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Stability analysis of switched nonlinear delay systems with sampled-data inputs

International Journal of Robust and Nonlinear control 2019年第14期29卷

作者： Zhichuang Wang Jian Sun Guoliang Chen Jie Chen School of Automation Beijing Institute of Technology Beijing China Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China

This study addresses the uniformly globally asymptotically stability (UGAS) problem of switched nonlinear delay systems (SNDSs) with sampled-data inputs (SDIs). By using multiple Lyapunov functionals (MLFs) method, mode-dependent average dwell times, and the total activating time length of MLFs, some stability criteria are explicitly obtained for SNDSs with SDIs. Meanwhile, the UGAS property for SNDSs with some or all unstable modes is investigated. For unstable modes and stable modes, we adopt different switching signals. Besides, we establish some sufficient stability conditions in the form of an upper bound on the sum of dwell times and sampling intervals. Simulation examples are adopted to illustrate and verify the effectiveness of our proposed methods.

关键词： multiple Lyapunov functionals sampled-data inputs stability switched nonlinear delay systems

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Shaping Effect of Phase-locked Loop on Impedance Characteristics of PMSG Wind Turbines Connected to Weak-grid

Shaping Effect of Phase-locked Loop on Impedance Characteris...

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IEEE General Meeting Power& Energy Society

作者： Feiya Wan Zhen Li Bin Liu Guanghui Li Xiaoliang Dong Xi Chen Samson S. Yu Amanullah M. T. Oo Zhenyu Shan Xiangdong Liu School of Automation and Key Laboratory for Intelligent Control & Decision on Complex Systems Beijing Institute of Technology Beijing China China Electric Power Research Institute Beijing China State Grid Corporation of China and North China Branch of State Grid Corporation of China Beijing China GEIRI North America USA School of Engineering Deakin University 75 Pigdon Rd Waurn Ponds Victoria Australia School of Electrical and Control Engineering North China University of Technology Beijing China

ISBN: (数字)9781728155081

ISBN: (纸本)9781728155098

With the booming installed capacity of permanent magnet synchronous generator (PMSG) for wind energy generation, the grid becomes weaker and the oscillation events are frequently observed in weak-grid-tied large-scale grid-connected PMSG wind farms. Although the impedance-based method is effective to analyze the stability of grid-connected systems, the mathematical relationship between external impedance characteristics and the internal controllers has not been quantitatively analyzed in depth, which is beneficial to the controller design. This paper studies the influence of phase-locked loop (PLL) on the impedance characteristics based on the analytical sequence impedance model of PMSG with typical topology and control structure. The shaping effect of PLL on the impedance characteristics is described so as to clearly describe the relationship between the external impedance characteristics of the wind turbines and the internal controller. Finally, the stability analysis of the grid-connected PMSG wind turbine system is provided in Matlab and simulation results are given to verify the oscillating mechanism caused by PLL.

关键词： Synchronous generators Stability analysis Wind turbines Topology Impedance Phase locked loops Oscillators

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A Learning Convolutional Neural Network Approach for Network Robustness Prediction

arXiv

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arXiv 2022年

作者： Lou, Yang Wu, Ruizi Li, Junli Wang, Lin Li, Xiang Chen, Guanrong The Department of Computing and Decision Sciences Lingnan University Hong Kong The Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The College of Computer Science Sichuan Normal University Chengdu610066 China The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The Department of Control Science and Engineering Tongji University Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

