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Chinese control Conference (CCC)

作者： Ming Zhang Weidong Yang Shaofu Wang Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education) University of Science and Technology Beijing Beijing P. R. China Laiwu Steel Corporation Laiwu P. R. China

The application of u-synthesis controller based on Quantitative Feedback Theory (QFT) tuning method in hot rolling Automatic Gauge control (AGC) system is delivered in this paper. In the proposed strategy, a renowned dual-perturbation AGC model is presented. To the stiffness uncertainties and nonlinear parts, the integration of optimal control μ-synthesis and manual tuning in the QFT design environment enable controller design with a high level of robust performance and robust stability. QFT's open-loop tuning can be more transparent than closed-loop tuning in industrial field. The simulation results show the combined controller is of better gauge control response and more effective on disturbance attenuation.

关键词： Robustness Tuning process control Uncertainty Mathematical model Strips control systems

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Fuzzy dynamic characteristic modeling and intelligent adaptive control for complex spacecraft

Fuzzy dynamic characteristic modeling and intelligent adapti...

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World Congress on Intelligent control and Automation (WCICA)

作者： Xiong Luo Song Hu Zengqi Sun Xintao Qiu School of Information Engineering Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education) University of Science and Technology Beijing Beijing China State Key Laboratory of Intelligent Technology and System Department of Computer Science and Technology Tsinghua National Laboratory for Information Science and Technology Tsinghua University Beijing China

Because of strong coupling, nonlinear, and time-varying characteristics, it is difficult to control complex spacecraft. By means of combining with controlled object dynamics and performance requirements, characteristic modeling and control approach is an effective way to solve this problem. Aimed at the flexible satellites described by using characteristic modeling approach, with the help of fuzzy rules, fuzzy dynamic characteristic modeling method and intelligent adaptive controller are designed to control this complex spacecraft. Meanwhile, based on the satellite system simulation platform, we validate the correctness and efficiency of the proposed modeling and control method by comparing with the simulation results of the other similar methods.

关键词： Adaptation model Laboratories Satellites Space vehicles Intelligent control Artificial intelligence Information science

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Dynamic modeling and tracking control of fuel filled space robotic system with flexible manipulators

Dynamic modeling and tracking control of fuel filled space r...

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International Symposium on Systems and control in Aerospace and Astronautics (ISSCAA)

作者： Xinxin Yang Xiong Luo Fuchun Sun Fengge Wu Huaping Liu School of Information Engineering Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education) University of Science and Technology Beijing Beijing China State Key Laboratory of Intelligent Technology and System Department of Computer Science and TechnologyTsinghua National Laboratory for Information Science and Technology Tsinghua University Beijing China

In recent years, dynamics model and control of space robot system are the hot topics in the research field. In this paper, a new dynamics model and control strategy of space robotic system with a flexible manipulator and a liquid fuel tank are investigated. Based on Lagrange equation method, the dynamics model of the space robotic system coupling with liquid sloshing, flexibility vibration and base movement is derived. The elastic deflection of the flexible manipulator is described by the assumed mode method and equivalent mechanical model is adopted instead of liquid sloshing under the environment of low-gravity. The inverse dynamics control algorithm combined with PD control method is performed to solve the trajectory tracking problem. Some simulation results are given to verify the effectiveness of the proposed method.

关键词： Mathematical model Aerospace electronics Manipulator dynamics Equations Space vehicles

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Intelligent sliding mode control of nonlinear systems based on cloud models

Intelligent sliding mode control of nonlinear systems based ...

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作者： Sun, Qu Wang, Yongyu School of Information Engineering University of Science and Technology Beijing Beijing 100083 China Key Laboratory of Advanced Control for Iron and Steel Process Ministry of Education Beijing 100083 China Century College University of Beijing Post and Telecommunication Beijing 100876 China

ISBN: (纸本)9787894631046

In order to overcome the drawbacks in the sliding-mode controller and the cloud controller, a novel intelligent sliding-mode controller is studied, which combines the two controllers for the first time. A sliding-mode controller based on cloud models, which consists of an equivalent controller and a cloud controller, is proposed for nonlinear systems with system uncertainties and external disturbances. The cloud controller, with the sliding-mode function as its input, is used to replace the discontinuous switch control part to alleviate the chattering phenomenon effectively. The stability of close-loop control system is guaranteed using Lyapunov stability theory. Finallythe designed sliding-mode controller is used to control the Genesion chaos system, and the simulation results demonstrated the feasibility and robustness of the method.

