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Communications in Computer and Information Science 2013年 355卷 496-504页

作者： Chen, Shihe Zhang, Xi Wang, Guoliang Yan, Weiwu Guangdong Electric Power Research Institute Guangzhou 510600 China Department of Automation Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China

In this paper, development of ultra supercritical unit control is summarized. Based on analyzing the control difficulties and the input-output relationship of Ultra-supercritical Units, a model predictive control scheme for Ultra-supercritical Unit is proposed. The input variables are fuel flow, turbine valve opening and water flow and output variables are load, steam temperatureteam pressure. The algorithm and implementation method are also given in details. © Springer-Verlag Berlin Heidelberg 2013.

关键词： Model predictive control

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Research and application of multiple model Predictive control in Ultra-supercritical boiler-turbine system

Communications in Computer and Information Science

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Communications in Computer and Information Science 2013年 355卷 469-476页

作者： Tian, Hengfeng Yan, Weiwu Wang, Guoliang Chen, Shihe Zhang, Xi Hu, Yong Li, Nan Department of Automation Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China Guangdong Electric Power Research Institute Guangzhou 510600 China

Based on analyzing the characteristics of Ultra-supercritical unit, this paper introduced a multiple model MCPC (Multivariable Constrained Predictive control) structure with three inputs and three outputs for coordination control of Ultra-supercritical unit. In the structure, double-layer structure of optimization was used to obtain good steady and dynamic performance, and piecewise linear models at the different operating points of Ultra-supercritical unit were used to deal with nonlinearity. In the real-time simulation, nonlinear model of 1000MW Ultrasupercritical unit in [1] was considered. Finally, the result of real-time simulation was given in the paper. © Springer-Verlag Berlin Heidelberg 2013.

关键词： Model predictive control

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H2 analytical decoupling design for a High-Angle-of-Attack missile

H2 analytical decoupling design for a High-Angle-of-Attack m...

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第三十二届中国控制会议

作者： ZHANG Wei SUN Bo OU Linlin ZHANG Weidong Department of Automation Shanghai Jiao Tong UniversityShanghaiChina Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Department of Automation Zhejiang University of Technology

ISBN: (纸本)9781479900305

The cross-coupling effects increase acutely as the angle of attack increases,which may result in unpredictable ***,as the angle of attack increases,the system may become static unstable and make the design of autopilot more *** order to overcome the problem,a new H analytical decoupling design approach which suitable for unstable non-minimum phase(NMP) systems is applied to a high-angle-of-attack *** with other developed methods,the new H analytical decoupling design method has three evident advantages:Firstly,the design procedure is simple. No weight function needs to be ***,the design result is easy to *** resulting controller is given in an analytical ***,the performance and robustness of the closed loop system can be easily *** simulation of 5-degree-of-freedom(DOF) missile with high-angle-of-attack proves the effectiveness of the design method.

关键词： High-angle-of-attack missile analytical decoupling H2 optimal control IMC

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Enhancing the Effectiveness of Fungicides by Optimizing Their Combinations

Enhancing the Effectiveness of Fungicides by Optimizing Thei...

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第26届中国控制与决策会议

作者： Xiang Wang Jia Ma Xiaowei Li Xiaodong Zhao Zongli Lin Jie Chen Zhifeng Shao Department of Automation and Key Laboratory of System Control and Information Processing of the Ministry of EducationShanghai Jiao Tong University Department of Resource and Environmental Science School of Agriculture and BiologyShanghai Jiao Tong University School of Biomedical Engineering Shanghai Jiao Tong University Charles L.Brown Department of Electrical and Computer Engineering University of Virginia

In controlling biological diseases, it is often more potent to use a combination of agents than using individual ones. However, the number of possible combinations increases exponentially with the number of agents and their concentrations. It is prohibitive to search for effective agent combinations by trial and error as biological systems are complex and their responses to agents are often a slow process. This motivates to build a suitable model to describe the biological systems and help reduce the number of experiments. In this paper, we consider the use of fungicides to inhibit Bipolarismaydis and construct models that describe the responses to fungicide combinations. Three data-driven modeling methods, the polynomial regression, the artificial neural network and the support vector regression, are compared based on the experimental data of the inhibition rates of the southern corn leaf blight with different fungicide combinations. The analysis of the results demonstrates that the support vector regression is best suited to the construction of the response model in terms of achieving better prediction with fewer experiments.

关键词： agent combinations,complex system,data-driven model,polynomial regression,artificial neural network,support vector regression

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Robust visual tracking based on online learning of joint sparse dictionary

Robust visual tracking based on online learning of joint spa...

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6th International Conference on Machine Vision, ICMV 2013

作者： Li, Qiaozhe Qiao, Yu Yang, Jie Bai, Li Key Laboratory of Ministry of Education for System Control and Information Processing Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China School of Computer Science University of Nottingham Nottingham United Kingdom

ISBN: (纸本)9780819499967

In this paper, we propose a robust visual tracking algorithm based on online learning of a joint sparse dictionary. The joint sparse dictionary consists of positive and negative sub-dictionaries, which model foreground and background objects respectively. An online dictionary learning method is developed to update the joint sparse dictionary by selecting both positive and negative bases from bags of positive and negative image patches/templates during tracking. A linear classifier is trained with sparse coefficients of image patches in the current frame, which are calculated using the joint sparse dictionary. This classifier is then used to locate the target in the next frame. Experimental results show that our tracking method is robust against object variation, occlusion and illumination change. © 2013 SPIE.

