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35th Chinese control Conference, CCC 2016

作者： Zhang, Hao Yan, Huaicheng Shi, Hongbo Key Laboratory of Advanced Control and Optimization for Chemical Process of Ministry of Education Shanghai200237 China Department of Control Science and Engineering Tongji University Shanghai200092 China

ISBN: (纸本)9789881563910

This paper is concerned with the L2- L∞state estimation problem for discrete-time delay neural networks with missing measurements and randomly occurring sensor linearity. The phenomena of missing measurements and randomly occurring sensor linearity are constructed with two sequences of random variables, which obey the partial Bernoulli distribution. A sufficient condition is firstly given such that the augmented filtering error system is stochastically stable with a guaranteed optimal L2- L∞performance by solving a set of linear matrix inequalities(LMIs). Finally, a simulation example is given to show the effectiveness of the proposed method. © 2016 TCCT.

关键词： discrete-time delayed neural networks L2 - L∞ state estimation missing measurements randomly occurring sensor linearity

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Solving chemical dynamic optimization problems with ranking-based differential evolution algorithms

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Chinese Journal of chemical Engineering 2016年第11期24卷 1600-1608页

作者： Xu Chen Wenli Du Feng Qian Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai 200237 China School of Electrical and Information Engineering Jiangsu University Zhenjiang 212013 China

Dynamic optimization problems(DOPs) described by differential equations are often encountered in chemical engineering. Deterministic techniques based on mathematic programming become invalid when the models are non-differentiable or explicit mathematical descriptions do not exist. Recently, evolutionary algorithms are gaining popularity for DOPs as they can be used as robust alternatives when the deterministic techniques are invalid. In this article, a technology named ranking-based mutation operator(RMO) is presented to enhance the previous differential evolution(DE) algorithms to solve DOPs using control vector parameterization. In the RMO, better individuals have higher probabilities to produce offspring, which is helpful for the performance enhancement of DE algorithms. Three DE-RMO algorithms are designed by incorporating the RMO. The three DE-RMO algorithms and their three original DE algorithms are applied to solve four constrained DOPs from the literature. Our simulation results indicate that DE-RMO algorithms exhibit better performance than previous non-ranking DE algorithms and other four evolutionary algorithms.

关键词： Dynamic optimization Differential evolution Ranking-based mutation operator control vector parameterization

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GPU-based coevolutionary particle swarm optimization 36

GPU-based coevolutionary particle swarm optimization

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第36届中国控制会议

作者： ZHAO Liang Zhu Yanxing ZHANG Jianyu Ye Zhencheng Key Laboratory of Advanced Control and Optimization for Chemical Process(Ministry of Education) East China University of Science and Technology Ethylene Plant of Jilin Petrochemical Company Ltd.PetroChina

ISBN: (纸本)9781538629185

Coevolutionary particle swarm optimization（CPSO） algorithm has been investigated and applied in the real world *** tackling the large-scale and complex real time optimization problems,the running time of CPSO algorithm is a *** this paper,Graphics processing Unit（GPU） is introduced to provide speedup in order to meet the real time *** CPSO algorithm has been implemented on GPU concurrently using the CUDA *** performance and run time of CPU-based and GPU-based CPSO algorithms are compared in *** experiment result shows that the GPU-based parallel computation mode can shorten the run time of CPSO algorithm apparently.

关键词： GPU CUDA Particle swarm optimization Coevolutionary particle swarm optimization

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Fuzzy H∞ Filtering for Nonlinear Networked Systems with Multiple Stochastic Time-Varying Delays, Packet Dropouts and Sensor Saturation 34

Fuzzy H∞ Filtering for Nonlinear Networked Systems with Mul...

