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2017 ASEE International Forum

作者： Liu, Xiwei Gong, Xiaoyan Wang, Fei-Yue Sun, Rui Gao, Yanqing Zhang, Yu Zhou, Jiehan Deng, Xiaofei State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China Intelligent Education Institution Qingdao Academy of Intelligent Industries China Qingdao Experimental High School China Oregon Institute of Technology United States University of Oulu Finland

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A full-dq based adaptive virtual impedance compensation in virtual power frame for droop-controlled parallel inverters under unbalanced conditions

A full-dq based adaptive virtual impedance compensation in v...

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Annual Conference of Industrial Electronics Society

作者： Yuan Cao Zhen Li Zelun Lu Zhen Chen Xiangdong Liu School of Automation and Key Laboratory for Intelligent Control & Decision on Complex Systems Beijing Institute of Technology Beijing China

Distributed generation (DG) is connected through three-phase inverter to the microgrid. The virtual power based droop control can guarantee the pure decoupling between active and reactive power irrespective of low-voltage or high-voltage microgrid application if the line-impedance information is known in advance. However, unbalances are commonly existent in practice so that this conventional control fails in application due to the significant coupling between active and reactive power. To overcome this problem and further extend virtual power based method into unbalanced conditions, this paper proposes an improved virtual power decoupling technique using full-dq transformation, which is utilized to identify arbitrary unbalanced components under unbalanced conditions. The identified unbalances are further compensated as an virtual impedance so as to ensure the pure decoupling of active and reactive power in the virtual power frame. Finally, simulation results are provided to verify the effectiveness of the proposed method under unbalanced conditions.

关键词： Impedance Reactive power Inverters Microgrids Voltage control Frequency control Couplings

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Joint task management of sensor and weapon based on distributed management system

Joint task management of sensor and weapon based on distribu...

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Chinese control Conference (CCC)

作者： Zhang Mingyang Chen Chen Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing School of Automation Beijing Institute of Technology Beijing

ISBN: (纸本)9781538629185

In the modern war, many combat units combat collectively has become the main form. In the task management of the combat units, the calculation is large and the real-time requirement is high. To solve the problem better, a joint task management method of sensor and weapon based on distributed management system is proposed. Firstly, the distributed management system is constructed according to the target trajectory and the distribution of combat units. In the system, all the combat units are divided into several combinations, which only consist of a few combat units. As a result, the problem scale is reduced, and the real-time requirement of battlefield is met. Secondly, joint task management model of sensor and weapon is established. We consider time constraints and resource constraints to ensure the feasibility of the attack scheme, and consider the impact between sensor and weapon to achieve better combat effect. A discrete particle swarm optimization algorithm based on crossover strategy (CDPSO) is proposed to solve the model. The results show that the performance of the proposed algorithm is satisfactory in joint task management of sensor and weapon.

关键词： Weapons Distributed management Command and control systems Time factors Mathematical model Data processing Real-time systems

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Performance monitoring and parameter optimization of fuzzy controller in combustion process of coke Oven

Performance monitoring and parameter optimization of fuzzy c...

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Asian control Conference

作者： Qi Lei Di Zhu Xu-Zhi Lai Min Wu School of Information Science and Engineering University of Central South University Changsha China 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

The controller parameters can not perform well in the combustion process of coke oven, caused by the fluctuating calories of fuel gas, the changes of coking time and many other exogenous disturbances. In this paper, an optimal method based on performance monitoring by using the trend extraction method, is raised to adjust the parameters of the controllers in the combustion process of coke oven. Firstly, because of the mixture of different kinds of error, the trend extraction method is used to separate trend component from original signal. Secondly, the monitoring indices are designed to examine whether the monitored controller parameters are suitable in the current situation. Then, the performance monitoring result is utilized to choose the decision vector of optimization and determine whether optimization is needed. Finally, simulation results demonstrate that our method performs well in the optimization for combustion process of coke oven in spite of massive changes in the calories of fuel gas.

关键词： Monitoring Combustion Market research Optimization Process control Ovens Heating systems

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A fast test and compensation system for optical encoders based on extreme learning machine — Fourier Neural Network

A fast test and compensation system for optical encoders bas...

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Youth Academic Annual Conference of Chinese Association of automation (YAC)

作者： Jiachen Zhao Jie Chen Fang Deng Hongda Li Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing China School of Automation Beijing Institute of Technology BeiJing

ISBN: (纸本)9781538629024

This paper describes fast test and compensation system for optical encoders. The resolution of system is 0.375", and it can measure the actual accuracy of an optical encoder in four minutes. Furthermore, a method called Extreme Learning Machine - Fourier Neural Network (ELM-FNN) are proposed to compensate the error. Fourier neural network (FNN) is chosen to fit the curve of the optical encoder's output, and the weights of FNN is calculated by Extreme Learning Machine (ELM). Experimental results demonstrate that ELM-FNN effectively improve the accuracy of the optical encoder. Compared to a back propagation neural network (BP net) and a standard FNN, ELM-FNN has advantages of higher accuracy and less training time.

