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arXiv 2022年

作者： Cheng, Bin Lv, Yuezu Li, Zhongkui Duan, Zhisheng Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai200120 China Advanced Research Institute of Multidisciplinary Sciences Beijing Institute of Technology Beijing100081 China Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing314000 China State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Peking University Beijing100871 China

This paper studies the consensus control problem faced with three essential demands, namely, discrete control updating for each agent, discrete-time communications among neighboring agents, and the fully distributed fashion of the controller implementation without requiring any global information of the whole network topology. Noting that the existing related results only meeting one or two demands at most are essentially not applicable, in this paper we establish a novel framework to solve the problem of fully distributed consensus with discrete communication and control. The first key point in this framework is the design of controllers that are only updated at discrete event instants and do not depend on global information by introducing time-varying gains inspired by the adaptive control technique. Another key point is the invention of novel dynamic triggering functions that are independent of relative information among neighboring agents. Under the established framework, we propose fully distributed state-feedback event-triggered protocols for undirected graphs and also further study the more complexed cases of output-feedback control and directed graphs. Finally, numerical examples are provided to verify the effectiveness of the proposed event-triggered protocols. Copyright © 2022, The Authors. All rights reserved.

关键词： Directed graphs

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Adaptive control for asynchronously switched fractional-order multi-agent systems

Adaptive control for asynchronously switched fractional-orde...

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第三十九届中国控制会议

作者： Xiongliang Zhang Shiqi Zheng Feng Liu Mingyuan Zhao Yuan Sun School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems School of Electrical and Electronic Engineering The University of Adelaide

In this paper, we discuss the adaptive controller design for a class of fractional-order（FO） multi-agent systems（MAS）.All the agents are described by FO pure-feedback nonlinear systems. This is more general than the existing works, which only considered strict-feedback or integer-order nonlinear systems. Meanwhile, asynchronous switching are considered in FO MAS,meaning the agents have heterogeneous switching dynamics. A new neural network based adaptive controller is proposed for the considered FO MAS, which can make the consensus tracking error converge to a small neighborhood of origin. Simulation verifies the results obtained.

关键词： Consensus tracking fractional-order multi-agent systems asynchronously switched systems

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PV Maximum Power-point Tracking Using Modified Particle Swarm optimization under Partial Shading Conditions

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Chinese Journal of Electrical Engineering 2020年第4期6卷 106-121页

作者： Al-wesabi Ibrahim M.B.Shafik Min Ding Mohammad Abu Sarhan Zhijian Fang Ahmed.G.Alareqi Tariq Al’moqri Ayman.M.Al-Rassas School of Automation China University of GeoscienceWuhan 430074China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074China School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China School of Oil and Natural Gas Engineering China University of GeosciencesWuhan 430074China School of Mathematics and Physics China University of GeoscienceWuhan 430074China School of Petroleum Engineering China University of PetroleumEast ChinaQingdao 266580China

A novel maximum power-point tracking approach is proposed based on studies investigating the output characteristics of photovoltaic(PV)systems under partial shading *** existence of partially shaded conditions leads to the presence of several peaks on PV curves,which decrease the efficiency of conventional ***,the proposed algorithm,which is based on the modified particle-swarm optimization(MPSO)technique,increases the output power of PV systems under such abnormal conditions and has a better performance compared to other *** proposed method is examined under several scenarios for partial shading condition and non-uniform irradiation levels using Matlab,and to investigate its effectiveness adequately,the results of the proposed method are compared with those of the neural network *** experimental results show that the proposed method can decrease the interference of the local maximum power-point to cause the PV system to operate at a global maximum *** efficiency of the MPSO is achieved with the least number of steady-state oscillations under partial shading conditions compared with the neural network method.

关键词： Photovoltaic(PV) maximum power point tracking(MPPT) step-up converter artificial neural networks(ANNs) particle-swarm optimization(PSO)

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KSLIC: K-mediods clustering based simple linear iterative clustering 1

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2nd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2019

作者： Zhang, Houwang Zhu, Yuan School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China

ISBN: (数字)9783030317232

ISBN: (纸本)9783030317225

Simple Linear Iterative Clustering (SLIC) is one of the most excellent superpixel segmentation algorithms with the most comprehensive performance and is widely used in various scenes of production and living. As a preprocessing step in image processing, superpixel segmentation should meet various demands in real life as much as possible, but SLIC is highly sensitive to noise. In this paper, a K-mediods clustering based simple linear iterative clustering (KSLIC) is proposed, which replaces the K-means clustering in SLIC with a modified local K-mediods clustering. To evaluate the performance of KSLIC, we test it on BSD500 benchmark dataset. The results show that it outperforms SLIC in terms of different noise environments including Gaussian noise, multiplicative noise and salt and pepper noise. © Springer Nature Switzerland AG 2019.