关键词： controllers

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A Hierarchical Image Annotation Method Based on SVM and Semi-supervised EM

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Acta Automatica Sinica 2010年第7期36卷 960-967页

作者： Yan-Yu Gao Yi-Xin YIN Takashi UOZUMI School of Information Engineering University of Science and Technology Beijing Beijing 100083 P. R. China Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education) University of Science and Technology Beijing Beijing 100083 P. R. China Department of Computer Science and Systems Engineering Muroran Institute of Technology Muroran 050-8585 Japan

Automatic image annotation, which aims at automatically identifying and then assigning semantic keywords to the meaningful objects in a digital image, is not a very difficult task for human but has been regarded as a difficult and challenging problem to machines. In this paper, we present a hierarchical annotation scheme considering that generally human s visual identification to a scenery object is a rough-to-fine hierarchical process. First, the input image is segmented into multiple regions and each segmented region is roughly labeled with a general keyword using the multi-classification support vector machine. Since the results of rough annotation affect fine annotation directly, we construct the statistical contextual relationship to revise the improper labels and improve the accuracy of rough annotation. To obtain reasonable fine annotation for those roughly classified regions, we propose an active semi-supervised expectation-maximization algorithm, which can not only find the representative pattern of each fine class but also classify the roughly labeled regions into corresponded fine classes. Finally, the contextual relationship is applied again to revise the improper fine labels. To illustrate the effectiveness of the presented approaches, a prototype image annotation system is developed, the preliminary results of which showed that the hierarchical annotation scheme is effective.

关键词： Hierarchical image annotation support vector machine (SVM) semi-supervised expectation-maximization coexistence relative location relationship

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Prediction and control of strip thickness based on Bayesian neural networks

Beijing Keji Daxue Xuebao/Journal of University of Science a...

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Beijing Keji Daxue Xuebao/Journal of University of Science and Technology Beijing 2010年第2期32卷 256-262页

作者： Peng, Kai-Xiang Yu, Shang-Zhi School of Information Engineering University of Science and Technology Beijing Beijing 100083 China Key Laboratory of the Ministry of Education for Advanced Control of Iron and Steel Process University of Science and Technology Beijing Beijing 100083 China

The Bayesian statistical theory was adopted to improve traditional neural network algorithms, and constraints representing network structural complexity were introduced to the network objective function in order to avoid over-fitting the networks and enhance the generalization ability. The improved networks were applied to strip thickness prediction in Jigang 1700mm mill, and the prediction result is superior to that of traditional neural networks in forecasting accuracy, training time and network stability. Then, the Bayesian neural networks were used to predict the plasticity coefficient of strips. Finally, the real-time forecasts of exit thickness and plasticity coefficient of strips were synthetically utilized in the thickness control system of hot strip rolling to improve strip quality further.

关键词： Forecasting

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Multiple model adaptive control of a kind of discrete time nonlinear

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Kongzhi yu Juece/control and Decision 2010年第6期25卷 841-846页

作者： Li, Xiao-Li School of Information Engineering University of Science and Technology Beijing Beijing 100083 China Key Laboratory of Advanced Control of Iron and Steel Process of Ministry of Education University of Science and Technology Beijing Beijing 100083 China

According to the varying scope of parameters, multiple models are set up for a kind of discrete time nonlinear system, and the corresponding controllers based on these models are given. At every sample time, the best model is selected by using an index switching function based on the estimation error of every model, and the controller based on this model is switched. "Localization" method is used to decrease the computing quantity of multiple model adaptive controller without the loss of control property. It can be proved that the closed-loop system is stable when different controllers are switched each other. Due to the existence of multiple models, the control performance is improved greatly.

