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

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A String of Tethered Drones - System Dynamics and control

A String of Tethered Drones - System Dynamics and Control

引用

European Conference on Mobile Robots (ECMR)

作者： Kosarnovsky, B. Arogeti, S. Ministry of Education Key Laboratory of Advanced Control and Optimization for Chemical Processes (East China University of Science and Technology) Shanghai P. R. China

ISBN: (纸本)9781728136059

In this paper we present a novel concept of a tethered-drones system. The system includes an arbitrary number of drones connected serially to an active ground station. The considered drones are of quadrotor type. Utilizing a unique pulley-gimbal mechanism, each drone can freely move along the tether and its position is measured with respect to the ground station without the use of standard onboard inertial sensors. The proposed system can be thought of as a robotic arm where each tether section acts as a variable-length link and each drone is a joint actuator. We model the coupled behavior of the ground station and the string, taking into account an arbitrary number of drones. Then, a controller that combines tools from geometric-control and linear-control is suggested. Finally, the concept is demonstrated using numerical simulations, which also illustrate its potential effectiveness.

关键词： Drones DC motors Position measurement Mathematical model Sensors Angular velocity Pulleys

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Graph-based semisupervised learning for icing fault detection of wind turbine blade

Graph-based semisupervised learning for icing fault detectio...

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作者： Yi, Huaikuan Jiang, Qinchao Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology Shanghai200237 China

The detection of blade icing faults in wind farms is an important task in improving the reliability and safety of wind power systems. Detection is primarily achieved through supervised learning, using labeled samples. However, labeling a large volume of data often consumes lots of manpower, which means that semisupervised learning is usually required. This study proposes a graph-based semisupervised learning system to achieve efficient fault detection in wind turbines (WTs). The proposed method detects abnormal samples by optimizing a small amount of labeled and a large amount of unlabeled data. Firstly, feature selection based on mutual information is performed on supervisory control and data acquisition data, so as to eliminate redundant variables. Secondly, a graph is constructed, using the limited labeled data to indicate the connections between samples. Finally, the initial class information is propagated to the entire data set by iteratively updating the input of the graph model. Experimental results based on a selection of benchmark data sets and real WT data demonstrate the feasibility of the proposed method. © 2020 IOP Publishing Ltd.

关键词： Wind turbines

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A class of nonlinear discrete-time system adaptive control based on second level multiple models 36

A class of nonlinear discrete-time system adaptive control b...

引用

第36届中国控制会议

作者： Yong Chen Xin Wang Zhenlei Wang Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Center of Electrical and Electronic Technology Shanghai Jiao Tong University

ISBN: (纸本)9781538629185

Using multiple models to deal with transients for adaptive systems which is large parametric uncertainties was introduced in the 1990 *** common method is using switching ***,these methods has some potential *** this paper,a new multiple models scheme is proposed for a class strict-feedback nonlinear discrete-time ***,we use feedback linearization and backstepping method to develop adaptive indirect schemes with an observer-based identifier to ***,we design a controller based on the convex hull of parameter *** the number of necessary models is less than switching scheme,and can use the information of all identification ***,simulation have demonstrated that this method is effectively to a class of strict-feedback nonlinear discrete-time system.

关键词： convex hull multiple models backstepping nonlinear system strict-feedback

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Localization with a mobile anchor using ABC-GA hybrid algorithm in wireless sensor networks

Localization with a mobile anchor using ABC-GA hybrid algori...

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International Workshop on advanced Computational Intelligence and Intelligent Informatics, IWACIII 2011

作者： Li, Sijin Qi, Rongbin Qian, Feng Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai 200237 China

Nodes localization plays an important role in applications of wireless sensor networks. In this paper, a localization scheme with a mobile anchor using a hybrid algorithm (ABC-GA) which combines artificial bee colony (ABC) algorithm with the advantages of genetic algorithm (GA) is proposed. The localization scheme determines location of unknown node by the mobile anchor;it has high accuracy without any additional requirements for the hardware of unknown node. The core problem of the scheme is finding the shortest path to traversal all unknown nodes. We use ABC-GA hybrid algorithm to solve this problem. Simulation results show that ABC-GA hybrid algorithm has high convergence rate and strong global search capability and the accuracy of localization scheme is satisfactory.

关键词： Genetic algorithms

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Real time optimization of the gasoline blending process with unscented Kalman filter

Real time optimization of the gasoline blending process with...

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2011 International Conference on Internet Computing and Information Services, ICICIS 2011

作者： Cheng, Hui Zhong, Weimin Qian, Feng 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)9780769545394

Gasoline blending is a critical process in petroleum refineries. Real-time optimization (RTO) techniques have been popular with the applications for the blending process for optimization purpose. However the dependency of RTO on the measurement of the component impairs its applicability. Therefore how to utilize the blending model and the product measurement to free RTO from the component measurement is the major research topic in this paper. Unscented Kalman Filter, due to its ability to estimate the parameter for nonlinear model, is chosen to estimate component properties based on the product measurement. The RTO strategy is then proposed with the UKF method for the recipe calculation periodically. Furthermore, the proposed RTO is tested with the gasoline blending benchmark problem, while the results are compared with the ideal blending case. The accuracy of the component estimation and the efficiency of the RTO are verified with the results. © 2011 IEEE.

关键词： Blending

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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...

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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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Specific index-related process monitoring using a two-step information extraction method 30

Specific index-related process monitoring using a two-step i...

引用

30th Chinese control and Decision Conference, CCDC 2018

作者： Zhao, Bo Song, Bing Tan, Shuai Shi, Hongbo 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)9781538612439

Specific index-related process monitoring covers a wide range of requirements from industrial production. At present, it is still a challenge to divide into the specific index-related information and the specific index-unrelated information more accurately. In this paper, a two-step information extraction method is presented to solve this problem. In the first step, the overall process variables are separated into a strongly related variable set and a weakly related variable set. In the second step, the two sets are further divided into a specific index-related subset and a specific index-unrelated subset, respectively. Two specific index-related subsets form a new subspace related to the specific index. Two specific index-unrelated subsets are combined into a specific index-unrelated subspace. Two T2 statistics are built in the specific index-related subspace and the specific index-unrelated subspace, respectively. Finally, the proposed method is applied for Tennessee Eastman (TE). The results indicate the effectiveness of the proposed method. © 2018 IEEE.

关键词： Indexes Information retrieval Monitoring Principal component analysis Correlation coefficient Correlation Fault detection

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An improved particle swarm algorithm based on cultural algorithm for constrained optimization

An improved particle swarm algorithm based on cultural algor...

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

ISBN: (纸本)9783642277078

This paper develops an improved particle swarm optimization algorithm based on cultural algorithm for constrained optimization problems. Firstly, chaos method is utilized in the initialization process of single swarm in population space to assure the searching breadth, and evolves with standard particle swarm optimization (PSO). Secondly, fixed proportion elites are selected from population space to construct the swarm of belief space through acceptance function. Then, the belief space updates its normative knowledge and situational knowledge according to the elite particles, and the elite-swarm in the belief space performs PSO operation according to the update knowledge and generates new particles. After that, the belief space renews the knowledge again, and passes down the new knowledge which has been updated twice to give better guidance to all the particles in the population space. The efficiency of the initialization strategy and the double evolving knowledge strategy are verified in six constrained optimization problems. © 2012 Springer-Verlag GmbH Berlin Heidelberg.

关键词： Constrained optimization

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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...

引用

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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Locust behaved particle swarm optimization technique 3

Locust behaved particle swarm optimization technique

引用

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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