Network robustness is critical for various societal and industrial networks again malicious attacks. In particular, connectivity robustness and controllability robustness reflect how well a networked system can maintain its connectedness and controllability against destructive attacks, which can be quantified by a sequence of values that record the remaining connectivity and controllability of the network after a sequence of node- or edge-removal attacks. Traditionally, robustness is determined by attack simulations, which are computationally very time-consuming or even practically infeasible. In this paper, an improved method for network robustness prediction is developed based on learning feature representation using convolutional neural network (LFR-CNN). In this scheme, higher-dimensional network data are compressed to lower-dimensional representations, and then passed to a CNN to perform robustness prediction. Extensive experimental studies on both synthetic and real-world networks, both directed and undirected, demonstrate that 1) the proposed LFR-CNN performs better than other two state-of-the-art prediction methods, with significantly lower prediction errors;2) LFR-CNN is insensitive to the variation of the network size, which significantly extends its applicability;3) although LFR-CNN needs more time to perform feature learning, it can achieve accurate prediction faster than attack simulations;4) LFR-CNN not only can accurately predict network robustness, but also provides a good indicator for connectivity robustness, better than the classical spectral measures. © 2022, CC BY.

关键词： complex networks

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An Improved Chimp Optimization Algorithm for Short-term Hydrothermal Scheduling

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2022年第3期49卷 666页

作者： Chen, Gonggui Wang, Shumin He, Yongsheng Shang, Wanfeng Long, Hongyu Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing University of Posts and Telecommunications Chongqing400065 China State Grid Chongqing Electric Power Company Chongqing400015 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen Shenzhen518055 China Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications Chongqing400065 China

The short-term hydrothermal scheduling (STHS) is a complex non-linear, non-convex and high-dimensional mathematical optimization problem. Due to the fact that the original chimp optimization algorithm (ChOA) is likely to fall into local optimum and has the disadvantage of low population diversity, an improved chimp optimization algorithm (IChOA) is proposed to solve the STHS problem. Firstly, Logistic-Tent chaotic mapping is employed to initialize the population and increase the population diversity. Then a new nonlinear convergence factor is introduced to make the optimization search process more applicable to STHS problems. Next, particle swarm algorithm (PSO) and gravitational search algorithm (GSA) are combined to enhance the shortcoming and balance the global search ability and local exploration ability of ChOA. Moreover, Cauchy mutation and opposition-based learning strategy are supplemented as perturbation interference when the optimal position is not updated to improve the ability to leap out of the local optimum. Finally, a graded optimization strategy is adopted for the constraint handing of dynamic reservoir balance and thermal power balance. Three standard hydrothermal power systems are utilized to verify the practicability and validity of the proposed method. Results of simulations reveal that IChOA has significant competitive advantages in addressing the STHS problem. © 2022, IAENG International Journal of Computer Science. All Rights Reserved.

关键词： Particle swarm optimization (PSO)

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Emotion extraction and entrainment in social media: The case of U.S. immigration and border security

Emotion extraction and entrainment in social media: The case...

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IEEE International Conference on Intelligence and Security Informatics, ISI

作者： Wingyan Chung Saike He Daniel Dajun Zeng Victor Benjamin Dept. of Decision and Info. Sciences Stetson University DeLand Florida U.S.A. State Key Lab of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China Dept. of MIS U. of Arizona Tucson Arizona U.S.A. Dept. of MIS University of Arizona Arizona U.S.A.

Emotion plays an important role in shaping public policy and business decisions. The growth of social media has allowed people to express their emotion publicly in an unprecedented manner. Textual content and user linkages fostered by social media networks can be used to examine emotion types, intensity, and contagion. However, research into how emotion evolves and entrains in social media that influence security issues is scarce. In this research, we developed an approach to analyzing emotion expressed in political social media. We compared two methods of emotion analysis to identify influential users and to trace their contagion effects on public emotion, and report preliminary findings of analyzing the emotion of 105,304 users who posted 189,012 tweets on the U.S. immigration and border security issues in November 2014. The results provide strong implication for understanding social actions and for collecting social intelligence for security informatics. This research should contribute to helping decision makers and security personnel to use public emotion effectively to develop appropriate strategies.

