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检索条件"机构=Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex System"

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control of Wireless Network Communication for Industrial Robot Servo systems

Control of Wireless Network Communication for Industrial Rob...

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第37届中国控制会议

作者： Meiliu Li Guangjun Wang Jinhua She Zhentao Liu Danyun Li School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Engineering Tokyo University of Technology

To meet the needs of high speed and good environmental adaptability for data transmission in industrial robot servo systems, this paper presents a wireless transmission technology on a data acquisition control terminal. The system is based on STM32 F103 RET6 processor and reads cache data from the SRAM of FPGA in DMA method, and communicates with a WIFI chip, Marvell 88 w8686, through the SDIO interface. A host computer segments and filters the data received by the WIFI chip and sends control commands back to STM32. The functions of the hardware and software of the system contains data compression and storage, wireless data transmission, data display, and control-command transmission. An experimental platform has been built. It carries out real-time transmission of 8-16 digits based on the TCP/IP protocol with expected performance.

关键词： Servo systems WIFI wireless communication TCP/IP protocol

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Exponential Stability of Highly Nonlinear Neutral Stochastic Differential Delay Equations

Exponential Stability of Highly Nonlinear Neutral Stochastic...

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第37届中国控制会议

作者： Yinghui HU Xiaofeng ZONG Jinhua SHE School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Engineering Tokyo University of Technology

This paper investigates the mean square and almost sure stability of a class of neutral stochastic differential delay equations with highly nonlinear *** first examine the regularity of the solution to the highly nonlinear neutral stochastic differential delay *** the explicit stability conditions are obtained by the Lyapunov functional and semimartingale convergence ***,the explicit stability conditions of pure delay neutral stochastic differential delay equations are *** is revealed that the delay term in the drift can contribute to the mean square and almost sure stability.

关键词： Neutral stochastic differential delay equations stability Lyapunov functional

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An intelligent Optimization Strategy Based on Prediction Model for Carbon Efficiency in Sintering Process

An Intelligent Optimization Strategy Based on Prediction Mod...

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第37届中国控制会议

作者： Jie Hu Min Wu Jinhua She School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Engineering Tokyo University of Technology

In order to improve the utilization of carbon in a sintering process, it is necessary to predict and optimize carbon efficiency. In the paper, first, comprehensive coke ratio（CCR） is taken to be a metric of the carbon efficiency. By analyzing the sintering mechanism, sintering parameters affecting the CCR are determined. Next, the fuzzy C-means clustering algorithm is used to identify different operating conditions. Then, least-squares support vector machine（LS-SVM） sub-models are established for the different operating conditions, and a CCR prediction model is established by incorporating the sub-models using the TS fuzzy intelligent fusion method. Finally, based on the CCR prediction model, a differential evolution algorithm is used to optimize the CCR by adjusting the operating parameters. Simulations using actual run data show that the prediction accuracy of the CCR prediction model is higher than that of a back-propagation neural network model and a single LS-SVM model, and the carbon efficiency optimization strategy reduced the CCR by 1.97 kg/t on average. Thus, the method provides us a guidance for an actual sintering process.

关键词： Sintering process Carbon efficiency Fuzzy C-means clustering Differential evolution algorithm Least square-support vector machine model

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An intelligent control Scheme for Burn-Through Point to Carbon Efficiency Optimization in Iron Ore Sintering Process

An Intelligent Control Scheme for Burn-Through Point to Carb...

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第37届中国控制会议

作者： Sheng Du Min Wu Jinhua She School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Engineering Tokyo University of Technology

The sintering process is an important step of preparing raw material for iron and steel smelting. It is a series of complex physico-chemical changes with high energy consumption, high pollution and large CO emissions. How to reduce carbon consumption while ensuring the quality and yield of sinter ore are the problems to be solved urgently. In this paper,An intelligent control scheme for burn-through point(BTP) to carbon efficiency optimization in iron ore sintering process is presented. The comprehensive coke ratio is employed as a measure of carbon efficiency;and the BTP, a measure of the stability of the sintering process. First, a model is established to predict the comprehensive coke ratio using a back propagation neural network, and the carbon efficiency is optimized using the particle swarm optimization algorithm. This yields an optimal strand velocity. Then, the control of the BTP is taken into consideration using an expert-fuzzy control strategy. This yields another strand velocity. Finally, these two kinds of strand velocity are integrated using the fuzzy satisfaction method to produce a control input for the strand velocity. It not only improves the carbon efficiency, but also ensures the stable running of the sintering process. Part of the strategy was tested in an actual plant, and the results show the effectiveness of the scheme.

关键词： Sintering process Carbon efficiency Optimization Burn-through point intelligent control

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Analysis and Fractional PD control Bifurcation of a Fractional-Order Genetic Regulatory Networks with Delays

Analysis and Fractional PD Control Bifurcation of a Fraction...

