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Two-Stage Robust Optimization Under Decision Dependent Uncertainty

IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1295-1306页

作者： Yunfan Zhang Feng Liu Yifan Su Yue Chen Zhaojian Wang João P.S.Catalão State Key Laboratory of Power System and Generation Equipment the Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Department of Mechanical and Automation Engineering the Chinese University of Hong KongHong Kong SARChina Ministry of Education Key Laboratory of System Control and Information Processing the Department of AutomationShanghai Jiao Tong Universityand also with Shanghai Engineering Research Center of Intelligent Control and ManagementShanghai 200240China Faculty of Engineering of the University of Porto and Institute for Systems and Computer Engineering Technology and Science(INESC TEC)Porto 4200-465Portugal IEEE

In the conventional robust optimization(RO)context,the uncertainty is regarded as residing in a predetermined and fixed uncertainty *** many applications,however,uncertainties are affected by decisions,making the current RO framework *** paper investigates a class of two-stage RO problems that involve decision-dependent *** introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem.A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision *** computational tractability,robust feasibility and optimality,and convergence performance of the proposed algorithm are guaranteed with theoretical *** motivating application examples that feature the decision-dependent uncertainties are ***,the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem.

关键词： Benders decomposition decision-dependent uncertainty endogenous uncertainty robust optimization(RO)

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Modeling the dynamic impacts of maritime network blockage on global supply chains

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The Innovation 2024年第4期5卷 103-109,102页

作者： Shen Qu Yunlei She Qi Zhou Jasper Verschuur Lu-Tao Zhao Huan Liu Ming Xu Yi-Ming Wei Center for Energy and Environmental Policy Research Beijing Institute of TechnologyBeijing 100081China School of Management Beijing Institute of TechnologyBeijing 100081China Beijing Key Lab of Energy Economics and Environmental Management Beijing 100081China Oxford Programme for Sustainable Infrastructure Systems(OPSIS) Environmental Change InstituteUniversity of OxfordOX12JD OxfordUK State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijing 100084China State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex Beijing 100084China School of Environment Tsinghua UniversityBeijing 100084China

Recent phenomena such as pandemics,geopolitical tensions,and climate change-induced extreme weather events have caused transportation network interruptions,revealing vulnerabilities in the global supply chain.A salient example is the March 2021 Suez Canal blockage,which delayed 432 vessels carrying cargo valued at$92.7 billion,triggering widespread supply chain *** ability to model the spatiotemporal ramifications of such incidents remains *** fill this gap,we develop an agent-based complex network model integrated with frequently updated maritime *** Suez Canal blockage is taken as a case *** results indicate that the effects of such blockages go beyond the directly affected countries and *** Suez Canal blockage led to global losses of about$136.9($127.5–$147.3)billion,with India suffering 75%of these *** losses show a nonlinear relationship with the duration of blockage and exhibit intricate trends post *** proposed model can be applied to diverse blockage scenarios,potentially acting as an earlyalert system for the ensuing supply chain ***,high-resolution daily data post blockage offer valuable insights that can help nations and industries enhance their resilience against similar future events.

关键词： chains valued extreme

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Cooperative Filtering with Range Measurements: A Distributed Constrained Zonotopic Method 7th

Cooperative Filtering with Range Measurements: A Distribute...

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Ding, Yu Cong, Yirui Wang, Xiangke Cheng, Long College of Intelligence Science and Technology National University of Defense Technology Changsha410073 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9789819733316

This article studies the distributed estimation problem of a multi-agent system with bounded absolute and relative range measurements. Parts of the agents are with high-accuracy absolute measurements, which are considered as anchors;the other agents utilize low-accuracy absolute and relative range measurements, each derives an uncertain range that contains its true state in a distributed manner. Different from previous studies, we design a distributed algorithm to handle the range measurements based on extended constrained zonotopes, which has low computational complexity and high precision. With our proposed algorithm, agents can derive their uncertain range sequentially along the chain topology, such that agents with low-accuracy sensors can benefit from the high-accuracy absolute measurements of anchors and improve the estimation performance. Simulation results indicate the effectiveness of our algorithm and verify our method can significantly improve the estimation accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Multi agent systems

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Multi-Blockchain Based Data Trading Markets With Novel Pricing Mechanisms

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IEEE/CAA Journal of Automatica Sinica 2023年第12期10卷 2222-2232页

作者： Juanjuan Li Junqing Li Xiao Wang Rui Qin Yong Yuan Fei-Yue Wang IEEE the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao 266590China the School of Artificial Intelligence Anhui UniversityHefei 266114 the Qingdao Academy of Intelligent Industries Qingdao 230031China the School of Mathematics Renmin University of ChinaBeijing 100872China the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 the Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China

