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检索条件"机构=The Key Laboratory of Advanced Control and Optimization for Chemical Processes"

共 633 条记录，以下是381-390 订阅

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Fault detection for networked systems with random packet losses, sensor saturation and multiplicative noises

Fault detection for networked systems with random packet los...

引用

International Conference on Information and Automation (ICIA)

作者： Fengfeng Qian Huaicheng Yan Hongbo Shi Hao Zhang Key Laboratory of Advanced Control and Optimization for Chemical Process of Ministry of Education East China University of Science and Technology Shanghai China Ministry of Education Tongji University Shanghai China

ISBN: (纸本)9781467391054

This paper is concerned with the H∞ fault detection (FD) for a class of networked systems with random packet losses, sensor saturation and multiplicative noises. The network with both output measurement and control packet losses is modeled with two independent Bernouli distributed white sequences. H∞ filtering theory is adapted to formulate the problem of fault detection for networked systems. The output measurement is affected by sensor saturation which is described by sector-nonlinearities, and the multiplicative noises are modeled as a form of Gaussian white noise. The purpose of the addressed problem is to design a fault detection filter such that, the fault detection dynamic system is exponentially stable in the mean square, and the error between the residual value and fault value is made as small as possible. A sufficient condition for FD filter is derived by solving the linear matrix inequality (LMI). Finally, a numerical example is illustrated to show the effectiveness of the designed method.

关键词： Fault detection Packet loss Noise Robustness Networked control systems Circuit faults

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Observer-based controller design for networked systems with multiple packet losses and mixed delay

Observer-based controller design for networked systems with ...

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第27届中国控制与决策会议

作者： Fengfeng Qian Huaicheng Yan Hongbo Shi Hao Zhang 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 EducationTongji University

The paper focuses on a stabilizing controller design problem for networked systems with quantization,mixed delays,and a series of packet losses due to the signal transmission through the unreliable communication *** of both sensor-to-controller and controller-to-actuator are taken into account for the networked systems,and the distributed time delay is also considered in the network *** conditions for designing the controller as well as the system parameters can be obtained by solving certain linear matrix ***,the effectiveness of the designed method is proved by a numerical example.

关键词： quantization,mixed delays,multiple packet losses

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Quantized H∞ control for networked systems with randomly multi-step transmission delays

Quantized H∞ control for networked systems with randomly mu...

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作者： Zhang, Yilian Yang, Fuwen Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology Shanghai200237 China

ISBN: (纸本)9781479940325

In this paper, a quantized H∞ control problem for networked control systems (NCSs) subject to randomly multi-step transmission delays is investigated. A quantizer is used before the measurement signal enters the communication network and the randomly multi-step transmission delays which are described by a mathematical model are considered during the transmission through the network. Sufficient conditions are derived for the considered system to satisfy the H∞ norm constraint subject to the randomly multi-step transmission delays. Simulation results demonstrate the effectiveness of the proposed method. © 2014 IEEE.

关键词： Networked control systems

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Dynamic multi-objective optimization of chemical processes using modified Bare-bones MOPSO algorithm 3

Dynamic multi-objective optimization of chemical processes u...

引用

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

作者： Wang, Shan-Shan Du, Wen-Li Chen, Xu Qian, Feng East China University of Science and Technology Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education Shanghai200237 China

Dynamic multi-objective optimization is a complex and difficult research topic of process systems engineering. In this paper, a modified Bare-bones MOPSO algorithm is proposed that takes advantage of few parameters of bare-bones algorithm. To avoid premature convergence, Gaussian mutation is introduced;and an adaptive sampling distribution strategy is also used to improve the exploratory capability. Moreover, a circular crowded sorting approach is adopted to improve the uniformity of the population distribution. Finally, by combining the algorithm with control vector parameterization, an approach is proposed to solve the dynamic optimization problems of chemical processes. It is proved that the new algorithm performs better compared with other classic multi-objective optimization algorithms through the results of solving three dynamic optimization problems.

关键词： Bone

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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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A Dynamic Clustering Method with Overlaps for Event Detection in WSNs

A Dynamic Clustering Method with Overlaps for Event Detectio...

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第三十四届中国控制会议

作者： Yuan HU Yugang NIU Bei Chen Key Laboratory of Advanced Control and Optimization for Chemical Process of Ministry of Education (East China University of Science and Technology) Central academy Shanghai Electric Group Co. Ltd.

ISBN: (纸本)9781467374439

Clustering is an energy efficient routing protocol for wireless sensor networks. Traditional clustering methods can prolong the network lifetime and achieve scalable performance, but they do not consider the event development. In many applications, the scalability and occurrence region of events often change. A dynamic clustering method with overlaps(DCMO) is proposed in this paper. Due to the 2-logical-coverage overlaps of the proposed clustering method, the clusters can be migrated with the changing tendency of events. As a result, the sensed data can be transmitted at a lower price. Simulations show that the proposed DCMO method has lower energy consumption, compared with LEACH protocol.

关键词： Clustering,event development,2-logical-coverage overlaps,event detection

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Fault diagnosis of CTA hydrogenation process based on KPCA 3

Fault diagnosis of CTA hydrogenation process based on KPCA

引用

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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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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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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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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