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第三十四届中国控制会议

作者： XU Xiaoli YAN Huaicheng ZHANG Hao SHI Hongbo Key Laboratory of Advanced Control and Optimization for Chemical Process of Ministry of Education School of information Science and Engineering East China University of Science and Technology Key Laboratory of Embedded System and Service Computing Ministry of Education

ISBN: (纸本)9781467374439

This paper investigates the problem of H∞ filter design for a class of nonlinear networked system based on T-S fuzzy model. Multiple stochastic time-varying delays and some incomplete information are considered simultaneously. Incomplete information includes randomly occurring sensor saturation and packet dropouts. Stochastic time-varying delays are depicted as a sequence of stochastic and independent variables, which take values on 0 and 1. Two sets of Bernoulli distributed white noises are introduced to describe randomly occurring sensor saturation and packet dropouts. System conservatism is reduced due to introduce an approach of piecewise quadratic Lyapunov function. By solving a set of linear matrix inequalities(LMIs), the filter parameters are obtained. Finally, a simulation example is provided to illustrate the effectiveness of the proposed filter design approach.

关键词： H ∞ filtering,multiple time-varying delays,randomly occurring sensor saturation,packet dropouts,piecewise quadratic Lyapunov function

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Adaptive neural output feedback control for stochastic nonlinear systems with unknown control directions

Adaptive neural output feedback control for stochastic nonli...

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第三十二届中国控制会议

作者： Yu Zhaoxu Li Shugang 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

ISBN: (纸本)9781479900305

This paper focuses on the problem of adaptive output feedback stabilization for a class of stochastic nonlinear system with unknown control *** using a linear state transformation,the unknown control coefficients are lumped together, such that the original system is transformed to a new system for which control design becomes *** employing the inputdriven observer,a novel adaptive neural network(NN) output-feedback controller which only contains one adaptive parameter is developed for such systems by using backstepping technique and NNs' *** proposed control design guarantees that all the signals in the closed-loop systems are 4-moment semi-globally uniformly ultimately bounded.

关键词： stochastic systems output-feedback neural network(NN) Nussbaum function backstepping

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Multi-objective optimization of p-xylene oxidation process using an improved self-adaptive differential evolution algorithm

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Chinese Journal of chemical Engineering 2017年第8期25卷 983-991页

作者： Lili Tao Bin Xu Zhihua Hu Weimin Zhong College of Engineering Shanghai Polytechnic University School of Mechanical Engineering Shanghai University of Engineering Science Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology

The rise in the use of global polyester fiber contributed to strong demand of the Terephthalic acid （TPA）. The liquid-phase catalytic oxidation of p-xylene （PX） to TPA is regarded as a critical and efficient chemical process in industry [ 1 ]. PX oxidation reaction involves many complex side reactions, among which acetic acid combustion and PX combustion are the most important. As the target product of this oxidation process, the quality and yield of TPA are of great concern. However, the improvement of the qualified product yield can bring about the high energy consumption, which means that the economic objectives of this process cannot be achieved simulta- neously because the two objectives are in conflict with each other. In this paper, an improved self-adaptive multi-objective differential evolution algorithm was proposed to handle the multi-objective optimization prob- lems. The immune concept is introduced to the self-adaptive multi-objective differential evolution algorithm （SADE） to strengthen the local search ability and optimization accuracy. The proposed algorithm is successfully tested on several benchmark test problems, and the performance measures such as convergence and divergence metrics are calculated. Subsequently, the multi-objective optimization of an industrial PX oxidation process is carried out using the proposed immune self-adaptive multi-objective differential evolution algorithm （ISADE）. optimization results indicate that application oflSADE can greatly improve the yield of TPA with low combustion loss without degenerating TA quality.

关键词： p-Xylene oxidation Operation condition optimization Multi-objective optimization Self-adaptive differential evolution

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process monitoring of multimode processes using kernel independent component analysis

Process monitoring of multimode processes using kernel indep...

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8th International Symposium on advanced control of chemical processes, ADCHEM 2012

作者： Zhang, Yingwei An, Jiayu Ma, Chi State Key Laboratory of Synthetical Automation of Process Industry Northeastern University Shenyang Liaoning 110819 China Key Laboratory of Advanced Control and Optimization for Chemical Processes Shanghai 200237 China

ISBN: (纸本)9783902823052

In the paper, a new process monitoring approach is proposed for handling the multimode problem in the industrial processes. The original space can be separated into two different parts, which are the common part and the specific part. There are both similarity and dissimilarity in the underlying correlations of different modes, which play different roles in the industrial processes. Because the industrial processes have the non-Gaussian and nonlinear characteristics, modified kernel independent component analysis is used to monitor the multimode processes in this paper. The global multimode basis vector and the multimode sub-basis vector are obtained based on the modified KICA. Then, the common part and specific part in one mode are respectively analyzed. The proposed method is applied to monitor the continuous annealing process. The proposed approach effectively captures the non-Gaussian and nonlinear relationship in different modes in the industrial processes. © 2012 IFAC.