In the era of big data,there is an urgent need to establish data trading markets for effectively releasing the tremendous value of the drastically explosive *** security and data pricing,however,are still widely regarded as major challenges in this respect,which motivate this research on the novel multi-blockchain based framework for data trading markets and their associated pricing *** this context,data recording and trading are conducted separately within two separate blockchains:the data blockchain(DChain) and the value blockchain(VChain).This enables the establishment of two-layer data trading markets to manage initial data trading in the primary market and subsequent data resales in the secondary ***,pricing mechanisms are then proposed to protect these markets against strategic trading behaviors and balance the payoffs of both suppliers and ***,in regular data trading on VChain-S2D,two auction models are employed according to the demand scale,for dealing with users’ strategic *** incentive-compatible Vickrey-Clarke-Groves(VCG)model is deployed to the low-demand trading scenario,while the nearly incentive-compatible monopolistic price(MP) model is utilized for the high-demand trading *** temporary data trading on VChain-D2S,a reverse auction mechanism namely two-stage obscure selection(TSOS) is designed to regulate both suppliers’ quoting and users’ valuation ***,experiments are carried out to demonstrate the strength of this research in enhancing data security and trading efficiency.

关键词： Auction data trading markets multi-blockchain pricing mechanisms

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Convection-UNet: A Deep Convolutional Neural Network for Convection Detection based on the Geo High-speed Imager of Fengyun-4B

Convection-UNet: A Deep Convolutional Neural Network for Con...

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Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA), International Conference on

作者： Yufei Wang Baihua Xiao The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences & University of Chinese Academy of Sciences Beijing China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Deep convection can cause a variety of severe weather conditions such as thunderstorms, strong winds, and heavy rainfall. Satellite observations provide all-weather and multi-directional observations, facilitating the timely detection of such weather systems, which is crucial to saving lives and property. However, previous methods based on channel feature extraction and threshold filtering did not make full use of information in satellite images, which led to limitations on such complex problems as strong convection detection. In this study, we propose a novel framework of a deep learning-based model Convection-UNet to detect convection. We use channel 4 to 7 of FY-4B GHI that we select according to the microphysical properties of convection as input and radar reflectivity as label. We combine the detailed training time and test time data augmentation strategies and build a deep neural network to automatically extract spatial context features and achieve end-to-end learning. Results show that the performance of our method far exceeds the previous channel extraction combined with threshold filtering methods such as BT and BTD at least 0.24 on Fi-measure. We also show that our channel selection and data augmentation strategies are of great significance to detect convection.

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Fast Depth Estimation of Object via Neural Network Perspective Projection 11

Fast Depth Estimation of Object via Neural Network Perspecti...

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11th IEEE Data Driven control and Learning systems Conference, DDCLS 2022

作者： Han, Yu Chen, Yaran Li, Haoran Ma, Mingjun Zhao, Dongbin Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China University of Chinese Academy of Sciences Beijing100000 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

In autonomous driving and mobile robotic systems, obtaining the depths of objects in real-time is crucial. The current network-based methods usually design complex network to achieve 3D object detection or monocular depth estimation for the whole image, resulting in too slow to be applied to mobile robots. The perspective projection-based method can achieve real-time, which calculates the object depth based on the camera parameters, the object sizes in the world coordinates and in image coordinates. While it relies heavily on the accuracy of object size in images coordinates, and the size is usually obtained with errors through detector network. Combining the perspective projection-based methods and network-based methods, we propose a fast object depth estimation method by designing a neural network to learn perspective projection, called Fast-Depth-NPP: 1) Instead of considering the whole image, we only consider the local depth of the image;2) Using local image patches as network inputs avoids measurement errors of object size with detector;3) the use of global information is enhanced by incorporating position encoding. Our method is validated on the mobile robot public dataset Neurons Perception dataset, achieving excellent results and meeting the real-time requirements. © 2022 IEEE.

关键词： Object detection

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Towards Efficient Path Finding and Moving Stability-Focused Trajectory Planning for Mobile Robots

Towards Efficient Path Finding and Moving Stability-Focused ...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Hou, Muyang Lei, Zhenya Hu, Haidong Wang, Zhong Wang, Yong Xie, Le Wang, Hesheng Shanghai Jiao Tong University Department of Automation Shanghai200240 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China Beijing Institute of Control Engineering Beijing100190 China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Department of Automation Shanghai200240 China

ISBN: (纸本)9781665481090

Nowadays, mobile robots play an important role in a variety of service scenarios. They need to plan and track trajectories to accomplish tasks such as delivery or guided tours. In such tasks, there are two remaining challenges: on one hand, the time and memory consumption of present path planners are still significant;on the other hand, service robots, which have a higher center of gravity, demand greater moving stability than that of current trajectory planners provide. To address these two challenges. Firstly, we propose a safety boundary first A∗ which fully utilizes environmental obstacle information to create a safety boundary and searches in it to quickly find an initial path. Secondly, we formulate the trajectory optimization problem as a nonlinear optimization problem, where the smoothness, safety, feasibility, and moving stability of the robot are taken into account. Furthermore, to constrain the robot's lateral acceleration, we design a time allocation algorithm based on non-uniform B-spline, enhancing the quality of the resulting trajectory. Simulations and real-world experiments demonstrate that our algorithms significantly improve path search efficiency, enhance moving stability, reduce the difficulty of tracking, and improve the quality of task completion. © 2024 IEEE.

