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第35届中国控制会议

作者： ZOU Qin ZHANG Junfeng LU Renquan ZHANG Ridong Information and Control Institute Hangzhou Dianzi University Key Laboratory of Advanced Control and Optimization for Chemical Processes

ISBN: (纸本)9781509009107

In practical processes,lots of phenomena are unable to be accurately described by conventional integer order models,while fractional order models can describe the characteristics more *** this paper,a new fractional order predictive functional control(FPFC) method is presented for the fractional order *** Oustaloup approximation is employed to derive the approximate model of the original ***,the Griinwald-Letnikov definition and fractional operator are used to extend the integer order predictive functional control to the *** with traditional predictive functional control,simulation results reveal that the fractional order controller can achieve improved control performance.

关键词： Fractional order calculus Oustaloup approximation fractional order system predictive functional control

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Fuzzy-Model-Based Leader-Follower Consensus of Nonlinear Multi-Agent Systems with Input Saturation

Fuzzy-Model-Based Leader-Follower Consensus of Nonlinear Mul...

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International Conference on control, Automation, Robotics and Vision (ICARCV)

作者： Xiaolu Liu Duxin Chen Yan-Wu Wang Huaicheng Yan School of Automation Huazhong University of Science and Technology Wuhan P. R. China School of Mathematics Southeast University Nanjing P. R. China Key Laboratory of Advanced Control and Optimization for Chemical Process of Ministry of Education East China University of Science and Technology Shanghai P. R. China

This paper presents a fuzzy-model-based method for solving the consensus problem of a class of nonlinear multi-agent systems (MASs) with input saturation. Since each agent has nonlinear dynamics, the system is not asymptotically null controllable with bounded controls (ANCBC). Therefore, the widely-used low-gain feedback method for designing consensus protocols of MASs with input saturation can no longer work. To this end, the Takagi-Sugeno (T-S) fuzzy model is adopted to formulate the error dynamics of those nonlinear follower agents with input saturation as well as a leader with time-varying states. Accordingly, by using the properties of convex hull, a set of invariance condition in the format of linear matrix inequality (LMI) is designed. Furthermore, by enlarging the shape reference set, the estimation of the attraction domain can be obtained. Simultaneously, by viewing the control gain as an extra free parameter in the LMI optimization procedure, the leader-follower consensus algorithm is proposed, which guarantees that all followers with input saturation can track the leader, and they can asymptotically reach consensus. Finally, numerical experiments validate the effectiveness of the proposed anti-saturation consensus algorithm.

关键词： Automation Nonlinear dynamical systems STEM Actuators Symmetric matrices

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Study on co-cracking performance of different hydrocarbon mixture in a steam pyrolysis furnace

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Chinese Journal of Chemical Engineering 2016年第9期24卷 1252-1262页

作者： Benfeng Yuan Jinlong Li Wenli Du Feng Qian Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology School of Information Science and Technology East China University of Science and Technology

Co-cracking is a process where the mixtures of different hydrocarbon feedstocks are cracked in a steam pyrolysis furnace, and widely adopted in chemical industries. In this work, the simulations of the co-cracking of ethane and propane, and LPG and naphtha mixtures have been conducted, and the software packages of COILSIM1 D and Sim CO are used to account for the cracking process in a tube reactor. The effects of the mixing ratio, coil outlet temperature, and pressure on cracking performance have been discussed in detail. The co-cracking of ethane and propane mixture leads to a lower profitability than the cracking of single ethane or single propane. For naphtha, cracking with LPG leads to a higher profitability than single cracking of naphtha, and more LPG can produce a higher profitability.

关键词： Co-cracking Mixing ratio Coil outlet temperature（COT） Coil outlet pressure（COP） Pyrolysis furnace

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Distributed H∞ Filtering for Active Semi-vehicle Suspension Systems through Network with Limited Capacity 35

Distributed H∞ Filtering for Active Semi-vehicle Suspension...

