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第三十四届中国控制会议

作者： Yu Zhaoxu Li Shugang Li Fangfei Key Laboratory of Advanced Control and Optimization for Chemical Process of Ministry of Education East China University of Science and Technology Department of Industrial Engineering and Logistics Shanghai Jiao Tong University Department of Mathematics East China University of Science and Technology

ISBN: (纸本)9789881563897

The problem of output feedback adaptive stabilization is addressed for a class of stochastic nonlinear systems with unknown time-varying delays and unknown control directions in this paper. Firstly, the unknown control coefficients are lumped together by using a linear state transformation, and the original system is transformed into a new system for which control design becomes feasible. Then, after the design of a novel neural observer, an output feedback adaptive neural network(NN) controller is developed for such systems by combining the Dynamic Surface control(DSC) technique, the Nussbaum gain function(NGF)method and the Lyapunov-Krasovskii method. The proposed controller ensures that all signals in the closed-loop systems are bounded in probability. Finally, a simulation example is given to verify the effectiveness and applicability of the proposed control design.

关键词： Stochastic systems,output feedback,neural network(NN),unknown control direction,time-varying delay

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Improving the performance of online classifier by removing the error samples from offline training data

Improving the performance of online classifier by removing t...

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International Conference on Computer and Communications (ICCC)

作者： Hanhan Zhang Jing Jin Sijie Zhou Yu Zhang Xingyu Wang Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China

Brain-computer interface (BCI) plays an important role in helping the people with severe motor disability. In event-related potential (ERP) based BCIs, subjects were asked to count the target stimulus in the offline experiment, the recorded electroencephalogram (EEG) data was used to train the classification mode. However, subjects may make mistakes in counting the target stimulus or be affected by the non-target stimulus. The target trials may not contain expected ERPs and the non-target trials may contain unexpected ERPs, which was called error samples. This paper intends to survey whether the classification accuracy could be improved after removing these error samples from offline training data. The result showed that the online performance of BCI system could be improved after selecting the offline samples for training the classification mode.

关键词： Electroencephalography Feature extraction Training Classification algorithms Electrodes Band-pass filters Brain modeling

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Event-triggered model predictive control for wireless networked control systems with packet losses

Event-triggered model predictive control for wireless networ...

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IEEE International Conference on Cyber Technology in Automation, control, and Intelligent Systems

作者： Quan Wang Yuanyuan Zou Yugang Niu Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China

ISBN: (纸本)9781479987313

This study investigates an event-triggered model predictive control for wireless networked control system with packet losses in the sensor-to-controller channel. Based on a predictive control compensation strategy, the closed-loop model with packet losses is established. The event-triggered conditions are derived by choosing the performance objective function of MPC as a Lyapunov function. Further, the maximal allowable number of successive packet losses is presented. Under the proposed mechanism, the energy consumption of the wireless network is alleviated and closed-loop stability is guaranteed. Finally, simulation results are shown to illustrate the effectiveness of the proposed method.

关键词： Packet loss Predictive control Networked control systems Wireless networks Closed loop systems Wireless sensor networks

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An Optimized BCI System Based on P300 and Visual Mismatch Negativity

An Optimized BCI System Based on P300 and Visual Mismatch Ne...

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International Conference on Information Science and control Engineering (ICISCE)

作者： Sijie Zhou Jing Jin Yu Zhang Xingyu Wang Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China

ISBN: (纸本)9781467368513

This paper presents a brain-computer interface (BCI) in which the face paradigm was optimized for the visual mismatch negativity (MMN). There were 12 cells in a LCD monitor. A single letter was at the bottom of each cell. In the new paradigm, a color face appeared above each of the 12 cells randomly while the gray faces appeared in others 11 cells. A traditional face paradigm with single character pattern was compared. Three healthy subjects participated in the experiment. Results showed that the new paradigm elicited larger N200 and N400 components than traditional face paradigm and had better performance in online session. The results demonstrated the advantages of the new paradigm in our P300 speller system.

关键词： Face Frequency modulation Visualization Accuracy Brain-computer interfaces Electroencephalography Electrodes

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On-demand state estimation with sampling time skew in power systems

On-demand state estimation with sampling time skew in power ...

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Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： Xinjun Ma Chensheng Liu Jing Wu Chengnian Long Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China

ISBN: (纸本)9781479958306

With new dynamics and uncertainties in today's power grids, traditional fixed-interval State Estimation (SE) may be unable to track the variability and monitor the power grid effectively. This paper presents a new architecture to transfer data and execute SE on demand. A list of situations are summarized to direct the SE-demand generator in system control center. As SCADA and PMU measurements are co-exist in realistic power systems, time skew problem is inevitable. To mitigate the influence of time skew, a state estimator based on time skew oriented weight adaptation is considered. In each SE circle, the weights assigned to the measurements not only correspond to their noise, but also the time offsets relative to the SE-demand point. Numerical examples demonstrate the improved accuracy of our estimator compared with the conventional hybrid SE when measurements time skew is present.

关键词： Phasor measurement units Time measurement Measurement uncertainty Current measurement Computer architecture State estimation Uncertainty

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Corrigendum to “Research on data reconciliation based on generalized T distribution with historical data” [Neurocomputing 175PA (2015) 808–815]

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Neurocomputing 2016年 177卷 670-670页

作者： Shengxi Wu Qiang Ye Cheng Chen Xingsheng Gu Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology No. 130 of Meilong Road Shanghai 200237 China

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Neural-observer-based adaptive control for stochastic nonlinear time-delay systems with unknown control directions 34

Neural-observer-based adaptive control for stochastic nonlin...

