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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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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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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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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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Dynamic Multi-objective Optimization of chemical processes Using Modified BareBones MOPSO Algorithm

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Journal of Donghua University(English Edition) 2014年第2期31卷 184-189页

作者：杜文莉王珊珊陈旭钱锋 Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of EducationEast China University of Science and Technology

Dynamic multi-objective optimization is a complex and difficult research topic of process systems engineering. In this paper,a modified multi-objective bare-bones particle swarm optimization( MOBBPSO) algorithm is proposed that takes advantage of a few parameters of bare-bones algorithm. To avoid premature convergence,Gaussian mutation is introduced; and an adaptive sampling distribution strategy is also used to improve the exploratory capability. Moreover, a circular crowded sorting approach is adopted to improve the uniformity of the population ***, by combining the algorithm with control vector parameterization,an approach is proposed to solve the dynamic optimization problems of chemical processes. It is proved that the new algorithm performs better compared with other classic multiobjective optimization algorithms through the results of solving three dynamic optimization problems.

关键词： dynamic multi-objective optimization bare-bones particle swarm optimization(PSO) algorithm chemical process

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Locust Behaved Particle Swarm Optimization Technique

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Journal of Donghua University(English Edition) 2014年第2期31卷 190-196页

作者：钟伟民谢雪勤梁毅罗娜张娟钱锋 Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of EducationEast China University of Science and Technology

The collective behavior of certain animals and insects has the characteristic of self-organization. The simple interactions among individuals can produce complex adaptive patterns at the level of the group. Recently,new scientific investigation pointed out that desert locusts show extreme phenotypic plasticity in transforming between the lonely phase and the swarming gregarious phase depending on the population density,which is controlled by a serotonin called 5-hydroxytryptamine( 5HT). In this paper,based on the mechanism of the locusts' collective behavior,a new particle swarm optimization technique called LBPSO is studied. The number of swarms is selfadaptively adjusted by the acquired outstanding particles coming from behind the previous global best solution. The swarm sizes are related to the corresponding serotonin 5HT,which is determined by the optimization parameters such as global best and iteration number. And each swarm adopts one of three rules below according to its density, generalized social evolution strategy, generalized cognition evolution strategy and the independent moving strategy. A comparative study of LBPSO,social particle swarm optimization( SPSO), improved SPSO and the standard particle swarm optimization( StdPSO) on their abilities of tracking optima is carried out. And the results under four static benchmark functions and a dynamic function generator moving peaks benchmark( MPB)show that LBPSO outperforms the other three functions in both static and dynamic landscapes due to the introduced locusts' collective behavior.

关键词： evolutionary algorithm particle swarm optimization(PSO) locust collective behavior

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Permanent-magnet synchronous motor velocity control based on second-order integral sliding mode control algorithm

Permanent-magnet synchronous motor velocity control based on...

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作者： Qi, Liang Bao, Sheng Shi, Hongbo Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China Central Academe Shanghai Electric Group Co. Ltd Shanghai China School of Mechatronic Engineering and Automation Shanghai University Shanghai China

In this paper, a novel second-order integral sliding mode control (SOSMC) algorithm is proposed to accomplish velocity control of the permanent-magnet synchronous motor (PMSM) so that the performance can be improved. An integral manifold is utilized to reduce the static error during the sliding mode movement phase to improve the control precision, and a new SOSMC law is achieved by a Lyapunov function approach so the system convergence is guaranteed. The presented method can not only eliminate the system chattering problem successfully but also improve the performance of the PMSM control system. Meanwhile, in order to solve the problem of the windup phenomenon of the PMSM control system, an anti-windup control method is proposed in the PMSM control system. The simulation experimental results are given to indicate that the proposed method is effective and can improve the performance of the PMSM control system such as fast response, high robustness and speed tracking precision, etc. © The Author(s) 2013..

关键词： Lyapunov functions

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A New Sample-Selection and Modeling Method Based on Near-Infrared Spectroscopy and Its Industrial Application

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Journal of Donghua University(English Edition) 2014年第2期31卷 207-211页

作者：贺凯迅程辉钱锋 Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of EducationEast China University of Science and Technology

Near-infrared( NIR) spectroscopy has been widely employed as a process analytical tool( PAT) in various fields; the most important reason for the use of this method is its ability to record spectra in real time to capture process properties. In quantitative online applications,the robustness of the established NIR model is often deteriorated by process condition variations,nonlinear of the properties or the high-dimensional of the NIR data set. To cope with such situation,a novel method based on principal component analysis( PCA) and artificial neural network( ANN) is proposed and a new sample-selection method is mentioned. The advantage of the presented approach is that it can select proper calibration samples and establish robust model effectively. The performance of the method was tested on a spectroscopic data set from a refinery process. Compared with traditional partial leastsquares( PLS),principal component regression( PCR) and several other modeling methods, the proposed approach was found to achieve good accuracy in the prediction of gasoline properties. An application of the proposed method is also reported.

关键词： gasoline blending near-infrared spectroscopy sample selection modeling method

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