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Observer-based finite time H ∞ control of nonlinear discrete time-varying systems with an adaptive event-triggered Mechanism

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Journal of the Franklin Institute 2020年第16期357卷 11668-11689页

作者： Menghua Chen Hamid Reza Karimi Jian Sun School of Automation Beijing Institute of Technology Beijing 100081 China Department of Mechanical Engineering Politecnico di Milano Milan 20156 Italy Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing 100081 China

The problem of finite time stability and synthesis is discussed for nonlinear discrete time-varying systems by using the T-S fuzzy model. To reduce the communication network resources, two adaptive event-triggered mechanisms (AETMs) are taken into consideration in sensor-to-observer (S/O) and observer-to-controller (O/C) channels. The thresholds of the AETMs are adjusted according to the estimation error rather than some fixed ones. Sufficient conditions for developing an observer-based time-varying control are obtained, which ensure the time-varying error system to be finite time stable and satisfy the H ∞ performance simultaneously. Moreover, using the singular value decomposition technique, recursive linear matrix inequalities conditions are obtained for computing the gains of the controller and observer. Finally, the superiority and applicability of the proposed method are demonstrated by examples.

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Energy-Efficient Multi-UAV-Enabled MEC systems with Space-Air-Ground Integrated Networks

arXiv

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arXiv 2024年

作者： Liu, Wenchao Zhang, Xuhui Ren, Jinke Shen, Yanyan Wang, Shuqiang Yang, Bo Guan, Xinping Cui, Shuguang Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong518055 China Southern University of Science and Technology Guangdong518055 China The Shenzhen Future Network of Intelligence Institute The School of Science and Engineering The Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Guangdong Shenzhen518172 China The Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong518055 China Shenzhen University of Advanced Technology Guangdong518055 China The Department of Automation The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai200240 China The School of Science and Engineering The Shenzhen Future Network of Intelligence Institute The Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Guangdong Shenzhen518172 China

With the development of artificial intelligence integrated next-generation communication networks, mobile users (MUs) are increasingly demanding the efficient processing of computation-intensive and latency-sensitive tasks. However, existing mobile computing networks struggle to support the rapidly growing computational needs of the MUs. Fortunately, space-air-ground integrated network (SAGIN) supported mobile edge computing (MEC) is regarded as an effective solution, offering the MUs multi-tier and efficient computing services. In this paper, we consider an SAGIN supported MEC system, where a low Earth orbit satellite and multiple unmanned aerial vehicles (UAVs) are dispatched to provide computing services for MUs. An energy efficiency maximization problem is formulated, with the joint optimization of the MU-UAV association, the UAV trajectory, the task offloading decision, the computing frequency, and the transmission power control. Since the problem is non-convex, we decompose it into four subproblems, and propose an alternating optimization based algorithm to solve it. Simulation results confirm that the proposed algorithm outperforms the benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Resource allocation

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Leader-Tracking in a Shape-Preserving Formation With Bearing-Only Measurements

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IFAC-PapersOnLine 2020年第2期53卷 5958-5963页

作者： Ziwen Yang Shanying Zhu Cailian Chen Gang Feng Department of Automation Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China. Department of Biomedical Engineering City University of Hong Kong Kowloon Hong Kong SAR.

This paper studies the problem of shape-preserving formation control for multi-agent systems where only bearing measurements are available. Most existing methods to solve the problems assume that accurate global or local position measurements of agents are available, or use estimators to get these information without analysis of estimators’ effects on the closed loop system stability. In this work, we propose a framework integrating an estimator to get relative positions using bearing-only measurements and an estimator-based controller to achieve tracking a pair of leaders in a formation with a preserved shape. The estimator is designed by exploiting orthogonality to cope with the high non-linearity of bearing measurements, based on which the controller is designed with the estimated relative positions. With rigorous theoretical analysis of the closed-loop system, we characterize the leaders which can be tracked by followers in a shape-preserving formation using bearing-only measurements, and the asymptotic stability of the closed-loop system can be guaranteed. Simulations testify the effectiveness of the proposed framework.

关键词： Shape-preserving control integrated estimation control leader-follower formation bearing-only measurements

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An Efficient Evolutionary Algorithm for High-Speed Train Rescheduling Under a Partial Station Blockage

SSRN

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SSRN 2023年

作者： Wang, Rongsheng Zhang, Qi Dai, Xuewu Yuan, Zhiming Zhang, Tao Ding, Shuxin Jin, Yaochu China Academy of Railway Sciences Corporation Limited Beijing100081 China Signal and Communication Research Institute China Academy of Railway Sciences Corporation Limited Beijing100081 China Train Operation Control Laboratory for High-Speed Railway National Engineering Research Center of System Technology for High-Speed Railway and Urban Rail Transit Beijing100081 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Faculty of Technology Bielefeld University BielefeldD-33615 Germany Department of Computer Science University of Surrey GuildfordGU2 7XH United Kingdom