关键词： Salt and pepper noise

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Chinese Calligraphy Character Generating via CGAN with a Multi-subnet Parallel and Cascade Generator

Chinese Calligraphy Character Generating via CGAN with a Mul...

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第三十九届中国控制会议

作者： Xiaoxue Zhou Ziying Zhang Xin Chen Mengxi Qin School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Arts and Communication China University of Geosciences

Chinese character generation is one of the key technologies in Chinese character intelligent design. Most generation methods cannot solve the situation where the generated Chinese character strokes have artifact or even miss. The main reason is that the generator is not strong enough. Therefore, in this paper, we propose a multi-subnet parallel and cascade generator, which combines the conditional generation adversarial network（CGAN） to realize the generation of calligraphy Chinese *** method divides a Chinese character generation task into several parts undertaken by each sub-generator, and the output of each sub-generator will contain certain image information, which will increase with the rise of sub-generator hierarchy. It allows each sub-generator to focus on its own task, generating more realistic characters eventually. Finally, the test set performs better than the CGAN with a single generator.

关键词： CGAN Chinese calligraphy character generation Multi-subnet parallel and cascade generator

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Bitcoin Fee Decisions in Transaction Confirmation Queueing Games under Limited Multi-Priority Rule

Bitcoin Fee Decisions in Transaction Confirmation Queueing G...

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2019 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2019

作者： Li, Juanjuan Yuan, Yong Wang, Fei-Yue State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China

ISBN: (纸本)9781728128535

In the Bitcoin system, transaction fees serve not only as the fundamental economic incentive to stimulate miners, but also as an important tuner for the Bitcoin system to define the priorities in the transaction confirmation process. In this paper, we aim to study the priority rules for queueing transactions based on their associated fees, and in turn users' decision-making in formulating their fees in the transaction confirmation queueing game. Based on the queueing theory, we first analyzed the waiting time of users under non-preemptive limited multi-priority (LMP) rule, which is formulated to adjust users' waiting time over different priorities. We then established a game-theoretical model, and analyze users' equilibrium fee decisions. Towards the end, we conducted computational experiments to validate the theoretical analysis. Our research findings can not only help understand users' fee decisions under the LMP rule, but also offer useful managerial insights in optimizing the queueing rules of Bitcoin transactions. © 2019 IEEE.

关键词： Blockchain

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A Feedback-based Print Quality Improving Strategy for FDM 3D Printing: An Optimal Design Approach

Research Square

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Research Square 2021年

作者： Tami, Tariku Sinshaw Xiong, Gang Fang, Qihang Dong, Xisong Shen, Zhen Wang, Fei-Yue The State Key Laboratory for Management and Control of Complex Systems Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan523808 China

Fused deposition modelling (FDM) 3D printing, as a supporting technology in social manufacturing and cloud manufacturing, is a rapidly growing technology in the era of industry 4.0. It produces objects with the layer-by-layer material accumulation technique. However, qualitative uncertainties are the common challenges yet. In order to assure print quality, studying the error causing parameters and minimizing their effects is important. This paper presents a feedback-based error compensation strategy, which integrates fuzzy inference system and grey wolf optimization algorithm. The objectives are twofold. First, the possible errors in FDM 3D printing are discussed in detail and optimal error causing parameters are obtained in percentage. This is used to understand the effects of the printing errors in every phase of the 3D printing process. From the nine optimization configuration trials used, Config-6 that has 100 number of iterations and 60 wolves is selected due to its higher convergence speed and best fitness value. The integral absolute error (IAE) is used as an objective function and the global minimum is achieved in the iteration interval [86, 100]. The outputs of this optimization problem is used to achieve the next objective. Second, a closed-loop quality monitoring approach comprising of inner-loops and an outer-loop is utilized. The three inner-loops are used to monitor the errors during pre-printing, printing, and post-printing, respectively. The outer-loop, on the other hand, is responsible for monitoring the aggregated errors in all the three 3D printing phases. The error compensation system simulation in Matlab is run for 10 seconds, and the results show that the "normal" range deformation factors are reached within less than 2 seconds for the inner-loops, whereas the outer-loop deformation factor is achieved within 7 seconds. The responses are within the acceptable time range. © 2021, CC BY.