关键词： Media Security Informatics Communities Public policy Couplings

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Data-Driven Interval Robust Optimization Method of Vpp Bidding Strategy in Spot Market Under Multiple Uncertainties

SSRN

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SSRN 2024年

作者： Ma, Ying Li, Zhen Liu, Ruyi Liu, Bin Yu, Samson S. Liao, Xiaozhong Shi, Peng School of Automation Key Laboratory for Intelligent Control & Decision on Complex Systems Beijing Institute of Technology Beijing100081 China School of Mathematics and Statistics University of Sydney NSW2006 Australia Beijing102249 China School of Engineering Deakin University VIC3216 Australia School of Electrical and Electronic Engineering Univerty of Adelaide SA5005 Australia Research and Innovation Center Obuda University Budapest1034 Hungary

Virtual power plant (VPP) participating in the spot market can improve the flexibility of new-type power systems with the increasing penetration of renewable energy. However, multiple uncertainties on the market side, load side and generation side affect the bidding plans and operation efficiency of a VPP. In this paper, a two-stage interval robust optimization model is established to optimize the bidding plans of a VPP including gas turbine, energy storage, photovoltaic (PV) and electric vehicles (EVs). Interval numbers generated by the data-driven model are used to describe the uncertainty and correlation of electricity price in the spot market. Uncertainty sets are employed to describe the uncertainties in the number of EVs and PV power generation. The objective of the model is to jointly optimize the cost of VPP participating in the spot market considering arbitrage opportunity. The objective function containing interval numbers is transformed into a solvable form based on a ranking method considering pessimistic decisions. To solve this model, an improved column-and-constraint generation (C&CG) algorithm is developed based on the combination method of genetic algorithm (GA) and solver Gurobi. The results show that the interval numbers of electricity price generated by the data-driven model proposed in this paper can reduce the cost fluctuation of VPP by 3.2%. The two-stage interval robust model proposed in this paper can reduce the cost of VPP by 1.4% compared with the conventional single-stage robust method, and lessen the cost by 35.2% compared with the robust method that does not consider arbitrage opportunities in day-ahead and real-time markets. The improved C&CG algorithm presents superior performance in convergence and accuracy. Compared to the stochastic optimization method that generates n scenarios, the computational time of the model with the interval optimization method introduced in this paper can be reduced to 1∕n. Results from case studies illust

关键词： Digital storage

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Data-Driven Interval Robust Optimization Method of Vpp Bidding Strategy in Spot Market Under Multiple Uncertainties

SSRN

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SSRN 2024年

Virtual power plant (VPP) participating in the spot market can improve the flexibility of new-type power systems with the increasing penetration of renewable energy. However, multiple uncertainties on the market side, load side and generation side affect the bidding plans and operation efficiency of a VPP. In this paper, a two-stage interval robust optimization model is established to optimize the bidding plans of a VPP including gas turbine, energy storage, photovoltaic (PV) and electric vehicles (EVs). Interval numbers generated by the data-driven model are used to describe the uncertainty and correlation of electricity price in the spot market. Uncertainty sets are employed to describe the uncertainties in the number of EVs and PV power generation. The objective of the model is to jointly optimize the cost of VPP participating in the spot market considering arbitrage opportunity. The objective function containing interval numbers is transformed into a solvable form based on a ranking method considering pessimistic decisions. To solve this model, an improved column-and-constraint generation (C&CG) algorithm is developed based on the combination method of genetic algorithm (GA) and solver Gurobi. The results show that the interval numbers of electricity price generated by the data-driven model proposed in this paper can reduce the cost fluctuation of VPP by 3.2%. The two-stage interval robust model proposed in this paper can reduce the cost by 1.4% compared with the single-stage robust method, and lessen the cost by 35.2% compared with the robust method that does not consider arbitrage opportunities in day-ahead and real-time markets. The improved C&CG algorithm presents superior performance in convergence and accuracy. Compared to the stochastic optimization method that generates n scenarios, the computational time of the model with the interval optimization method introduced in this paper can be reduced to 1∕n. Results from case studies illustrate the effectivene

关键词： Digital storage

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Distributed Online Convex Optimization with Adversarial Constraints: Reduced Cumulative Constraint Violation Bounds under Slater’s Condition

arXiv

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arXiv 2023年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Hong, Yiguang Chai, Tianyou Johansson, Karl H. Lab for Information & Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems China The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm10044 Sweden

