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作者： Tang, Yang Qian, Feng Gao, Huijun Kurths, Jürgen Potsdam Institute for Climate Impact Research Potsdam14473 Germany Institute of Physics Humboldt University of Berlin Berlin12489 Germany Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai200237 China Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin150080 China Institute for Complex Systems and Mathematical Biology University of Aberdeen AberdeenAB24 3UE United Kingdom Department of Control Theory Nizhny Novgorod State University Gagarin Avenue 23 Nizhny Novgorod606950 Russia King Abdulaziz University Jeddah21589 Saudi Arabia

Complex networks have, in recent years, brought many innovative impacts to large-scale systems. However, great challenges also come forth due to distinct complex situations and imperative requirements in human life nowadays. This paper attempts to present an overview of recent progress of synchronization of complex dynamical networks and its applications. We focus on robustness of synchronization, controllability and observability of complex networks and synchronization of multiplex networks. Then, we review several applications of synchronization in complex networks, especially in neuroscience and power grids. The present limitations are summarized and future trends are explored and tentatively highlighted. © 2014 Elsevier Ltd. All rights reserved.

关键词： Synchronization

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Teaching-Learning-Based optimization with Learning Enthusiasm Mechanism and Its Application in chemical Engineering

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Journal of Applied Mathematics 2018年第none期2018卷 1-19页

作者： Chen, Xu Xu, Bin Yu, Kunjie Du, Wenli School of Electrical and Information Engineering Jiangsu University Zhenjiang Jiangsu212013 China Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai200237 China School of Mechanical Engineering Shanghai University of Engineering Science Shanghai201620 China School of Electrical Engineering Zhengzhou University Zhengzhou450001 China

Teaching-learning-based optimization (TLBO) is a population-based metaheuristic search algorithm inspired by the teaching and learning process in a classroom. It has been successfully applied to many scientific and engineering applications in the past few years. In the basic TLBO and most of its variants, all the learners have the same probability of getting knowledge from others. However, in the real world, learners are different, and each learner's learning enthusiasm is not the same, resulting in different probabilities of acquiring knowledge. Motivated by this phenomenon, this study introduces a learning enthusiasm mechanism into the basic TLBO and proposes a learning enthusiasm based TLBO (LebTLBO). In the LebTLBO, learners with good grades have high learning enthusiasm, and they have large probabilities of acquiring knowledge from others;by contrast, learners with bad grades have low learning enthusiasm, and they have relative small probabilities of acquiring knowledge from others. In addition, a poor student tutoring phase is introduced to improve the quality of the poor learners. The proposed method is evaluated on the CEC2014 benchmark functions, and the computational results demonstrate that it offers promising results compared with other efficient TLBO and non-TLBO algorithms. Finally, LebTLBO is applied to solve three optimal control problems in chemical engineering, and the competitive results show its potential for real-world problems. © 2018 Xu Chen et al.

关键词： Computational efficiency

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$\mathcal {H}_{\infty }$ State Estimation for PDT-Switched Coupled Neural Networks Under Round-Robin Protocol: A Cooperation-Competition-Based Mechanism

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IEEE Transactions on Network Science and Engineering 2022年第2期10卷 911-921页

作者： Hao Shen Yinsheng Song Jing Wang Ju H. Park AnHui Province Key Laboratory of Special Heavy Load Robot Anhui University of Technology Ma'anshan China Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China School of Electrical and Information Engineering Anhui University of Technology Ma'anshan China School of Information Science and Engineering East China University of Science and Technology Shanghai China Department of Electrical Engineering Yeungnam University Kyongsan Republic of Korea

This paper is concentrated on the $\mathcal {H_{\infty }}$ state estimation problem for switched coupled neural networks based on a Takagi-Sugeno fuzzy model. Notably, the time-variant network topology with alternate fast switchings and slow ones is described suitably by a persistent dwell-time rule, and the interactive dynamics with both cooperative properties and antagonistic ones among nodes are featured comprehensively by the switching signed graph. In view of the communication pressures brought by network-induced problems and the requirements in digital control, the round-robin protocol and logarithmic quantization are flexibly integrated for more transmission efficiency and fewer data collisions. Thereafter, by utilizing a relaxed multiple Lyapunov function method and some novel matrix process techniques, sufficient criteria guaranteeing the exponential stability in a globally uniform sense with a prescribed $\mathcal {H_{\infty }}$ performance level of the estimation error system are established. Finally, the synthesized analysis of the proposed method is presented with an illustrative example.