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第37届中国控制会议

作者： Feng Liu Ting Dong Qicheng Mei Shiqi Zheng Guang Ling Longsheng Wei School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Science Wuhan University of Technology

In this paper, a fractional-order gene regulatory network model with two delays is investigated. The existence condition of Hopf bifurcation is deduced by analyzing its characteristic equation, where the total delay is taken as a bifurcation parameter. These bifurcation behaviors may cause the network system heavy oscillation and instability. An fractional PD control method is proposed to control bifurcations in the system. Finally, numerical simulations are provided to verify the theoretical analysis. Simulation results show that the nonlinear behavior of the system can be controlled by this method.

关键词： Hopf bifurcation fractional PD control genetic regulatory networks time delays

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Further Result on the Stability Analysis of Time-Delay system Based on a Novel Inequality

Further Result on the Stability Analysis of Time-Delay Syste...

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第37届中国控制会议

作者： Yibo Huang Jianqi An Yong He School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Engineering Tokyo University of Technology

This paper investigates the issue for stability of linear system with time-varying ***,by combining the Freematrix technique and the B-L inequality,a new inequality named Free-matrix Legendre inequality is ***,based on this inequality,two sets of stability criteria are developed which are less conservative than the existing ones due to the fact that more freedom are ***,a numerical example is given to illustrate the advantage of our method.

关键词： Time-delay system Lyapunov-Krasovskii functional Integral inequality Conservatism analysis

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Iterative Learning control for Batch Weighing and Feeding Process

Iterative Learning Control for Batch Weighing and Feeding Pr...

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第37届中国控制会议

作者： Fayang You Jianqi An School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Engineering Tokyo University of Technology

Due to the fact that material properties may change, weighing and feeding process is difficult to achieve high accuracy with conventional control methods. This paper discusses application of iterative learning control in weighing and feeding process,which takes advantage of previous control information. First, a continual state-space model is established based on dynamic analysis of weighing and feeding process. Next, this model is discretized into a two-dimensional state space model, which takes into account more state information about the process. Then, an iterative learning controller is presented, which improves the tracking performance with the increase of feeding times. A multi-objective simulated annealing is used to find the controller parameters with better control performance under the stability condition. Finally, simulation results are given, which show that a faster feed rate and more accurate feed accuracy is obtained after several feeding batches.

关键词： Weighing and Feeding process Iterative learning control Batch Process Multi-object optimization 2D state space model Linear matrix inequality

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Burden Distribution Decision for Blast Furnace Based on Multiple Condition State

Burden Distribution Decision for Blast Furnace Based on Mult...

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第37届中国控制会议

作者： Huicong Chen Jianqi An School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Engineering Tokyo University of Technology

The relationship of state parameters and burden distribution is uncertain in blast furnaces. In the present industry,burden operation mainly relies on the experiences of the workers. So it is difficult to control burden operation. To solve these problems, this paper presents a model to adjust the burden distribution using fuzzy C-means（FCM） and wavelet analysis. First,multiple condition states of blast furnaces are analysed through data processing, the state parameters are clustered based on the similarity, and then this paper searches for the corresponding burden parameters from the history data. Finally, for different state clusters, best burden parameters are selected to adjust the conditions. Simulation results show that the burden distribution adjustments based on the state parameters clustering are efficient.

关键词： Multiple Condition Burden Distribution Fuzzy C-Means Blast Furnace

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Bifurcation Analysis and control of Fractional-Order Quorum Sensing Network

Bifurcation Analysis and Control of Fractional-Order Quorum ...

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第37届中国控制会议

作者： Liu Feng Dai Xiangjuan Mei Qicheng Ling Guang Wang Xinmei School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Science Wuhan University of Technology

In this paper, bifurcation analysis and control of fractional-order quorum sensing network regulated by s RNA is studied. The dynamics of fractional quorum sensing network with time-delay is analyzed. The stability criterion and bifurcation conditions of the fractional quorum sensing network are given. In order to control the dynamic properties of fractional order systems, a feedback controller is designed, and the complex dynamics of controlled fractional-order networks are further investigated. The simulation results show that this control method can achieve the ideal control effect.

关键词： Quorum sensing network fractional-order bifurcation control

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Deep Forest-Based Classification of Hyperspectral Images

Deep Forest-Based Classification of Hyperspectral Images

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第37届中国控制会议

作者： Xu Yin Ruilin Wang Xiaobo Liu Yaoming Cai School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Computer Science China University of Geosciences

The classification of hyperspectral images(HSIs) is a hot topic in the field of remote sensing technology. In recent years, convolutional neural network(CNN) has achieved great success for HSI classification. However, CNN has to do a great effort in parameters tuning which is time-consuming. Furthermore, a large number of samples are required to train CNN,nevertheless, it is expensive to obtain enough training samples from HSIs. In this paper, we propose a novel classification approach based on deep forest. To reduce the dimension of hyperspectral data, principal component analysis(PCA) is performed during the pre-processing. In contrast to the CNN, our method has fewer hyper-parameters and faster training speed. To the best of our knowledge, this is among the first deep forest-based hyperspectral spectral information classification. Extensive experiments are conducted on two real-world HSI datasets to show the proposed method is significantly superior to the state-ofthe-art methods.

关键词： Deep Forest Hyperspectral Image（HSI） Deep Neural Network（DNN） Principal Component Analysis（PCA）

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