This paper considers distributed online convex optimization with adversarial constraints. In this setting, a network of agents makes decisions at each round, and then only a portion of the loss function and a coordinate block of the constraint function are privately revealed to each agent. The loss and constraint functions are convex and can vary arbitrarily across rounds. The agents collaborate to minimize network regret and cumulative constraint violation. A novel distributed online algorithm is proposed and it achieves an O(T max{c,1−c}) network regret bound and an O(T 1−c/2) network cumulative constraint violation bound, where T is the number of rounds and c ∈ (0, 1) is a user-defined trade-off parameter. When Slater’s condition holds (i.e, there is a point that strictly satisfies the inequality constraints), the network cumulative constraint violation bound is reduced to O(T 1−c). Moreover, if the loss functions are strongly convex, then the network regret bound is reduced to O(log(T)), and the network cumulative constraint violation bound is reduced to O(√log(T)T) and O(log(T)) without and with Slater’s condition, respectively. To the best of our knowledge, this paper is the first to achieve reduced (network) cumulative constraint violation bounds for (distributed) online convex optimization with adversarial constraints under Slater’s condition. Finally, the theoretical results are verified through numerical simulations. Copyright © 2023, The Authors. All rights reserved.

关键词： Economic and social effects

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Procedure Recognition by Knowledge-Driven Segmentation in Robotic-Assisted Vitreoretinal Surgery

Procedure Recognition by Knowledge-Driven Segmentation in Ro...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Zhen Li Yawen Deng Qiang Ye Weihong Yu Haoxiang Qi Yaliang Liu Zhangguo Yu Gui-Bin Bian School of Electronic and Information Engineering Tongji University Shanghai China Institute of Automation Chinese Academy of Sciences Beijing China School of Mechatronic Engineering Beijing Institute of Technology Beijing China Department of Ophthalmology Peking Union Medical College Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China Key Laboratory of Biomimetic Robots and Systems Ministry of Education Beijing China International Joint Research Laboratory of Biomimetic Robots and Systems Ministry of Education Beijing China State Key Laboratory of Intelligent Control and Decision of Complex System Beijing China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Internal limiting membrane (ILM) peeling is a vital vitreoretinal surgery procedure. However, due to the thickness of just 1-2 micrometers and the intricacies associated with its varying density and adhesion, the difficulty of manipulation exceeds the physiological limits of human perception and operation. Surgical robot is characterized by high precision and stability. However, navigating intricate intraocular environments and handling minuscule high-precision areas remain enormous challenges. These include issues of uneven lighting, field-of-view loss, and motion blur. This paper proposed a perception method named ‘Multimodal Surgical Process Recognition based on Domain Knowledge and Segmentation (MSPR-DKS),’ designed to address these challenges and provide input for the precise control of robots. Moreover, a comprehensive dataset focused on ILM peeling during macular hole surgeries was established. Experimental results underscore the efficacy of this approach, with segmentation accuracies exceeding 99.27% for instruments and macular holes and an average accuracy of 98.97% in recognizing surgical processes. This study paves the way for leveraging domain knowledge and image segmentation to improve robot-assisted manipulation of soft tissues in ophthalmology.

关键词： Image segmentation Visualization Accuracy Medical robotics Motion segmentation Surgery Lighting

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Stability of linear systems with sawtooth input delay and predictor-based controller

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Automatica 2020年 117卷

作者： Haoyuan Sun Jian Sun Jie Chen School of Automation Beijing Institute of Technology Beijing 100081 PR China Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing 100081 PR China School of Electronic and Information Engineering Tongji University Shanghai 200082 PR China

In this paper, the stability of linear systems with sawtooth input delay widely existing in networked systems and predictor-based controller is considered. Under the assumption that there exists an instant where the input delay is zero, a necessary and sufficient condition is obtained to guarantee the exponential stability of the closed-loop system, that is, the closed-loop system is stable if and only if the matrix A + B K is Hurwitz. Two simulation examples are given to confirm the validity of the obtained results.

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