关键词： Switches Topology Switching circuits Protocols Neurons State estimation Quantization (signal)

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Finite-time Sliding Mode control Under Dynamic Event-triggered Scheme ⁎

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IFAC-PapersOnLine 2020年第2期53卷 5069-5074页

作者： Jiarui Li Yugang Niu Bei Chen The Key Laboratory of Advanced Control and Optimization for Chemical Process (East China University of Science and Technology) Ministry of Education and The Shanghai Institute of Intelligent Science and Technology (Tongji University) Shanghai China The School of Electric and Electronic Engineering Shanghai University of Engineering Science Shanghai 201620 China

In this work, we investigate the finite-time boundedness (FTB) problem for a class of continuous-time uncertain systems via sliding mode control (SMC) method. A dynamic event-triggered scheme is introduced to determine whether the measurement output will be transmitted. And then, a state observer is designed by means of the transmitted output information, based on which a sliding surface in the estimation space is construct. It is shown that the corresponding SMC law can drive the system trajectories onto the specified sliding surface in a finite (possibly short) time. Meanwhile, a partitioning strategy is introduced to analyze the FTB over both reaching phase and the sliding motion, respectively. Finally, a numerical simulation is given.

关键词： Sliding mode control finite-time boundedness event-triggered control external disturbance partitioning strategy

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Neighborhood Interval Observer Based Coordination control for Multi-agent Systems with Disturbances ⁎

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IFAC-PapersOnLine 2020年第2期53卷 10994-10999页

作者： Xiaoling Wang Guo-Ping Jiang Wen Yang Housheng Su Xiaofan Wang College of Automation Nanjing University of Posts and Telecommunications Nanjing 210023 China and also with Jiangsu Engineering Lab for IOT Intelligent Robots (IOTRobot) Nanjing 210023 China Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Shanghai 200237 China School of Artifcial Intelligence and Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Luoyu Road 1037 Wuhan 430074 China Department of Automation Shanghai University Shanghai 200072 China

This paper focuses on multi-agent systems with uncertain disturbances, in which only the bounding functions on the disturbances and the bounds on the initial state of each agent are known. By designing a neighborhood interval observer for this kind of multi-agent system, the estimation of the sum of the relative state of each agent associated with itself and its neighbors is frstly realized. Then, on the basis of these estimated information, local control algorithm is designed to drive the system to achieve bounded consensus.

关键词： Multi-agent system disturbance neighborhood interval observer bounded consensus

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Adaptive neural output feedback control for stochastic nonlinear systems with unknown control directions

Adaptive neural output feedback control for stochastic nonli...

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Chinese control Conference (CCC)

作者： Zhaoxu Yu Shugang Li Key Laboratory of Advanced Control and Optimization for Chemical Process of Ministry of Education East China University of Science and Technology Shanghai 200237 China Department of Industrial Engineering and Logistics Shanghai Jiao Tong University China 200240 China

This paper focuses on the problem of adaptive output feedback stabilization for a class of stochastic nonlinear system with unknown control directions. By using a linear state transformation, the unknown control coefficients are lumped together, such that the original system is transformed to a new system for which control design becomes feasible. By employing the input-driven observer, a novel adaptive neural network (NN) output-feedback controller which only contains one adaptive parameter is developed for such systems by using backstepping technique and NNs' parameterization. The proposed control design guarantees that all the signals in the closed-loop systems are 4-moment semi-globally uniformly ultimately bounded.

