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Robust semi-global leader-following practical consensus of a group of linear systems with imperfect actuators

Science China(Information Sciences) 2017年第7期60卷 195-206页

作者： Liangren SHI Zhiyun ZHAO Zongli LIN Department of Automation Shanghai Jiao Tong University Key Laboratory of Advanced Control and Optimization for Chemical Processes(East China University of Science and Technology) Ministry of Education Charles L. Brown Department of Electrical and Computer Engineering University of Virginia

This paper considers the problem of semi-global leader-following consensus of a multi-agent system whose agent dynamics are represented by linear systems. The input output characteristics of the follower agent actuators, such as those of saturation and dead-zone, are imperfect, not precisely known, and subject to the effect of disturbances. Two consensus control algorithms, of the low-and-high gain feedback type and the low gain based variable structure control type, are proposed for solving the consensus problem. It is shown that both of these control algorithms achieve semi-global leader-following practical consensus in the presence of the imperfectness of the actuators when the communication topology among the follower agents is represented by a strongly connected and detailed balanced directed graph and the leader agent is a neighbor of at least one follower agent. The theoretical results are illustrated by numerical simulation.

关键词： consensus leader-following actuator saturation dead-zone low-and-high gain feedback low gain based variable structure control

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SSVEP recognition using multivariate linear regression for brain computer interface

SSVEP recognition using multivariate linear regression for b...

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IEEE International Conference on Computer and Communications, ICCC 2015

作者： Wang, Haiqiang Zhang, Yu Jin, Jing Wang, Xingyu Key Laboratory for Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology China

ISBN: (纸本)9781467381253

Until now, the canonical correlation analysis (CCA)-based method has been most widely applied to steady-state visual evoked potential (SSVEP). Artificial sine-cosine signals are used as the original references in the CCA method, which could hardly reflect the real SSVEP features buried in electroencephalogram (EEG). In this study, we use principal component analysis (PCA) to extract EEG features multivariate linear regression (MLR) is implemented on EEG and the specific sample labels. Experimental results show that the proposed MLR method outperformed other two competing methods for SSVEP recognition, especially in short time window. © 2015 IEEE.

关键词： Brain computer interface

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An agent-based service-oriented integration architecture for chemical process automation

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Chinese Journal of chemical Engineering 2015年第1期23卷 173-180页

作者：罗娜钟伟民万锋叶贞成钱锋 Key Laboratory of Advanced Control and Optimization for Chemical Processes (East China University of Science and Technology) Ministry of Education

In reality, traditional process control system built upon centralized and hierarchical structures presents a weak response to change and is easy to shut down by single failure. Aiming at these problems, a new agent-based service-oriented integration architecture was proposed for chemical process automation system. Web services were dynamically orchestrated on the internet and agent behaviors were built in them. Data analysis, model, optimization, control, fault diagnosis and so on were capsuled into different web services. Agents were used for service compositions by negotiation. A prototype system of poly(ethylene terephthalate) process automation was used as the case study to demonstrate the validation of the integration.

关键词： Automation system Integration Web services Service-oriented architecture Agent

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Strategy of changing cracking furnace feedstock based on improved group search optimization

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Chinese Journal of chemical Engineering 2015年第1期23卷 181-191页

作者：年笑宇王振雷钱锋 Key Laboratory of Advanced Control and Optimization for Chemical Processes (East China University of Science and Technology) Ministry of Education

The scheduling process of cracking furnace feedstock is important in an ethylene plant. In this paper it is described as a constraint optimization problem. The constraints consist of the cycle of operation, maximum tube metal temperature, process time of each feedstock, and flow rate. A modified group search optimizer is proposed to deal with the optimization problem. Double fitness values are defined for every group. First, the factor of penalty function should be changed adaptively by the ratio of feasible and general solutions. Second, the "excellent" infeasible solution should be retained to guide the search. Some benchmark functions are used to evaluate the new algorithm. Finally, the proposed algorithm is used to optimize the scheduling process of cracking furnace feedstock. And the optimizing result is obtained.

关键词： Cracking furnace Scheduling of feedstock Group search optimizer Adaptive penalty function Double fitness values

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Activated sludge model parameters calibration based on multi-objective PSO

Activated sludge model parameters calibration based on multi...

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International Conference on Environmental Engineering and Computer Application, ICEECA 2014

作者： Wang, R.R. Cao, L.L. Liu, M.D. Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology China

ISBN: (纸本)9781138028074

The model of an Orbal oxidation ditch activated sludge process was set up based on ASM3 and Takacs’s double index settlement rate of secondary sedimentation tank model in this paper. According to the condition of the multi-objective output simulation model, multi-objective particle swarm optimization algorithm (MOPSO) is used to calibrate part of the high sensitivity parameters in the activated sludge process model. Simulation results show that the effectiveness of MOPSO for parameter calibration is obvious, which can further improve the accuracy of the model. © 2015 Taylor & Francis Group, London.

