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3rd International Workshop on advanced Computational Intelligence and Intelligent Informatics, IWACIII 2013

作者： Li, Bo Li, Zhi Luo, Na Zhong, Weimin East China University of Science and Technology Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education Shanghai200237 China

As the large amounts of operate data collected from Distributed control System (DCS) often contain outliers and these data are more complexity and nonlinearity. They can't be used directly to model, optimization and fault diagnosis. In fault diagnosis, the existence of outliers can destroy the covariance structure of Kernel Principal Component Analysis (KPCA), which cause the model can't really reflect the actual normal condition. In this paper, KPCA method is adopted to establish the normal statistic monitor model from the historical data which can represent the normal industrial operate condition. First, the outlier detection algorithm is used to eliminate outliers among normal work condition. Then the primary statistic model for fault diagnosis of the Squared Prediction Error (SPE) and T2 are established according to the data exclude outliers. The effectiveness of this fault diagnosis is demonstrated by the operate data of industrial Crude Terephthalic Acid (CTA) hydrogenation process, and simulation results show that this method can identify the industrial failure condition.

关键词： Failure analysis

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Locust behaved particle swarm optimization technique 3

Locust behaved particle swarm optimization technique

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3rd International Workshop on advanced Computational Intelligence and Intelligent Informatics, IWACIII 2013

作者： Zhong, Wei-Min Xie, Xue-Qin Liang, Yi Luo, Na Zhang, Juan Qian, Feng East China University of Science and Technology Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education Shanghai200237 China

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. 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 self-adaptively adjusted by the acquired outstanding particles coming from behind the previous global best solution. The swarm sizes are related to the corresponding serotonin 5-hydroxytryptamine which is determined by the optimization parameters such as global best, iteration number etc. 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, SPSO, Improved SPSO and the original PSO on their ability of tracking optima is carried out. And the results under four static benchmark functions and a dynamic function generator 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： Iterative methods

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An improved particle swarm optimization algorithm based on two kinds of chaotic maps 3

An improved particle swarm optimization algorithm based on t...

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3rd International Workshop on advanced Computational Intelligence and Intelligent Informatics, IWACIII 2013

作者： Li, MingHao Wang, XueWu Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai200237 China

Particle swarm optimization algorithm tends to fall into local optimum sometimes. To resolve this problem, an improved particle swarm optimization algorithm based on two kinds of different chaotic maps is proposed. The algorithm produces primitive chaotic particle swarm using the uniform distribution of Tent map and improves the diversity of search. When the particle swarm evolves to a local optimum, the chaotic mutation operator produced by Logistic map is adopted to form a disturbance on the swarm to drive particle swarm jump out of local optimum and approach the global optimum. Meanwhile, an adaptive inertia weight factor is introduced to adjust particles inertia weight factor adaptively, which forms a new 2-chaotic maps embedded adaptive particle swarm optimization algorithm (2-CMEAPSO) that can fully utilize the randomness and ergodicity of the chaotic motion to enhance optimization capability. Experimental results show that the improved algorithm can efficiently overcome the premature of standard particle swarm optimization algorithm. Besides, it has stronger global optimization ability and higher accuracy than the basic particle swarm optimization algorithm.

关键词： Global optimization

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A hybrid differential evolution algorithm integrated with particle swarm optimization 3

A hybrid differential evolution algorithm integrated with pa...

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3rd International Workshop on advanced Computational Intelligence and Intelligent Informatics, IWACIII 2013

作者： Fan, Qinqin Yan, Xuefeng Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology Shanghai200237 China

In order to implement self-adaptive control parameters, a hybrid differential evolution algorithm integrated with particle swarm optimization (PSODE) is proposed. In PSODE, control parameters are encoded to be a symbiotic individual of original individual, and each original individual has its own symbiotic individual. Differential evolution operators are applied to evolve the original population. And, PSO is applied to co-evolve the symbiotic population. Thus, with the evolution of the original population in PSODE, the symbiotic population is dynamically and self-adaptively adjusted and the real-time optimum control parameters are obtained. To illustrate the performance of PSODE, DE/rand/1, DE/best/1, DE/rand-to-best/1, DE/rand/2, DE/best/2, self-adaptive Pareto DE (SPDE), self-adaptive DE (SDE) and PSODE are applied to optimize 9 benchmark functions. The results show that the average performance of PSODE is the best.

关键词： Particle swarm optimization (PSO)

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Octane model based on principal component analysis and neural network by near-infrared spectroscopy and its application in online gasoline blending process 3

Octane model based on principal component analysis and neura...

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3rd International Workshop on advanced Computational Intelligence and Intelligent Informatics, IWACIII 2013

作者： He, Kaixun Cheng, Hui Qian, Feng Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai200237 China

For the gasoline pipeline blending process, recipe optimization system is greatly dependent on the near-infrared spectroscopy online analyzer, whose spectral model plays an important role in the measurement. The sepectral model's accuracy and adaptability directly affect the applicability of the entire online blending system. This paper studies how to establish model for gasoline octane number for the gasoline pipeline blending process with near-infrared spectroscopy online analyzer. It is proposed using principal component analysis (PCA) together with Artificial Neural Network (ANN) method to establish spectral-model for octane number. Multivariate linear regressions(MLR) and partial least squares (PLS) method have also been used to establish gasoline octane model for comparison purpose. The results show that the model established by PCA and ANN has strong anti-jamming capability and suitable for gasoline online blending application.