关键词： Adaptive systems Artificial neural networks Nonlinear systems control design Observers

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A federated data-driven evolutionary algorithm

arXiv

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arXiv 2021年

作者： Xu, Jinjin Jin, Yaochu Du, Wenli Gu, Sai Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai200237 China Department of Computer Science University of Surrey GuildfordGU2 7XH United Kingdom Department of Chemical and Process Engineering University of Surrey GuildfordGU27XH United Kingdom

Data-driven evolutionary optimization has witnessed great success in solving complex real-world optimization problems. However, existing data-driven optimization algorithms require that all data are centrally stored, which is not always practical and may be vulnerable to privacy leakage and security threats if the data must be collected from different devices. To address the above issue, this paper proposes a federated data-driven evolutionary optimization framework that is able to perform data driven optimization when the data is distributed on multiple devices. On the basis of federated learning, a sorted model aggregation method is developed for aggregating local surrogates based on radial-basis-function networks. In addition, a federated surrogate management strategy is suggested by designing an acquisition function that takes into account the information of both the global and local surrogate models. Empirical studies on a set of widely used benchmark functions in the presence of various data distributions demonstrate the effectiveness of the proposed framework. © 2021, CC BY.

关键词： optimization

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Analysis and discussion of group mobility management in ubiquitous network

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Nanjing Li Gong Daxue Xuebao/Journal of Nanjing University of Science and Technology 2012年第SUPPL.2期36卷 299-305页

作者： Cao, Jian-Fu Ling, Zhi-Hao Yuan, Yi-Feng School of Information Science and Engineering East China University of Science and Technology Shanghai 200237 China Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education Shanghai 200237 China

In order to research the group mobility management technology, the research progress of group mobility management in ubiquitous network is summarized. Based on the new features of the ubiquitous network, the structures, performances and application fields of group mobility model are discussed, and some key technologies such as context awareness, service decision-making, cooperative transmission and radio resource management are also introduced. Aiming at the challenges in this filed, some solutions and valuable research directions are proposed.

关键词： Decision making

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Improving differential evolution with impulsive control framework

Improving differential evolution with impulsive control fram...

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Congress on Evolutionary Computation

作者： Wei Du Sunney Yung Sun Leung Chun Kit Kwong Yang Tang Institute of Textiles and Clothing The Hong Kong Polytechnic University Hong Kong Hong Kong Polytechnic University Kowloon HK Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hong Kong The Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China

ISBN: (纸本)9781479974931

Differential evolution (DE) is a simple but powerful evolutionary algorithm, which has been widely and successfully used in many areas. In this paper, an impulsive control method is introduced to the DE framework, and the impulsive DE (IpDE) is proposed for improving the performance of DE. The impulsive control operation instantly moves the individuals which do not update for continuous pre-defined generations to a desired state based on the individuals with better fitness values in the current population. This way, IpDE controls individuals' positions in the space domain according to the stagnation status of the population. In order to validate the effectiveness of IpDE, the presented framework is applied to the original DE algorithms, as well as several state-of-the-art DE variants. Experimental results exhibit that IpDE is a simple but effective framework to improve the performance of the studied DE algorithms.

关键词： Sociology Statistics optimization Radiation detectors Aerospace electronics Hybrid power systems Evolution (biology)

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Robust order scheduling in the fashion industry: A multi-objective optimization approach

arXiv

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arXiv 2017年

作者： Du, Wei Tang, Yang Leung, Sunney Yung Sun Tong, Le Vasilakos, Athanasios V. Qian, Feng Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai200237 China Institute of Textile and Clothing Hong Kong Polytechnic University Hong Kong Hong Kong Department of Computer Science Electrical and Space Engineering Lulea University of Technology Lulea97187 Sweden

—In the fashion industry, order scheduling focuses on the assignment of production orders to appropriate production lines. In reality, before a new order can be put into production, a series of activities known as pre-production events need to be completed. In addition, in real production process, owing to various uncertainties, the daily production quantity of each order is not always as expected. In this research, by considering the pre-production events and the uncertainties in the daily production quantity, robust order scheduling problems in the fashion industry are investigated with the aid of a multi-objective evolutionary algorithm (MOEA) called nondominated sorting adaptive differential evolution (NSJADE). The experimental results illustrate that it is of paramount importance to consider pre-production events in order scheduling problems in the fashion industry. We also unveil that the existence of the uncertainties in the daily production quantity heavily affects the order scheduling. Copyright © 2017, The Authors. All rights reserved.

关键词： Multiobjective optimization

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