. In particular, we investigate the relation between GANs and parallel intelligence,with the conclusion that GANs have a great potential in parallel systems research in terms of virtual-real interaction and integration. Clearly, GANs can provide substantial algorithmic support for parallel intelligence.

关键词： ACP approach adversarial learning generative adversarial networks(GANs) generative models parallel intelligence zero-sum game

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Stable control of Single-Link Flexible-Joint Manipulator

Stable Control of Single-Link Flexible-Joint Manipulator

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Chinese control Conference (CCC)

作者： Ze Yan Xuzhi Lai Qingxin Meng Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan Hubei P. R. China

This paper presents a novel approach for stable control of a single-link flexible-joint manipulator (SLFJM). The control objective is to stabilize the SLFJM at the straight-up equilibrium position from the straight-down equilibrium position and suppress vibration by only using position measurement. First, differential homeomorphic transformation is used to equivalently convert the original system into a new handy system. Next, the new system is divided into two parts: linear and nonlinear. The nonlinear part is considered as a virtual disturbance of the linear part. Then, the Equivalent-input-disturbance-based (EID-based) control system is designed to suppress this virtual nonlinear disturbance at the zero equilibrium point. By this way, the control objective of the original system is effectively realized. Finally, the numerical results demonstrate its validity.

关键词： Manipulators Observers Vibrations Shafts State feedback

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Convolutional Neural Network Features Comparison Between Back-Propagation and Extreme Learning Machine

Convolutional Neural Network Features Comparison Between Bac...

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第37届中国控制会议

作者： Atmane Khellal Hongbin Ma Qing Fei School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology

Recently deep learning based architectures have been widely deployed in many problems of artificial *** deep learning models, Convolutional Neural Networks（CNN） have been reported in numerous successful applications such as object recognition, and natural language processing. The convolutional neural networks are trained by back-propagating the classification error using the Back-Propagation（BP） algorithm, which requires a large amount of data and slows the training process. To overcome these difficulties, a new fast and accurate approach based on Extreme Learning Machine（ELM） to train any convolutional neural network has been proposed. The developed framework（ELM-CNN） is based on the concept of autoencoding to learn the convolutional filters with biases, by reconstructing the normalized input and the intercept term. In this paper, systematic comparison with traditional back-propagation based training method（BP-CNN） has been made with respect to two aspects qualitative and quantitative. The experimental results on the popular MNIST dataset show that the ELM-CNN algorithm achieves competitive results in terms of generalization performance and up to 16 times faster than the back-propagation based training of CNN.

关键词： Convolutional Neural Network Extreme Learning Machine Features Comparison Visualization

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Fast Automatic Localization of Epileptic Seizure Onset Zones Using complex Morlet Wavelet Transform-based Singular Value Decomposition

Fast Automatic Localization of Epileptic Seizure Onset Zones...

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Chinese control Conference

作者： Zelin Fang Xiongbo Wan Ting Wan Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan Hubei P. R. China

A new method for localization of epileptic seizure onset zones (SOZs) is proposed, which uses the Shannon-entropy-based complex Morlet wavelet transform to extract a satisfactory time-frequency feature of high-frequency oscillations (HFOs). The singular value decomposition and the K-medoids clustering algorithm are employed to extract effective features from the redundant matrix of wavelet coefficients. A distinctive feature is to use the singular values to detect HFOs with the consideration that the singular values of HFOs are generally significantly higher than those of normal case. Based on the half-maximum method, the localization of SOZs are achieved by using the characteristics of HFOs. Comparisons show that our method provides a higher sensitivity and specificity than two existing methods do.

关键词： Hafnium oxide Clustering algorithms Feature extraction Wavelet transforms Singular value decomposition Oscillators

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Load Frequency control in Power systems via A Sampled-Data controller

Load Frequency Control in Power Systems via A Sampled-Data C...

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第37届中国控制会议

作者： XingChen ShangGuan Yong He Lin Jiang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems University of Liverpool

In modern power systems, load frequency control(LFC) scheme usually operates in the discrete mode, while the most existing LFC schemes are designed in the continuous mode, such that those LFC schemes do not work usually in their best manner in practice. In this paper, a method of state-feedback controller design of load frequency control(LFC) for one–area system is discussed in continuous-discrete mode via sampled–data control scheme. At first, the model of LFC is constructed in continuous-discrete mode by using the input delay method. Then, a new method is present to design a state–feedback ***, a case study is given to show the effectiveness and the benefits of the proposed method.

关键词： Power system Load frequency control Sampled-data control State–feedback controller

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