This paper investigates the high-speed train rescheduling (HSTR) problem under a partial station blockage and proposes an efficient problem-specific strengthen elitist genetic algorithm (PS-SEGA) for HSTR. An HSTR model that is subject to train operation constraints is established to minimize the total train delay. The PS-SEGA framework consists of the following main steps. Firstly, a permutation-based encoding method is developed to define an efficient search space based on a train departure sequence. A decoding method is employed to output the rescheduled timetable and objective value based on the departure sequence. Moreover, a hybrid initialization method involving an efficient heuristic strategy (EHS) is put forward to accelerate the convergence speed of Evolutionary algorithms (EAs). Using problem-specific knowledge, EHS generates an efficient and feasible solution for the initial population. Finally, a restart strategy is presented to maintain genetic diversity. Compared with other advanced EAs and their improved variants also using the PS-SEGA framework, experimental results demonstrate the effectiveness of the proposed PS-SEGA for addressing HSTR scenarios under the partial station blockage. As for the scenarios that CPLEX can not obtain optimal solutions within 10 minutes, PS-SEGA can all provide quasi-optimal solutions in real-time. Furthermore, compared with the other two strategies (i.e., First-Scheduled-First-Served and EHS), PS-SEGA can find the best departure sequence that can output the timetable with a quasi-optimal solution. © 2023, The Authors. All rights reserved.

关键词： Genetic algorithms

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Unsupervised learning of depth, optical flow and pose with occlusion from 3D geometry

arXiv

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arXiv 2020年

作者： Wang, Guangming Zhang, Chi Wang, Hesheng Wang, Jingchuan Wang, Yong Wang, Xinlei Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Beijing Institute of Control Engineering Beijing100080 China DeepBlue Academy of Sciences Shanghai200240 China

—In autonomous driving, monocular sequences contain lots of information. Monocular depth estimation, camera ego-motion estimation and optical flow estimation in consecutive frames are high-profile concerns recently. By analyzing tasks above, pixels in the first frame are modeled into three parts: the rigid region, the non-rigid region, and the occluded region. In joint unsupervised training of depth and pose, we can segment the occluded region explicitly. The occlusion information is used in unsupervised learning of depth, pose and optical flow, as the image reconstructed by depth, pose and flow will be invalid in occluded regions. A less-than-mean mask is designed to further exclude the mismatched pixels which are interfered with motion or illumination change in the training of depth and pose networks. This method is also used to exclude some trivial mismatched pixels in the training of the flow net. Maximum normalization is proposed for smoothness term of depth-pose networks to restrain degradation in textureless regions. In the occluded region, as depth and camera motion can provide more reliable motion estimation, they can be used to instruct unsupervised learning of flow. Our experiments in KITTI dataset demonstrate that the model based on three regions, full and explicit segmentation of occlusion, rigid region and non-rigid region with corresponding unsupervised losses can improve performance on three tasks significantly. Copyright © 2020, The Authors. All rights reserved.

关键词： Unsupervised learning

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Topology inference for multi-agent cooperation under unmeasurable latent input

arXiv

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arXiv 2020年

作者： Jiao, Qing Li, Yushan He, Jianping Shi, Ling Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong

Topology inference is a crucial problem for cooperative control in multi-agent systems. Different from most prior works, this paper is dedicated to inferring the directed network topology from the observations that consist of a single, noisy and finite time-series system trajectory, where the cooperation dynamics is stimulated with the initial network state and the unmeasurable latent input. The unmeasurable latent input refers to intrinsic system signal and extrinsic environment interference. Considering the time-invariant/varying nature of the input, we propose two-layer optimization-based and iterative estimation-based topology inference algorithms (TO-TIA and IE-TIA), respectively. TO-TIA allows us to capture the separability of global agent state and eliminates the unknown influence of time-invariant input on system dynamics. IE-TIA further exploits the identifiability and estimability of more general time-varying input and provides an asymptotic solution with desired convergence properties, with higher computation cost compared with TO-TIA. Our novel algorithms relax the dependence of observation scale and leverage the empirical risk reformulation to improve the inference accuracy in terms of the topology structure and edge weight. Comprehensive theoretical analysis and simulations for various topologies are provided to illustrate the inference feasibility and the performance of the proposed algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Multi agent systems

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食品系统与气候变化、空气污染之间的相互作用综述（英文）

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engineering 2025年第1期 215-233页

作者： Chaopeng Hong Rui Zhong Mengyao Xu Peidong He Huibin Mo Yue Qin Danna Shi Xinlei Chen Kebin He Qiang Zhang Shenzhen Key Laboratory of Ecological Remediation and Carbon Sequestration Institute of Environment and Ecology Shenzhen International Graduate School Tsinghua University State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex College of Environmental Sciences and Engineering Peking University Institute of Data and Information Shenzhen International Graduate School Tsinghua University State Key Joint Laboratory of Environment Simulation and Pollution Control School of Environment Tsinghua University Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System Science Tsinghua University

Food systems are deeply affected by climate change and air pollution, while being key contributors to these environmental challenges. Understanding the complex interactions among food systems, climate change, and air pollution is crucial for mitigating climate change, improving air quality, and promoting the sustainable development of food systems. However, the literature lacks a comprehensive review of these interactions, particularly in the current phase of rapid development in the field. To address this gap, this study systematically reviews recent research on the impacts of climate change and air pollution on food systems, as well as the greenhouse gas and air pollutant emissions from agri-food systems and their contribution to global climate change and air pollution. In addition, this study summarizes various strategies for mitigation and adaptation, including adjustments in agricultural practices and food supply chains. Profound changes in food systems are urgently needed to enhance adaptability and reduce emissions. This review offers a critical overview of current research on the interactions among food systems, climate change, and air pollution and highlights future research directions to support the transition to sustainable food systems.