关键词： Principal component analysis

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A new co-evolutionary immune algorithm for flow shop with zero wait

A new co-evolutionary immune algorithm for flow shop with ze...

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5th FTRA International Conference on Computer Science and its Applications, CSA 2013

作者： Xu, Zhenhao Zhang, Shun Gu, Xingsheng Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai 200237 China

ISBN: (纸本)9783642416736

The flow shop scheduling problems with zero wait is considered as one of the most challenging problems in the field of scheduling. This paper deals with the problem considering the makespan minimization as the objective. In order to solve the corresponding model, a new co-evolutionary immune algorithm (NCIA) is proposed based on the '80/20' rule and a designed local search strategy. Moreover, a global crossover operator is presented to preserve excellent genes fragment and increase the diversity of population during the process of evolution. to confirm the performance of the proposed algorithm, in terms of solution quality, the algorithm is applied to various test instances of benchmark problems available in the literature. The computational results show that the proposed algorithm outperforms other algorithms. © 2014 Springer-Verlag Berlin Heidelberg.

关键词： Scheduling

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Optimal Leader Selection for Fast Consensus via Consensus Centrality 33

Optimal Leader Selection for Fast Consensus via Consensus Ce...

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第三十三届中国控制会议

作者： WANG Ying YANG Wen WANG Xiaofan Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of China Key Laboratory of Advanced Control and Optimization for Chemical Processes (East China University of Science and Technology) Ministry of Education

ISBN: (纸本)9781479947249

In real applications,network resources are generally *** by the significance of optimizing network,the consensus centrality index is proposed to quantify how fast a leader could guide all the nodes in a network to reach the desired *** the basis of complex networks,the relation of the consensus centrality distribution and the degree distribution in several model and real networks is ***,the influence of network structure on consensus performance is *** the network becoming more heterogeneous or denser,the maximum consensus centrality *** faster convergence speed can be achieved if the node with the maximum consensus centrality is selected leader.

关键词： Consensus centrality,Fast convergence,Leader selection,Frequency distribution,Pearson correlation coefficient.

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Fixed-time consensus for multi-agent systems under directed and switching interaction topology

Fixed-time consensus for multi-agent systems under directed ...

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2014 American control Conference, ACC 2014

作者： Zuo, Zongyu Yang, Wen Tie, Lin Meng, Deyuan Beijing 100191 China Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education Shanghai 200237 China Beijing 100191 China

ISBN: (纸本)9781479932726

In this paper, we address the fixed-time consensus problem for multi-agent systems in networks with directed and switching interaction topology. With the introduction of mirror operation, two global distributed nonlinear consensus protocols are constructed for each first-order agent under strongly connected information flow. The distinctive feature of this paper is to address the explicit bounds of the finite settling time for both protocols are independent of initial condition, which makes it possible for network consensus problems of a multi-agent team with guaranteed convergence time. Further, the second protocol is valid for the networks of multi-agents with switching topology provided that the sum of time intervals, in which the information flow is strongly connected, is larger than the estimated upper-bound for settling time. Finally, simulations are provided to demonstrate the performance and effectiveness of our theoretical results. © 2014 American Automatic control Council.

关键词： Multi agent systems

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Co-simulation Research of Autonomous Landing control System 33

Co-simulation Research of Autonomous Landing Control System

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第三十三届中国控制会议

作者： WANG Zhaoning WU Shengxi DUN Xiangming GU Xingsheng Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Research Institute of Robotics Shanghai Jiao Tong University

ISBN: (纸本)9781479947249

In this paper, a control simulation of the autonomous landing process of a Vertical Take-Off and Landing(VTVL) Reusable Launch Vehicle(RLV) is proposed and we consider the effects of the inner liquid propellant sloshing, elastic vibration, disturbance force, disturbance torque and other complex conditions in the virtual RLV model. On the basis of dynamics modeling of the RLV, we analyzed RLV's landing process. The landing control system was designed under certain conditions. Co-simulation Research was achieved by ADAMS and MATLAB/Simulink. The simulation results show that the control system performs well.

关键词： vertical take-off and landing autonomous landing reusable launch vehicle control system co-simulation research

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Adaptive Local Outlier Probability for Dynamic process Monitoring

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Chinese Journal of chemical Engineering 2014年第7期22卷 820-827页

作者：马玉鑫侍洪波王梦灵 Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology

Complex industrial processes often have multiple operating modes and present time-varying behavior. The data in one mode may follow specific Gaussian or non-Gaussian distributions. In this paper, a numerically efficient movingwindow local outlier probability algorithm is proposed, lies key feature is the capability to handle complex data distributions and incursive operating condition changes including slow dynamic variations and instant mode shifts. First, a two-step adaption approach is introduced and some designed updating rules are applied to keep the monitoring model up-to-date. Then, a semi-supervised monitoring strategy is developed with an updating switch rule to deal with mode changes. Based on local probability models, the algorithm has a superior ability in detecting faulty conditions and fast adapting to slow variations and new operating modes. Finally, the utility of the proposed method is demonstrated with a numerical example and a non-isothermal continuous stirred tank reactor.

关键词： Time-varying Complex data distribution Local outlier probability Multi-mode Fault detection

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