关键词： Activated sludge process

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High dimension feature extraction based visualized SOM fault diagnosis method and its application in p-xylene oxidation process

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Chinese Journal of chemical Engineering 2015年第9期23卷 1509-1517页

作者：田颖杜文莉钱锋 Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology

Purified terephthalic acid(PTA) is an important chemical raw material. P-xylene(PX) is transformed to terephthalic acid(TA) through oxidation process and TA is refined to produce PTA. The PX oxidation reaction is a complex process involving three-phase reaction of gas, liquid and solid. To monitor the process and to improve the product quality, as well as to visualize the fault type clearly, a fault diagnosis method based on selforganizing map(SOM) and high dimensional feature extraction method, local tangent space alignment(LTSA),is proposed. In this method, LTSA can reduce the dimension and keep the topology information simultaneously,and SOM distinguishes various states on the output map. Monitoring results of PX oxidation reaction process indicate that the LTSA–SOM can well detect and visualize the fault type.

关键词： Self-organizing map Local tangent space alignment Fault diagnosis Visualization P-xylene oxidation

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Model Reduction for Switched Genetic Regulatory Networks with Time-Varying Delays 36

Model Reduction for Switched Genetic Regulatory Networks wit...

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第36届中国控制会议

作者： Mengqi Xue Yang Tang Ligang Wu Feng Qian Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Research Institute of Intelligent Control and Systems Harbin Institute of Technology

ISBN: (纸本)9781538629185

The model reduction problem is studied in this work for the switched genetic regulatory networks（GRNs） with timevarying delays. The attention is focused on constructing a reduced-order model to approximate the considered high-order GRNs under that the switching signal is subject to some certain constraints, such that the error system between the original system and the reduced-order one is exponentially stable with a weighted H∞ performance. By utilizing the bounding technique as well as the dwell time method, the stability conditions and the weighted H performance are established for the error system. Then, the solvability conditions for the reduced-order models for the GRNs are also established by using the projection method. Finally,numerical simulation is presented to illustrate the effectiveness of the proposed method.

关键词： Switched System Genetic Regulatory Networks Model Reduction Time-Varying Delays

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Soft Sensor for Ammonia Concentration at the Ammonia Converter Outlet Based on an Improved Group Search optimization and BP Neural Network

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Chinese Journal of chemical Engineering 2012年第6期20卷 1184-1190页

作者：阎兴頔杨文马贺贺侍洪波 Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology

The ammonia synthesis reactor is the core unit in the whole ammonia synthesis production. The ammonia concentration at the ammonia converter outlet is a significant process variable, which reflects directly the production efficiency. However, it is hard to be measured reliably online in real applications. In this paper, a soft sensor based on BP neural network (BPNN) is applied to estimate the ammonia concentration. A modified group search optimization with nearest neighborhood (GSO-NH) is proposed to optimize the weights and thresholds of BPNN. GSO-NH is integrated with BPNN to build a soft sensor model. Finally, the soft sensor model based on BPNN and GSO-NH (GSO-NH-NN) is used to infer the outlet ammonia concentration in a real-world application. Three other modeling methods are applied for comparison with GSO-NH-NN. The results show that the soft sensor based on GSO-NH-NN has a good prediction performance with high accuracy. Moreover, the GSO-NH-NN also provides good generalization ability to other modeling problems in ammonia synthesis production.

关键词： ammonia synthesis ammonia concentration soft sensor group search optimization

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An aligned mixture probabilistic principal component analysis for fault detection of multimode chemical processes

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Chinese Journal of chemical Engineering 2015年第8期23卷 1357-1363页

作者：杨雅伟马玉鑫宋冰侍洪波 Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology

A novel approach named aligned mixture probabilistic principal component analysis(AMPPCA) is proposed in this study for fault detection of multimode chemical processes. In order to exploit within-mode correlations,the AMPPCA algorithm first estimates a statistical description for each operating mode by applying mixture probabilistic principal component analysis(MPPCA). As a comparison, the combined MPPCA is employed where monitoring results are softly integrated according to posterior probabilities of the test sample in each local model. For exploiting the cross-mode correlations, which may be useful but are inadvertently neglected due to separately held monitoring approaches, a global monitoring model is constructed by aligning all local models together. In this way, both within-mode and cross-mode correlations are preserved in this integrated space. Finally, the utility and feasibility of AMPPCA are demonstrated through a non-isothermal continuous stirred tank reactor and the TE benchmark process.

关键词： Multimode process monitoring Mixture probabilistic principal component analysis Model alignment Fault detection

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

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Chinese Journal of chemical Engineering 2012年第6期20卷 1174-1179页

作者：解翔侍洪波 Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and TechnologyMinistry of Education

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

关键词： multimode process monitoring fuzzy C-means locality preserving projection integrated monitoring index Tennessee Eastman process

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