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Corrigendum to "A comprehensive analysis of submarine groundwater discharge and nutrient fluxes in the Bohai Sea, China"

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Water research 2025年第PT B期268卷 122659页

作者： Xiaolang Zhang Hailong Li Xuejing Wang Xingxing Kuang Yan Zhang Kai Xiao Chao Xu Department of Geosciences Florida Atlantic University Boca Raton FL 33431 USA. State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control School of Environmental Science and Engineering Southern University of Science and Technology Shenzhen 518055 China. Electronic address: lihailong@***. Institute of Surface-Earth System Science School of Earth System Science Tianjin University Tianjin 300072 China. State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control School of Environmental Science and Engineering Southern University of Science and Technology Shenzhen 518055 China. State Key Laboratory of Biogeology and Environmental Geology and School of Water Resources and Environment China University of Geosciences-Beijing Beijing 100083 China. Department of Geosciences Texas Tech University Texas 79409 USA.

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Combined Nonlinear control of Non-Affine MIMO system with Input and State Delays

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Journal of Physics: Conference Series 2021年第1期1901卷

作者： E L Eremin L V Nikiforova E A Shelenok Information and Control Systems Department Amur State University Blagoveshchensk Russia Automation and System Engineering Department Pacific National University Khabarovsk Russia

The article studies algorithms synthesis of a multiply connected system for controlling non-affine plants, the state variables of which are not available for direct measurement. It is shown that the decentralized control law constructed on the basis of the hyperstability criterion eliminates the influence of non-linearities and compensates external and parametric perturbations in the tracking multiply connected system with the required accuracy.

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Vertical Transport of Ultrafine Particles and Turbulence Evolution Impact on New Particle Formation at the Surface & Canton Tower

SSRN

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SSRN 2023年

作者： Wu, Hao Li, Zhanqing Hai, Shangfei Gao, Yang Jiang, Jingkun Zhao, Bin Cribb, Maureen Zhang, Dongmei Pu, Dongyang Liu, Mengqi Wang, Chunling Lan, Jing Wang, Yuying Key Laboratory of China Meteorological Administration Atmospheric Sounding School of Electrical Engineering Chengdu University of Information Technology Chengdu610225 China Department of Atmospheric and Oceanic Science University of Maryland College ParkMD20742 United States College of Oceanic and Atmospheric Sciences Ocean University of China Qingdao266100 China Frontiers Science Center for Deep Ocean Multispheres and Earth System Key Laboratory of Marine Environmental Science and Ecology Ministry of Education Ocean University of China Qingdao266100 China School of Environment State Key Joint Laboratory of Environment Simulation and Pollution Control Tsinghua University Beijing100084 China State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex Beijing100084 China College of Global Change and Earth System Science Beijing Normal University Beijing100875 China Guangzhou Climate and Agrometeorology Center Guangzhou511430 China Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration Nanjing University of Information Science & Technology Nanjing210044 China

New particle formation (NPF) is a key process occurring in the planetary boundary layer (PBL). Newly formed particles are an important source of aerosols and cloud condensation nuclei (CCN) that influence clouds and climate, while the distribution of these new particles at different altitudes has rarely been studied. In-situ measurements of ultrafine particles (UFP) observed at the ground and at the top of the Canton Tower (454 m) located in downtown Guangzhou in southern China were analyzed using the measurements of multiple meteorological and physicochemical quantities, as both observed during a field campaign and simulated with the WRF-chem model. We found that turbulence and NPF characters vary considerably with heights, with UFP concentration diminishing by half from the surface to the tower top. This indicates the UFP transports upward from the ground in the lower boundary layer. A consistent relationship is established between the occurrences of NPF and the evolution of turbulence. The correlation between the exchange ratio at the tower top has correlated well with nucleation growth, suggesting that turbulence can play an important role in the episodes of NPF growth, whose growth rate is closely related to the turbulence exchange ratio, effectively dictating the ultrafine particle concentration before and during the lockdown period. A new mechanism is thus hypothesized: NPF happens eailer near the surface and grows faster at the upper PBL, attributed to condensable vapors being transported by turbulent vertical mixing in the boundary layer. Model simulations using the WRF-Chem model reveal that the exchange ratio changed the NPF parameters, supporting the proposed mechanism that the evolution of the PBL variation has a significant impact on NPF, which should not be omitted in the NPF research, since this physical factor could be a dominant one in the NPF mechanism. © 2023, The Authors. All rights reserved.

关键词： Turbulence

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