关键词： E-learning

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Boundary feedback stabilization and control of star-shaped open canals

Boundary feedback stabilization and control of star-shaped o...

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13th IFAC Symposium on Large Scale Complex systems: Theory and Applications, LSS 2013

作者： Cen, Lihui Xi, Yugeng Li, Dewei Department of Control Engineering Central South University Changsha China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China Department of Automation Shanghai Jiaotong University Shanghai China

This paper considers the boundary control of a star-shaped open-channel network modeled by the Saint-Venant equations. We present the boundary feedback stabilization of the Saint-Venant equations by means of a Riemann invariants method. The estimation of the parameters satisfying the stability condition is deduced. The boundary feedback control is developed to guarantee that the solution of the control system decays exponentially, only by taking the water levels at the gate boundaries as the feedback. © IFAC.

关键词： Stabilization

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Min-max MPC for LPV systems subject to Actuator Saturation by a Saturation-dependent Lyapunov Function

Min-max MPC for LPV Systems subject to Actuator Saturation b...

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第三十二届中国控制会议

作者： ZHANG Langwen WANG Jingcheng LI Kang Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of China School of Electronics Electrical Engineering and Computer Science Queen's University Belfast

ISBN: (纸本)9781479900305

In this paper,we present a model predictive control algorithm for input-saturated systems by a saturation-dependent Lyapunov function *** saturation-dependent Lyapunov function captures the real-time information on the severity of saturation and thus leads to less conservative results in controller design.A set invariance condition for the systems with input saturation is presented.A min-max MPC algorithm is proposed for the linear parameter-varying(LPV) systems based on the invariant *** MPC controller is determined by solving a linear matrix inequality(LMI) optimization *** example demonstrates the effectiveness of the proposed algorithm.

关键词： Model Predictive control Linear parameter-varying Actuator saturation Saturation-dependent

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Automated classification of protein subcellular location patterns on images of human reproductive tissues

Automated classification of protein subcellular location pat...

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3rd Sino-Foreign-Interchange Workshop on Intelligent Science and Intelligent Data Engineering, IScIDE 2012

作者： Yang, Fan Xu, Ying-Ying Shen, Hong-Bin Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Shanghai China Key Laboratory of Optic-Electronic and Communication Jiangxi Science and Technology Normal University Nanchang China

ISBN: (纸本)9783642366680

As one of the most significant characteristics of human cell, subcellular localization plays a critical role for understanding specific functions of mammalian proteins. In this study, we developed a novel computational protocol for predicting protein subcellular locations from microscope cell images in human reproductive tissues. Experiments are performed on a benchmark dataset consisting of 7 major subcellular classes in human reproductive tissues collected from Human Protein Atlas database. We first separated protein and DNA staining in the images with both linear and nonnegative matrix factorization separation methods;then we extracted protein multi-view texture features including wavelet haralick and local binary patterns;finally based on the selected important feature subset achieved by feature selection technique, we constructed ensemble classifier based on support vector machines for predictions. Our experimental results show that 84% accuracy can be achieved through current system, and when only considering the most confident classifications, the accuracy can rise to 98%. © Springer-Verlag 2013.

关键词： Support vector machines

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Robust H∞ control with partly quantized information

Robust H∞ control with partly quantized information

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第三十二届中国控制会议

作者： GE Yang WANG Jingcheng LI Kang Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information ProcessingMinistry of Education of China School of Electronics Electrical Engineering and Computer ScienceQueen's University Belfast

ISBN: (纸本)9781479900305

This paper is concerned with the stability and stabilizability problems of networked control systems（NCSs） with partly quantized *** precisely,the remote state variables transported from other sub-systems experience quantization errors,while the local state variables do *** consideration is much more natural in NCSs due to the distributive nature of *** errors are represented as convex poly-topic *** on the Lyapunov-Krasovskii （L-K） functional approach,sufficient conditions for the existence of a quantized robust Hstate feedback controller for NCSs are *** conditions are obtained in terms of bilinear matrix inequalities（BMIs）.Furthermore,a cone complementarity algorithm is utilized to convert these BMIs into a convex optimization ***,a simulation example is provided to demonstrate the efficiency of proposed theorems.

关键词： robust control quantized feedback control partly quantized information

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Model predictive control for constrained uncertain piecewise linear systems

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控制理论与应用（英文版） 2013年第2期11卷 230-236页

作者： Dewei LI Yugeng XI Yuanyuan ZOU Department of Automation and Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology

For constrained piecewise linear （PWL） systems, the possible existing model uncertainty will bring the difficulties to the design approaches of model predictive control （MPC） based on mixed integer programming （MIP）. This paper combines the robust method and hybrid method to design the MPC for PWL systems with structured uncertainty. For the proposed approach, as the system model is known at current time, a free control move is optimized to be the current control input. Meanwhile, the MPC controller uses a sequence of feedback control laws as the future control actions, where each feedback control law in the sequence corresponds to each partitions and the arbitrary switching technique is adopted to tackle all the possible switching. Furthermore, to reduce the online computational burden of MPC, the segmented design procedure is suggested by utilizing the characteristics of the proposed approach. Then, an offline design algorithm is proposed, and the reserved degree of freedom can be online used to optimize the control input with lower computational burden.

关键词： Model predictive control Constrained piecewise linear system Structured uncertainty Segmented designprocedure

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