关键词： 3D printers

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Security of Networked control systems with Incomplete Information Based on Game Theory

Security of Networked Control Systems with Incomplete Inform...

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第三十九届中国控制会议

作者： Lei Gao Jian Sun Jun Li Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing Institute of Technology Chongqing Innovation Center China Industrial Control Systems Cyber Emergency Response Team

The security problem of networked control systems（NCSs） suffering denial of service（DoS） attacks with incomplete information is investigated in this paper. Data transmission among different components in NCSs may be blocked due to DoS attacks. We use the concept of security level to describe the degree of security of different components in an NCS. Intrusion detection system（IDS） is used to monitor the invalid data generated by DoS attacks. At each time slot, the defender considers which component to monitor while the attacker considers which place for invasion. A one–shot game between attacker and defender is built and both the complete information case and the incomplete information case are considered. Furthermore, a repeated game model with updating beliefs is also established based on the Bayes’ rule. Finally, a numerical example is provided to illustrate the effectiveness of the proposed method.

关键词： Networked control systems Security DoS attacks Game theory Incomplete information

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Dynamic power systems line outage detection using particle filter and partially observed states

arXiv

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arXiv 2021年

作者： Yang, Xiaozhou Chen, Nan Zhai, Chao The Department of Industrial Systems Engineering and Management National University of Singapore Singapore117576 Singapore ETH Zurich Future Resilient Systems Singapore-ETH Centre 1 CREATE Way CREATE Tower Singapore138602 Singapore The School of Automation China University of Geosciences Wuhan430074 China The Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China

Real-time transmission line outage detection is difficult because of partial phasor measurement unit (PMU) deployment and varying outage signal strength. Existing detection approaches focus on monitoring PMU-measured nodal algebraic states, i.e., voltage phase angle and magnitude. The success of such approaches, however, is largely predicated on strong outage signals and the presence of PMUs in the outage location's vicinity. To overcome these limitations, a unified framework is proposed in this work by utilizing both nodal voltage information and generator dynamic states, e.g., rotor angular position. The proposed scheme is shown to be faster and more robust to unknown outage locations through the incorporation of generator dynamics. Using the IEEE 39-bus system simulation data, the proposed scheme's properties and performances compared to existing approaches are presented. The new approach could help improve operators' real-time situational awareness by detecting outages faster and providing a breakdown of outage signals for diagnostic purposes, making future power systems more resilient. Copyright © 2021, The Authors. All rights reserved.

关键词： Monte Carlo methods

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A Reinforcement-Learning-Based Evolutionary Algorithm Using Solution Space Clustering For Multimodal optimization Problems

A Reinforcement-Learning-Based Evolutionary Algorithm Using ...

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Congress on Evolutionary Computation

作者： Hai Xia Changhe Li Sanyou Zeng Qingshan Tan Junchen Wang Shengxiang Yang School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan China School of Computer Science and Informatics De Montfort University Leicester United Kingdom

In evolutionary algorithms, how to effectively select interactive solutions for generating offspring is a challenging problem. Though many operators are proposed, most of them select interactive solutions (parents) randomly, having no specificity for the features of landscapes in various problems. To address this issue, this paper proposes a reinforcement-learning-based evolutionary algorithm to select solutions within the approximated basin of attraction. In the algorithm, the solution space is partitioned by the k-dimensional tree, and features of subspaces are approximated with respect to two aspects: objective values and uncertainties. Accordingly, two reinforcement learning (RL) systems are constructed to determine where to search: the objective-based RL exploits basins of attraction (clustered subspaces) and the uncertainty-based RL explores subspaces that have been searched comparatively less. Experiments are conducted on widely used benchmark functions, demonstrating that the algorithm outperforms three other popular multimodal optimization algorithms.

关键词： Uncertainty Clustering algorithms Evolutionary computation Reinforcement learning Benchmark testing Search problems Feature extraction

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