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第35届中国控制会议

作者： ZHENG Xiaoyuan ZHANG Hao YAN Huaicheng Department of Control Science and Engineering Tongji University Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education

ISBN: (纸本)9781509009107

This paper investigates a distributed H filtering problem for active semi-vehicle suspension *** active semi-vehicle suspension system is divided into two subsystems and filter systems are designed for each *** information of active semi-vehicle suspension system is transmitted via wireless network with limited ***,the limited bandwidth and time delay of network are taken into *** applying Lyapunov functional approach,the H filtering performance is established,and the filtering design is converted to optimization problem with linear matrix inequality ***,a simulation experiment is provided to illustrate the effectiveness of the proposed filtering system.

关键词： Active semi-vehicle suspension system Time-delay Event-triggered Distributed H_∞ filter

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Neural Component Analysis for Fault Detection

arXiv

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arXiv 2017年

作者： Zhao, Haitao Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education School of Information Science and Engineering East China University of Science and Technology

Principal component analysis (PCA) is largely adopted for chemical process monitoring and numerous PCA-based systems have been developed to solve various fault detection and diagnosis problems. Since PCA-based methods assume that the monitored process is linear, nonlinear PCA models, such as autoencoder models and kernel principal component analysis (KPCA), has been proposed and applied to nonlinear process monitoring. However, KPCA-based methods need to perform eigen-decomposition (ED) on the kernel Gram matrix whose dimensions depend on the number of training data. Moreover, prefixed kernel parameters cannot be most effective for different faults which may need different parameters to maximize their respective detection performances. Autoencoder models lack the consideration of orthogonal constraints which is crucial for PCA-based algorithms. To address these problems, this paper proposes a novel nonlinear method, called neural component analysis (NCA), which intends to train a feedforward neural work with orthogonal constraints such as those used in PCA. NCA can adaptively learn its parameters through backpropagation and the dimensionality of the nonlinear features has no relationship with the number of training samples. Extensive experimental results on the Tennessee Eastman (TE) benchmark process show the superiority of NCA in terms of missed detection rate (MDR) and false alarm rate (FAR). The source code of NCA can be found in https://***/haitaozhao/***. Note to Practitioner: Online monitoring for chemical process has been considered as a critical and hard task in real industrial applications. In this paper, an innovative method called neural component analysis (NCA) is proposed for fault detection. NCA is a unified model including a nonlinear encoder and a linear decoder. Due to its simple and intuitive format, NCA has superior performance in both computational efficiency and fault detection which makes it suitable for proces

关键词： Principal component analysis

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Differences in motor imagery activity between the paretic and non-paretic hands in stroke patients using an EEG BCI 11th

Differences in motor imagery activity between the paretic an...

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11th International Conference on Augmented Cognition, AC 2017, held as part of the 19th International Conference on Human-Computer Interaction, HCI 2017

作者： Qiu, Zhaoyang Chen, Shugeng Allison, Brendan Z. Jia, Jie Wang, Xingyu Jin, Jing Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China Department of Neurology Huashan Hospital Fudan University Shanghai China Department of Cognitive Science University of California at San Diego La JollaCA United States

Stroke is the leading cause of serious and long-term disability worldwide. Stroke survivors may recover some motor function after rehabilitation therapy. Many studies have shown that motor imagery (MI) based brain-computer Interface (BCI) can improve upper limb stroke rehabilitation. However, as stroke patients have suffered neurological damage, the brain regions associated with motor function might be compromised, thus impairing BCI performance. In this paper, we tried to explore whether stroke patients’ imagination of hand movement differed between paretic versus non-paretic hands. Ten stroke patients (5 male, aged 21–69 years, mean 48.4 ± 15.4) participated in this study. They imagined moving either the left or the right hand according to cues. The common spatial patterns (CSP) approach was used to extract MI features, and a support vector machine (SVM) was used for classification. Results did not show that motor imagery accuracy for paretic hands was not substantially worse than with non-paretic hands. In tandem with other work assessing motor accuracy in healthy participants versus stroke patients, these results suggest that possible concerns about stroke patients’ use of BCI-based motor imagery systems may not present serious obstacles to wider research and implementation. © Springer International Publishing AG 2017.