关键词： Trajectories

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Graph Attention Memory for Visual Navigation 4

Graph Attention Memory for Visual Navigation

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4th International Conference on Data-Driven Optimization of complex systems, DOCS 2022

作者： Li, Dong Zhang, Qichao Zhao, Dongbin Noah's Ark Lab Huawei Beijing China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex System China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing China

ISBN: (数字)9781665459822

ISBN: (纸本)9781665459822

Visual navigation in complex environments is inefficient with traditional reactive policy or general-purposed recurrent policy due to the long-term memory problem. To address this issue, this paper proposes a graph attention memory (GAM) architecture consisting of memory construction module, graph attention module, and control module. The memory construction module builds the topological graph based on supervised learning by taking the exploration prior. Then, guided attention features to reach the goal are extracted with the graph attention module. Finally, the deep reinforcement learning based control module makes decisions based on visual observations and guided attention features. In addition, the convergence of the proposed GAM module for recurrent attention operation is analyzed in this paper. We evaluate GAM-based navigation system in two complex 3D ViZDoom environments. Experimental results show that the GAM-based navigation system outperforms all baselines in both success rate and navigation efficiency, and significantly improves the generalization. © 2022 IEEE.

关键词： Deep learning

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High-adaption locomotion with stable robot body for planetary exploration robot carrying potential instruments on unstructured terrain

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Chinese Journal of Aeronautics 2021年第5期34卷 652-665页

作者： Kang XU Shoukun WANG Junzheng WANG Xiuwen WANG Zhihua CHEN Jinge SI School of Automation Beijing Institute of Technology.Beijing 100081China Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of TechnologyBeijing 100081China Key Laboratory Ministry of Industry and Information Technology Beijing Institute of TechnologyBeijing 100081China

There is a strong demand for Planetary Exploration Mobile robots(PEMRs)that have the capability of the traversability,stability,efficiency and high load while tackling the specialized tasks on planet *** this paper,an electric parallel wheel-legged hexapod robot which has high-adaption locomotion on the unstructured terrain is ***,the hybrid control framework,which enables robot to stably carry the heavy loads as well as to traverse the uneven terrain by utilizing both legged and wheeled locomotion,is also *** on this framework,robot controls the multiple DOF leg for performing high-adaption locomotion to negotiate obstacles via Gait Generator(GG).Additionally,by using Whole-Body control(WBC)of framework,robot has the capability of flexibly accommodating the uneven terrain by Attitude control(AC)kinematically adjusting the length of legs like an active suspension system,and by Force/torque Balance control(FBC)equally distributing the Ground Reaction Force(GRF)to maintain a stable *** simulation and experiment are employed to validate the proposed framework with the physical system in the planetary analog ***,to smoothly demonstrate the performance of robot transporting heavy loads,the experiment of carrying 3-person load of about 240 kg is deployed.

关键词： Attitude control Gait generation High-adaption locomotion Obstacle avoidance Planetary exploration robot

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A Two-layer Adaptive control Strategy for Photovoltaic Power in Different Scenarios 3

A Two-layer Adaptive Control Strategy for Photovoltaic Power...

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3rd International Conference on Energy Engineering and Power systems, EEPS 2023

作者： Wan, Yanghui Yan, Jiahao Chu, Tianfeng Li, Yaping Yuan, Kun Zhang, Kaifeng Southeast University Key Laboratory of Measurement and Control of Complex System of Engineering Ministry of Education Nanjing China Power Automation Department Nanjing China State Grid Liaoning Electric Power Co. Ltd. Electric Power Research Institute Shenyang China

ISBN: (纸本)9798350313857

Currently, control strategies for distributed photovoltaic (PV) power generation are relatively single and rigid, and cannot flexibly adapt to the diverse demands of power grids in different scenarios. Firstly, this paper analyzes the adaptive relationship between different dispatching demands, the regulation ability of distributed PV, and the characteristics of the local voltage. Secondly, for different scenarios, the coordinated controller determines the reference values of active power and reactive power given to the inverter. Thus, distributed PV power control modes which can adapt to different scenarios have been established. Then, these control modes can be classified into single control modes and compound control modes according to the complexity of scenario demands. On this basis, a specific two-layer adaptive control strategy and process of distributed PV power generation in a low-voltage distribution network are further proposed, which takes into account both external demands of the distribution network and the regulation ability of distributed PV. Finally, a case study is given to verify the rationality and effectiveness of the two-layer adaptive scenarios control strategy. This strategy can effectively improve the comprehensive support ability of distributed PV power generation towards the power grid in multiple scenarios, and improve the stable operation level of the regional power grid. © 2023 IEEE.

关键词： Reactive power

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