关键词： controllers

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Tension Soft Sensor of Continuous Annealing Lines Using Cascade Frequency Domain Observer with Combined PCA and Neural Networks Error Compensation

Tension Soft Sensor of Continuous Annealing Lines Using Casc...

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2010 49th IEEE Conference on Decision and control

作者： Qiang Liu Tianyou Chai Hong Wang S. Joe Qin Key Laboratory of Integrated Automation of Process Industry (Northeastern University) Ministry of Education and Research Center of Automation Northeastern University Shenyang China Control Systems Centre the University of Manchester Manchester M60 1QD the United Kingdom Mork Family Department of Chemical Engineering and Materials Science the Ming Hsieh Department of Electrical Engineering University of Southern California Los Angeles CA 90089-1211 USA

ISBN: (纸本)9781424477456

In continuous annealing processes, strip tension is an important factor which indicates whether the annealing line works steadily. The strip tension detection in the continuous annealing process, therefore, is essential for the reliable and stable operation of the units, and will help to improve the quality of strip products. However, in real annealing processes, only a limited number of strip tensions can be measured. Since installing tension sensors everywhere will be unrealistic and expensive, in this paper a cascade reduced-order observer method with principal component analysis (PCA) and neural networks (NN) compensation is proposed to estimate the unknown tensions between each two neighboring rolls so as to monitor the tension profile for the whole line. When the main observer model is established, the influences of strip inertia and roll eccentricity on tensions are taken into account for better estimation. Moreover, when the error compensation is designed, PCA scores are used as the inputs to the NN. The application results show the effectiveness of the proposed method, with the estimated tensions used for the analysis of the strip-breaks fault.

关键词： Error compensation Continuous annealing annealing tension strip tension stability operations Neural network cascades

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Evolutionary algorithm using kernel density estimation model in continuous domain

Evolutionary algorithm using kernel density estimation model...

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Asian control Conference

作者： Na Luo Feng Qian Key Laboratory of Advanced Control and Optimization of Chemical Process Ministry of Education State Key Laboratory of Chemical Engineering East China University of Science and Technology Shanghai China Key Laboratory of Advanced Control and Optimization of Chemical Process Ministry of Education State-Key Laboratory of Chemical Engineering East China University of Science and Technology Shanghai China

ISBN: (纸本)9781424454402

Estimation of distribution algorithm (EDA) is a kind of evolutionary algorithm which updates and samples from probabilistic model in evolutionary course. The key of EDA is the construction of probability model suitable for real distribution. Gaussian distribution is widely used in EDAs but the assumption of normality is not realistic for many real-life problems. In this paper, a new EDA using kernel density estimation (KEDA) is introduced. Adaptive change strategy of kernel width is presented and selection scheme, sampling method are also given cooperated with KEDA. The results of 5 benchmark functions show that results of KEDA outperform PBIL C , UMDA C , EDA G , H-EDA.

关键词： Evolutionary computation Kernel Electronic design automation and methodology Laboratories Histograms Density functional theory Gaussian distribution Automatic control chemical processes control engineering education

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Quantum clustering-based weighted linear programming support vector regression for multivariable nonlinear problem

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Soft Computing 2009年第9期14卷 921-929页

作者： Yu, Yanfang Qian, Feng Liu, Huimin Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China

Linear programming support vector regression shows improved reliability and generates sparse solution, compared with standard support vector regression. We present the v-linear programming support vector regression approach based on quantum clustering and weighted strategy to solve the multivariable nonlinear regression problem. First, the method applied quantum clustering to variable selection, introduced inertia weight, and took prediction precision of v-linear programming support vector regression as evaluation criteria, which effectively removed redundancy feature attributes and also reduced prediction error and support vectors. Second, it proposed a new weighted strategy due to each data point having different influence on regression model and determined the weighted parameter p in terms of distribution of training error, which greatly improved the generalization approximate ability. Experimental results demonstrated that the proposed algorithm enabled the mean squared error of test sets of Boston housing, Bodyfat, Santa dataset to, respectively, decrease by 23.18, 78.52, and 41.39%, and also made support vectors degrade rapidly, relative to the original v-linear programming support vector regression method. In contrast with other methods exhibited in the relevant literatures, the present algorithm achieved better generalization performance.

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