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34th Chinese control Conference, CCC 2015

作者： Zhaoxu, Yu Shugang, Li Fangfei, Li Key Laboratory of Advanced Control and Optimization for Chemical Process of Ministry of Education East China University of Science and Technology Shanghai China Department of Industrial Engineering and Logistics Shanghai Jiao Tong University Shanghai China Department of Mathematics East China University of Science and Technology Shanghai China

ISBN: (纸本)9789881563897

The problem of output feedback adaptive stabilization is addressed for a class of stochastic nonlinear systems with unknown time-varying delays and unknown control directions in this paper. Firstly, the unknown control coefficients are lumped together by using a linear state transformation, and the original system is transformed into a new system for which control design becomes feasible. Then, after the design of a novel neural observer, an output feedback adaptive neural network (NN) controller is developed for such systems by combining the Dynamic Surface control (DSC) technique, the Nussbaum gain function (NGF) method and the Lyapunov-Krasovskii method. The proposed controller ensures that all signals in the closed-loop systems are bounded in probability. Finally, a simulation example is given to verify the effectiveness and applicability of the proposed control design. © 2015 Technical Committee on control Theory, Chinese Association of Automation.

关键词： neural network (NN) output feedback Stochastic systems time-varying delay unknown control direction

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Effect of effluent organic matter on ozonation of bezafibrate

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Frontiers of Environmental Science & Engineering 2015年第6期9卷 962-969页

作者： Huan HE Qian SUI Shuguang LU Wentao ZHAO Zhaofu QIU Gang YU State Environmental Protection Key Laboratory of Environmental Risk Assessment and Control on Chemical Process School of Resources and Environmental Engineering East China University of Science and Technology Shanghai 200237 China State Key Laboratory of Pollution Control and Resource Reuse College of Environmental Science and Engineering Tongji University Shanghai 200092 China School of Environment THU - VEOLIA Joint Research Center for Advanced Environmental Technology Tsinghua University Beijing 100084 China

The influence of three effluent organic matter （EfOM） model compounds, humic acid （HA）, bovine serum albumin （BSA）, and sodium alginate （AGS）, on the ozonation ofbezafibrate （BF）, a typical pharmaceutical and personal care product （PPCP）, was investigated. The results show that ozonation efficiently removed BF from aqueous solution with removal efficiencies 〉 95% within 8 min for all conditions. The reaction rate of BF decreased with increasing model compounds concentrations and the influence was more pronounced for HA and BSA, while less pronounced for AGS. Although BF concentration was significantly reduced, the degree of mineralization achieved was only approximately 11%. The addition of HA and BSA improved the mineralization of the solution, while the influence of AGS was minor. The acute toxicity of BF solution during ozonation was determined using the Luminescent bacteria test, and the toxicity exhibited an initial increase and a successive reduction. An overall decreased acute toxicity was observed with an increase of HA. The presence of BSA increased the formation rate of toxicity intermediates and resulted in inhibition peak forward.

关键词： ozonation bezafibrate acute toxicity humicacid bovine serum albumin sodium alginate

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Majorization Theory in Sensor Scheduling

Majorization Theory in Sensor Scheduling

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IEEE Annual Conference on Decision and control

作者： Chao Yang Wen Yang Hongbo Shi Key Laboratory of Advanced Control and Optimization for Chemical Processes Dept. of Automation East China University of Science and Technology Shanghai China 200237

ISBN: (纸本)9781479978878

This paper presents that the majorization theory plays an essential role in a class of sensor scheduling problems, whose solutions all have periodic or uniformly distributed patterns. This paper revisits the problem of communication time scheduling for a single sensor with local computation capability, and strengthens its original result by the majorization theory. The scheduling for a single normal sensor in a general-order system is also studied, and the optimal schedules for minimizing the upper bound of the objective function is provided. Examples are provided at the end.

关键词： Sensors Objective function Reinforcement Optimal scheduling

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Soft measurement for component content based on adaptive model of Pr/Nd color features

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Chinese Journal of chemical Engineering 2015年第12期23卷 1981-1986页

作者：陆荣秀杨辉 School of Mechanical & Electrical Engineering Nanchang University School of Electrical and Electronic Engineering East China Jiaotong University Key Laboratory of Advanced Control & Optimization of Jiangxi Province

For measurement of component content in the extraction and separation process of praseodymium/neodymium(Pr/Nd), a soft measurement method was proposed based on modeling of ion color features, which is suitable for fast estimation of component content in production field. Feature analysis on images of the solution is conducted,which are captured from Pr/Nd extraction/separation field. H/S components in the HSI color space are selected as model inputs, so as to establish the least squares support vector machine(LSSVM) model for Nd(Pr) content,while the model parameters are determined with the GA algorithm. To improve the adaptability of the model,the adaptive iteration algorithm is used to correct parameters of the LSSVM model, on the basis of model correction strategy and new sample data. Using the field data collected from rare earth extraction production, predictive methods for component content and comparisons are given. The results indicate that the proposed method presents good adaptability and high prediction precision, so it is applicable to the fast detection of element content in the rare earth extraction.

关键词： Pr/Nd extraction Color feature Component content Adaptive iterative least squares support vector machine Real-time correction

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