关键词： process monitoring

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Prediction Consensus-Based Distributed Kalman Filtering with Packet Loss 36

Prediction Consensus-Based Distributed Kalman Filtering with...

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第36届中国控制会议

作者： Sha Fan Huaicheng Yan Hao Zhang Mengling Wang Xisheng Zhan Key Laboratory of Advanced Control and Optimization for Chemical Process of Ministry of Education East China University of Science and Technology College of Mechatronics and Control Engineering Hubei Normal University Department of Control Science and Engineering Tongji University

ISBN: (纸本)9781538629185

This paper is concerned with the problem of distributed state estimation for linear discrete system with packet loss.A state prediction consensus mechanism is introduced to promote the estimation *** applying matrix theory,a sufficient condition for the convergence of the estimation error system is ***,a numerical example is given to show the algorithm proposed in this paper has better consensus performance compared with a classical Kalman consensus filtering（KCF） by some simulations in real and packet-dropping cases,***,the influence of consensus coefficient and prediction consensus coefficient on the estimation error covariance are presented by simulation.

关键词： Distributed state estimation Packet loss Kalman consensus filtering(KCF) State prediction consensus mechanism

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An energy efficient clustering and relay node selection algorithm in wireless sensor networks 35

An energy efficient clustering and relay node selection algo...

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第35届中国控制会议

作者： HU Yuan JIA Tinggang NIU Yugang Key Laboratory of Advanced Control and Optimization for Chemical Process of Ministry of Education (East China University of Science and Technology) Automation Division of Shanghai Electric Group Co. Ltd.

ISBN: (纸本)9781509009107

Wireless sensor networks utilize large numbers of sensors to perform sensing and forwarding tasks in the monitoring *** to the resources constraints of sensors and the energy efficiency QoS requirement,prolonging the network lifetime is a major concern in *** is an efficient approach to reduce energy consumption since it can reduce data transmission of the *** most traditional clustering protocols,the cluster heads（CHs） aggregate the coming data from their cluster members（CMs） and send the aggregated data to the remote sink node directly or visa other ***,assigning a node to serve as CHs and relay nodes simultaneously may result in unbalanced energy *** energy efficient overlapping clustering and relay node selection algorithm is proposed,which selects different nodes as CHs and relay node to attain the balancing of energy consumption of the network.A direction fitness metric is designed to select the proper boundary nodes in the overlapping area to serve as relay *** results show that the proposed algorithm significantly prolongs the network lifetime.

关键词： Wireless sensor networks energy efficiency clustering relay nodes multi-hop

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Neural network-based source tracking of chemical leaks with obstacles

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Chinese Journal of chemical Engineering 2021年第5期34卷 211-220页

作者： Qiaoyi Xu Wenli Du Jinjin Xu Jikai Dong Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of EducationEast China University of Science and TechnologyShanghai 200237China Shanghai Institute of Intelligent Science and Technology Tongji UniversityShanghai 200092China

The leakage of hazardous gases poses a significant threat to public security and causes environmental *** effective and accurate source term estimation(STE)is necessary when a leakage accident ***,most research generally assumes that no obstacles exist near the leak source,which is inappropriate in practical *** solve this problem,we propose two different frameworks to emphasize STE with obstacles based on artificial neural network(ANN)and convolutional neural network(CNN).Firstly,we build a CFD model to simulate the gas diffusion in obstacle scenarios and construct a benchmark ***,we define the structure of ANN by searching,then predict the concentration distribution of gas using the searched model,and optimize source term parameters by particle swarm optimization(PSO)with well-performed cost ***,we propose a one-step STE method based on CNN,which establishes a link between the concentration distribution and the location of ***,we propose a novel data processing method to process sensor data,which maps the concentration information into feature *** comprehensive experiments illustrate the performance and efficiency of the proposed methods.

关键词： Obstacle optimization Neural networks Feature extraction Source term estimation Computational fluid dynamics (CFD)

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