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The 24th Chinese control and Decision Conference (第24届中国控制与决策学术年会 2012 CCDC)

作者： Lijing Meng Jing Jin Xingyu Wang Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of EducationSc Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education S

A navigation system based on P300 brain computer interface system (BCIs) and steady-state visual evoked potentials (SSVEP) BCIs respectively was designed in this paper. In the experiment, subjects were required to move a ball on the computer screen to the target position by P300 BCI system and SSVEP BCI system. Bayesian linear discriminant analysis (BLDA) is used to detect P300 potentials and canonical correlation analysis (CCA) is used to detect SSVEP. The aim of this paper is to show the drawbacks and advantages of these two BCIs, when they were used in navigation task. The online experimental results show that P300 BCIs is more robust for subjects compared to SSVEP BCIs.

关键词： P300 Steady-State Visual Evoked Potentials (SSVEP) Brain Computer Interface Navigation System

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Multimode Process Monitoring Based on Fuzzy C-Means in Locality Preserving Projection Subspace

Multimode Process Monitoring Based on Fuzzy C-Means in Local...

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第23届过程控制会议

作者： Xiang Xie Hongbo Shi Key Laboratory of Advanced Control and Optimization for Chemical Processes(East China University of Key Laboratory of Advanced Control and Optimization for Chemical Processes (East China University of

For complex industrial processes with multiple operating conditions, it is important to develop effective monitoring algorithms to ensure the safety of the producing processes. This paper proposes a novel monitoring strategy based on fuzzy c-means (FCM). First, the high dimensional historical data are transferred to a low dimensional subspace space by locality preserving projection (LPP). Then the scores in the novel subspace are classified into several overlapped clusters, each representing an operating mode. After that, the distance statistics of each cluster are integrated though the membership values into a novel BID monitoring index. The efficiency and effectiveness of the proposed method are validated though the Tennessee Eastman (TE) benchmark process.

关键词： Multimode process monitoring fuzzy c-means locality preserving projection integrated monitoring index Tennessee Eastman process.

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Modified Self-adaptive Immune Genetic Algorithm for Optimization of Combustion Side Reaction of p-Xylene Oxidation

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Chinese Journal of chemical Engineering 2012年第6期20卷 1047-1052页

作者：陶莉莉孔祥东钟伟民钱锋 Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of EducationEast China University of Science and Technology Automation Institute East China University of Science and Technology

In recent years, immune genetic algorithm (IGA) is gaining popularity for finding the optimal solution for non-linear optimization problems in many engineering applications. However, IGA with deterministic mutation factor suffers from the problem of premature convergence. In this study, a modified self-adaptive immune genetic algorithm (MSIGA) with two memory bases, in which immune concepts are applied to determine the mutation parameters, is proposed to improve the searching ability of the algorithm and maintain population diversity. Performance comparisons with other well-known population-based iterative algorithms show that the proposed method converges quickly to the global optimum and overcomes premature problem. This algorithm is applied to optimize a feed forward neural network to measure the content of products in the combustion side reaction of p-xylene oxidation, and satisfactory results are obtained.

关键词： self-adaptive immune genetic algorithm artificial neural network measurement p-xylene oxidation process

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A Quantized H-infinity control for networked control systems with time-varying delay and multiple packet dropouts

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Kongzhi Lilun Yu Yingyong/control Theory and Applications 2013年第4期30卷 469-474页

作者： Yan, Huai-Cheng Su, Zhen-Zhen Zhang, Hao Yang, Fu-Wen Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology Shanghai 200237 China School of Information Science and Engineering East China University of Science and Technology Shanghai 200237 China Department of Control Science and Engineering Tongji University Shanghai 200092 China

This paper is concerned with the problem of quantized H-infinity control for networked control systems (NCSs) with time-varying delay and multiple packet dropouts. The packet dropouts in both the measurement channel and the control channel are considered simultaneously, and are modeled as stochastic variables with Bernoulli random binary distribution. Both the control input and measurement output are quantized before transmission and the quantization errors are described as sector-bound uncertainties. Sufficient conditions for the existence of an observer-based, delay-dependent controller are developed to ensure the exponentially mean-square stability of the closed-loop system and to achieve the optimal H-infinity disturbance attenuation. A numerical example is given to illustrate the effectiveness of the proposed method.

关键词： Networked control systems

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Chaotic particle swarm optimization algorithm with harmony search for industrial applications

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Qinghua Daxue Xuebao/Journal of Tsinghua University 2012年第3期52卷 325-330页

作者： Du, Wenli Zhang, Hailong Qian, Feng Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology Shanghai 200237 China

Particle swarm optimization methods for optimization problems tend to search premature solutions. This paper presents an improved particle swarm optimization algorithm merging chaotic and harmony searches. The chaos particle swarm optimization method is stable, robust and adaptable. The harmony search algorithm is a meta heuristic algorithm for simulating band tuning to obtain an optimal harmonized process with a global search. Results for four standard test functions show that this chaos particle swarm optimization algorithm with a harmony search (CPSO-HS) can jump out of local optimums with fast convergence and good stability. This algorithm has been successfully applied to parameter estimates for a heavy oil thermal cracking model.

关键词： Particle swarm optimization (PSO)

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Prediction of Cracking Gas Compressor Performance and Its Application in Process Optimization

Prediction of Cracking Gas Compressor Performance and Its Ap...