关键词： Brain computer interface

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Event-based Distributed State Estimation over a WSN with False Data Injection Attack

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IFAC-PapersOnLine 2016年第22期49卷 286-290页

作者： Lei, Li Yang, Wen Yang, Chao Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Ministry of Education Shanghai200237 China

In this paper, we consider the security issue in a wireless sensor network (WSN). We investigate distributed state estimation over a WSN under hostile attacks. To defend against malicious attacks, we propose a novel event-based mechanism, with which each sensor decides to send data to its neighboring sensors according to a data detection condition. When the detection condition is satisfied, the sensor is triggered at time step k, then it estimates the target state and sends its observation data to its neighbors; Otherwise, the sensor can only receive the data from its neighboring sensors. We provide a sufficient condition to guarantee the stability of the proposed estimator under the malicious attacks, and a numerical example to illustrate the effectiveness of the proposed event-based estimator. © 2016

关键词： Wireless sensor networks Network security State estimation Data detection Distributed state estimation Event based False data injection attacks Malicious attack Observation data Security issues Target state

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Improved Estimation of Distribution Algorithm for Solving Unrelated Parallel Machine Scheduling Problem

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Journal of Donghua University(English Edition) 2016年第5期33卷 797-802页

作者：孙泽文顾幸生 Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of EducationEast China University of Science and Technology

Scheduling problem is a well-known combinatorial optimization *** effective improved estimation of distribution algorithm(IEDA) was proposed for minimizing the makespan of the unrelated parallel machine scheduling problem(UPMSP).Mathematical description was given for the *** IEDA which was combined with variable neighborhood search(IEDA_VNS) was proposed to solve the UPMSP in order to improve local search ability.A new encoding method was designed for representing the feasible solutions of the *** knowledge of the UPMSP were taken consideration in IEDA_ VNS for probability matrix which was based the processing time *** simulation results show that the proposed IEDA_VNS can solve the problem effectively.

关键词： Scheduling neighborhood scheduling minimizing processed unrelated probabilistic intelligent heuristic representing

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Variable Classification-based Co-estimation of Distribution Algorithm Assisted by a Cooperative Particle Swarm Optimizer

Variable Classification-based Co-estimation of Distribution ...

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2017 Asia-Pacific Computational Intelligence and Information Technology Conference

作者： Wen Liu Xuefeng Yan East China University of Science and Technology Key Laboratory of Advanced Control and Optimization for Chemical Process of Ministry of Education East China University of Science and Technology

Since estimation of distribution algorithms(EDAs) were proposed,many methods have been made to improve EDAs' *** this paper,a variable classification-based cooperative estimation of distribution algorithm(VC-CEDA) is proposed to balance the global and local searching ability of estimation of distribution *** study proposes a variable classification method to improve the efficiency and accuracy of probability model *** a cooperative particle swarm optimizer that can improve the local searching ability is *** VC-CEDA combines the advantages of traditional algorithm and cooperative ***-CEDA shows significantly better performance on 10 test *** this study,the experimental results show that the VC-CEDA performs better than traditional EDAs.

关键词： Variable Classification-based Co-estimation of Distribution Algorithm Assisted by a Cooperative Particle Swarm Optimizer Swarm

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Variable Classification-based Co-estimation of Distribution Algorithm Assisted by a Cooperative Particle Swarm Optimizer

Variable Classification-based Co-estimation of Distribution ...

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2017第二届亚太计算智能与信息技术学术会议 (APCIIT 2017)

Since estimation of distribution algorithms(EDAs) were proposed,many methods have been made to improve EDAs' performance. In this paper,a variable classification-based cooperative estimation of distribution algorithm(VC-CEDA) is proposed to balance the global and local searching ability of estimation of distribution algorithms. This study proposes a variable classification method to improve the efficiency and accuracy of probability model development. And a cooperative particle swarm optimizer that can improve the local searching ability is proposed. The VC-CEDA combines the advantages of traditional algorithm and cooperative operations. VC-CEDA shows significantly better performance on 10 test functions. In this study,the experimental results show that the VC-CEDA performs better than traditional EDAs.

关键词： Variable Classification-based Co-estimation of Distribution Algorithm Assisted by a Cooperative Particle Swarm Optimizer Swarm

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