关键词： Adaptive optics Optical computing Optical variables measurement Optical sensors Neural networks Ultraviolet sources Training

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An SNR-Adaptive Relaying Algorithm for Multi-Antenna Cooperative Networks

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China Communications 2017年第10期14卷 228-236页

作者： Shuangshuang Han Peng Zhang Feijin Shi Fei-Yue Wang The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of SciencesBeijing China Qingdao Academy of Intelligent Industries Qingdao Shandong Province China The School of Computer Engineering Weifang University Shandong P. R. China Communications Headquarters Ministry of Foreign Affairs of the People's Republic of China Beijing China Research Center of Computational Experiments and Parallel Systems The National University of Defense Technology Changsha China

Multiple-input multiple-output(MIMO) and cooperative communications have been attracted great attention for the improvements of communication capacity, power consumption, and transmission coverage. The conventional fixed relaying protocols, amplify-and-forward(AF) and decode-and-forward(DF), have their own advantages and disadvantages, i.e. AF performs better than DF for low signal-to-noise ratio(SNR) region, while the reverse is true for high SNR region. Therefore, this paper proposes an SNR-adaptive forward(SAF) relaying scheme obtaining the advantages of both AF and DF. Furthermore, the proposed SAF does not need to switch between AF and DF when SNR changes. The main idea is to adaptively derive the soft information at the cooperative relay nodes based on the information of the received signal and the SNR. Besides, based on the theoretical analysis and the simulation results, it is affirmed that the proposed SAF achieves superior performance than both AF and DF for all SNRs. Moreover, the performance gain would be improved with the increasing number of parallel cooperative relay nodes.

关键词： MIMO SNR SNR-adaptive forward multi-antenna

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Algebraic Connectivity Estimation Based on Decentralized Inverse Power Iteration

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Asian Journal of control 2017年第2期19卷 805-812页

作者： Wei, Yue Fang, Hao Chen, Jie Xin, Bin School of Automation Beijing Institute of Technology Beijing 100081 China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing 100081 China

In this work, we propose a new scheme to estimate the algebraic connectivity of the graph describing the network topology of a multi-agent system. We consider network topologies modeled by undirected graphs. The main idea is to propose a new decentralized conjugate gradient algorithm and a decentralized compound inverse power iteration scheme. The matrix inversion computation in this scheme is replaced by solving the non-homogeneous linear equations relying on the proposed decentralized conjugate gradient algorithm. With this scheme, we can achieve a fast convergence rate in estimating the algebraic connectivity by setting the parameter μ properly. Simulation results demonstrate the effectiveness of the proposed scheme. © 2016 Chinese Automatic control Society and John Wiley & Sons Australia, Ltd

关键词： Network topology

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Cooperative transportation control of multiple mobile manipulators through distributed optimization

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Science China. Information Sciences 2018年第12期61卷 120201-1--120201-1--120201-17页

作者： Chen Jie Kai Shixiong School of Automation Beijing Institute of Technology Key Laboratory of Intelligent Control and Decision of Complex SystemsMOE Beijing 100081

This paper investigates the problem of distributed control of multiple redundant mobile manipulators to collectively transport an object tracking a desired trajectory with energy and manipulability optimized. To solve this optimization problem, formation control tasks are introduced as equality constraints with the variables being the velocities. In this paper, we propose a distributed proximal gradient algorithm searching for the optimal solution, with which the stability of the closed-loop system is proved. Simulations demonstrate the effectiveness of the proposed distributed optimization scheme and proximal algorithm.

关键词： multiple mobile manipulators cooperative transportation control motion planning energy and manipulability distributed optimization

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End-to-End Driving Activities and Secondary Tasks Recognition Using Deep Convolutional Neural Network and Transfer Learning

End-to-End Driving Activities and Secondary Tasks Recognitio...

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2018 IEEE intelligent Vehicles Symposium, IV 2018

作者： Xing, Yang Tang, Jianlin Liu, Hong Lv, Chen Cao, Dongpu Velenis, Efstathios Wang, Fei-Yue Advanced Vehicle Engineering Centre Cranfield University BedfordMK43 0AL United Kingdom Vehicle Intelligence Pioneers Ltd Qingdao China Jiangsu XCMG Construction Machinery Research Institute Ltd. No. 26 Tuolanshan Road Economic Development Zone Xuzhou Jiangsu221004 China School of Mechanical and Mechatronics Engineering University of Waterloo 200 University Avenue West WaterlooONN2L 3G1 Canada State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781538644522

Drivers' decision and their corresponding behaviors are important aspects that can affect the driving safety, and it is necessary to understand the driver behaviors in real-time. In this study, an end-to-end driving-related tasks recognition system is proposed. Specifically, seven common driving activities are identified, which are normal driving, right mirror checking, rear mirror checking, left mirror checking, using in-vehicle video device, texting, and answering mobile phone. Among these, the first four activities are regarded as normal driving tasks, while the rest three are divided into distraction group. The images are collected using a consumer range camera, namely, Kinect. In total, five drivers are involved in the naturalistic data collection. Before training the identification model, the raw images are first segmented using a Gaussian mixture model (GMM) to extract the driver region from the background. Then, a pre-trained deep convolutional neural network (CNN) model is trained to classify the behaviors, which directly takes the processed RGB images as the input and outputs the identified label. In this work, the AIexNet is selected as the pre-trained CNN model. Then, to reduce the training cost, the transfer learning mechanism is applied to the CNN model. An average of 79\% detection accuracy is achieved for the seven driving tasks. The proposed integration model can be used as a low-cost driver distraction and dangerous tasks recognition modeL. © 2018 IEEE.

关键词： Neural network models

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Downhole-Friction-Estimation-Based Rotary Speed control for Drillstring System with Stick-Slip Vibrations

Downhole-Friction-Estimation-Based Rotary Speed Control for ...

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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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