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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： Wenyong Wang Ziqiang Sun Huanlai Cao Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education E Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education E

It is known that the characteristic of Dissolved Oxygen (DO) control system is non-linear, variability and uncertainty, etc. This paper presents Genetic Algorithm (GA) to optimize the fuzzy controller, which adjusts the membership function of fuzzy controller parameters by GA. The results show that the optimized fuzzy controller based on GA is well up for the shortage of the traditional fuzzy controller. With this controller we can get target such as: obtaining better control result, improving the efficiency of sewage treatment, having significant effect for stable and secure running and reducing energy consumption.

关键词： Sewage treatment Dissolved oxygen Fuzzy control Genetic algorithm

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Consensus based distributed estimation with sensor selection strategies in energy constrained wireless sensor networks

Consensus based distributed estimation with sensor selection...

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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： Wen Yang 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

In the applications of wireless sensor networks(WSNs), sensor energy saving is essential to increase the life of sensor networks. In this paper, we consider the problem of performing consensus based estimation over energy constrained WSNs, in which energy is conserved by selecting only a subset of sensors to observe the state of the dynamical system at each time step. First, we derive an sufficient condition for the convergence of the state estimation covariance. Second, we propose a sensor selection strategy to schedule sensors to measure the system state for next step with the goal of minimizing the state estimation error subject to sensor energy constraint. Finally, we provide some numerical examples to illustrate the performance and effectiveness of the proposal strategy.

关键词： consensus problem Kalman filter state estimation convex optimization

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A Comparison of Navigation System Based on P300 BCI and SSVEP BCI

A Comparison of Navigation System Based on P300 BCI and SSVE...

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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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Parameter estimation of industrial PET reactor using multi-objective kernel density estimation of distribution algorithm

Parameter estimation of industrial PET reactor using multi-o...

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作者： Luo, Na Qian, Feng Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Ministry of Education Shanghai 200237 China State-Key Laboratory of Chemical Engineering East China University of Science and Technology Shanghai 200237 China

With a multitude of reaction pathways, poly (ethylene-terephthalate) (PET) polymerization of industrial practice is complex, and the quality of PET is normally described in terms of several experimentally measured indices. In this paper, parameters estimation of industrial PET reactors is presented as a multi-objective problem to make the mathematic model consistent with the actual industrial process. Considering the interrelation among parameters and the failure of general optimization algorithms, a new multi-objective estimation of distribution algorithm is proposed. Kernel density estimation is used to make the new population more suitable for real-life problems instead of Gaussian model during the evolution of the algorithm. Strategies including selection of kernel width, sampling method and Pareto domination selection are used to explore and exploit the search space more efficiently. With industrial operating data identified in steady state and eliminated from gross error, kinetic parameters are estimated by minimizing carboxyl end group concentration and degree of polymerization simultaneously using the proposed algorithm. The simulation results show that the model with estimated parameters has better predictive performance compared with the experimental parameters. Copyright © 2011 Curtin University of Technology and John Wiley & Sons, Ltd. Copyright © 2011 Curtin University of Technology and John Wiley & Sons, Ltd.

关键词： Parameter estimation

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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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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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