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第23届过程控制会议

作者： Shaojun Li Feng Li Key Laboratory of Advanced Control and Optimization for Chemical Processes (East China University of

In ethylene plant, charge gas compressor is one of the most important units. The charge gas compressor usually is a centrifugal compressor. The information of centrifugal compressors’ performance is not applicable to all of the conditions, which restricts the operation optimization of the compressor. To solve this problem, a tri-layer BP neural network was introduced to model the performance of compressor by using the data provided by manufacturers. The input data of the model in other conditions should be corrected according to the similar theory. At last, the method was used to optimize the system of charge gas compressor by embedding compressor performance model into the ASPEN PLUS model of compressor. The result shows that it is an effective method to optimize the compressor system.

关键词： Compressor characteristic curve neural-network modeling

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Robust iterative learning control with quadratic performance index

Robust iterative learning control with quadratic performance...

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作者： Xu, Zuhua Zhao, Jun Yang, Yi Shao, Zhijiang Gao, Furong National Laboratory of Industrial Control Technology Department of Control Science and Engineering Zhejiang University Hangzhou 310027 China Key Laboratory of Advanced Control and Optimization for Chemical Processes Shanghai 200237 China Department of Chemical and Biomolecular Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong

In this paper, a robust iterative learning control (ILC) designed through a linear matrix inequality (LMI) approach is proposed first, based on the worst-case performance index with ellipsoidal uncertainty and polytopic uncertainty, respectively. Since the design based on worst-case performance index is too conservative, a novel ILC design based on nominal performance index is further proposed, and its robust convergence properties are proven. The latter can give better performance when the nominal model is close to the true process. Simulations have demonstrated the effectiveness and excellent performance of the proposed methods. © 2011 American chemical Society.

关键词： Iterative methods

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Regularized CSP with Fisher's criterion to improve classification of single-trial ERPs for BCI

Regularized CSP with Fisher's criterion to improve classific...

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2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2012

作者： Zhang, Yu Zhao, Qibin Zhou, Guoxu Wang, Xingyu Cichocki, Andrzej Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai 200237 China Laboratory for Advanced Brain Signal Processing Brain Science Institute RIKEN Wako-shi Saitama 351-0198 Japan

ISBN: (纸本)9781467300223

A brain-computer interface (BCI) based on the combination of oddball paradigm and face perception has been introduced. Such BCI mainly exploits three event-related potential (ERP) components, namely vertex positive potential (VPP), N170 and P300 instead of only P300. With different temporal and spatial distributions of the three ERP components, a regularized common spatial pattern (CSP) with Fisher's criterion (FC), named FCCSP, is proposed to extract the most discriminative features for single trial classification of ERP components. With linear discriminant analysis (LDA) classifier, the proposed FCCSP spatial filtering method yields an average classification accuracy of 95.4% on seven healthy subjects for single-trial ERP components, which outperforms no spatial filtering, the CSP and the FC. © 2012 IEEE.

关键词： Brain computer interface

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Minimum time dynamic optimization using double-layer optimization algorithm

Minimum time dynamic optimization using double-layer optimiz...

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World Congress on Intelligent control and Automation (WCICA)

作者： Xuan Guo Wenli Du Rongbin Qi Feng Qian Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education 200237 China

A double-layer optimization algorithm (DLOA) was proposed to solve the minimum time dynamic optimization problem. The first step of DLOA was to discrete time region and control region. The inner optimization is to construct optimal control problem with free final states. Differential evolution algorithm is used to find the optimal solution in given terminal time, then the optimization results was compared with the threshold set. In the outer, DLOA calculated the time range of next iteration according to the inner calculation. When applied to typical minimum time dynamic optimization problem, DLOA demonstrated a competitive optimal searching ability and more accurate optimization results. DLOA could solve the optimization problem with local optimum and applied to models without gradient information.

关键词： Optimization Heuristic algorithms chemical engineering Computers Educational institutions chemicals Dynamic programming

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Multiple models adaptive control based on cluster-optimization for a class of nonlinear system

Multiple models adaptive control based on cluster-optimizati...

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10th World Congress on Intelligent control and Automation, WCICA 2012

作者： Huang, Miao Wang, Zhenlei Qian, Feng Wang, Xin Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Shanghai 200237 China Center of Electrical and Electronic Technology Shanghai Jiao Tong University Shanghai 200237 China

ISBN: (纸本)9781467313988

For a class of nonlinear discrete time system with fast time-varying or jumping parameters, a multiple models adaptive controller (MMAC) based on cluster-optimization is proposed. Based on the input-output data, the sample data are classified into several clusters by the fuzzy kernel clustering adaptive algorithm. Then the local models can be constructed corresponding clusters by the least square method. To improve the transient response during the change of the working points, besides the distance, the directional derivative of system is computed also. It is utilized to identify the system trend of changing working point. Before the changing occurs, new weighted models are developed by the corresponding local models, indicated by the system directional derivative. Meanwhile the distance between the data and the centre of clusters are used to find the weighted coefficients. So a better approach ability can be got than that designed only by the distance. The simulation results show that the proposed controller is superior to that of the conventional multiple models controller. © 2012 IEEE.

关键词： Transient analysis

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