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International Conference on Guidance, Navigation and control, ICGNC 2022

作者： Wu, Chunxu Shou, Yicheng Li, Zhen Liu, Xiangdong School of Automation and Key Laboratory for Intelligent Control and Decision on Complex Systems Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9789811966125

Multi-robot system outperforms in efficiency for cooperative task, where the challenging burden of information exchange for surrounding environment is facilitated by collaborative simultaneous localization and mapping (SLAM). However, the utilization of environmental information, i.e., mapping, should be specifically put into efficient use and merged from the viewpoint of computation saving and localization accuracy improvement. Towards these issues, this paper proposes the Collaborative SLAM system based on shared priori maps which includes multi-agent initialization and a new map sharing and reuse mechanism among various active agents in an incremental way. Finally, based on the data set recorded in the actual laboratory environment, the proposed Collaborative SLAM system is evaluated from the perspectives of localization accuracy and computational power consumption. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mapping

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Transformer-Based Macroscopic Regulation for High-Speed Railway Timetable Rescheduling

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IEEE/CAA Journal of Automatica Sinica 2023年第9期10卷 1822-1833页

作者： Wei Xu Chen Zhao Jie Cheng Yin Wang Yiqing Tang Tao Zhang Zhiming Yuan Yisheng Lv Fei-Yue Wang Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China Signal&Communication Research Institute China Academy Railway SciencesBeijing 100081China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049 State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China IEEE State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China

Unexpected delays in train operations can cause a cascade of negative consequences in a high-speed railway *** such cases,train timetables need to be ***,timely and efficient train timetable rescheduling is still a challenging problem due to its modeling difficulties and low optimization *** paper presents a Transformer-based macroscopic regulation approach which consists of two stages including Transformer-based modeling and policy-based ***,the relationship between various train schedules and operations is described by creating a macroscopic model with the Transformer,providing the better understanding of overall operation in the high-speed railway ***,a policy-based approach is used to solve a continuous decision problem after macro-modeling for fast *** experiments on various delay scenarios are *** results demonstrate the effectiveness of the proposed method in comparison to other popular methods.

关键词： High-speed railway reinforcement learning train timetable rescheduling Transformer

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Implicit Scene Context-aware Interactive Trajectory Prediction for Autonomous Driving

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2023年第9期9卷 1-17页

作者： Lan, Wenxing Li, Dachuan Hao, Qi Zhao, Dezong Tian, Bin Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China James Watt School of Engineering University of Glasgow Glasgow U.K State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China

The accurate prediction of behaviors of surrounding traffic participants is critical for autonomous vehicles (AV). How to fully encode both explicit (e.g., map structure and road geometry) and implicit scene context information (e.g., traffic rules) within complex scenarios is still challenging. In this work, we propose an implicit scene context-aware trajectory prediction framework (the PRISC-Net, Prediction with Implicit Scene Context) for accurate and interactive behavior forecasting. The novelty of the proposed approach includes: 1) development of a behavior prediction framework that takes advantage of both model- and learning-based approaches to fully encode scene context information while modeling complex interactions;2) development of a candidate path target predictor that utilizes explicit and implicit scene context information for candidate path target prediction, along with a motion planning-based generator that generates kinematic feasible candidate trajectories;3) integration of the proposed target predictor and trajectory generator with a learning-based evaluator to capture complex agent-agent and agent-scene interactions and output accurate predictions. Experiment results based on vehicle behavior datasets and real-world road tests show that the proposed approaches outperform state-of-the-art methods in terms of prediction accuracy and scene context compliance. IEEE

关键词： Trajectories

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Task Assignment Scheme Designed for Online Urban Sensing Based on Sparse Mobile Crowdsensing

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IEEE Internet of Things Journal 2025年第11期12卷 17791-17806页

作者： Zeng, Hongjian Xiong, Yonghua She, Jinhua Yu, Anjun China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Tokyo University of Technology School of Engineering Hachioji Tokyo192-0982 Japan Jiangxi Ganyue Expressway Company Ltd. Operation Management Department Jiangxi Nanchang330000 China

Sparse mobile crowdsensing (SMCS) achieves urban-scale environmental sensing by assigning tasks to workers in specific subareas and inferring global data from the collected information. However, the effectiveness of SMCS is often limited because many studies overlook workers’ mobility and data collection time during subarea selection, as well as the time constraints of the sensing cycle in task assignment. This may affect the task completion timeliness and data quality. To address these issues, we develop a subarea evaluation method based on deep reinforcement learning, considering both the temporal effectiveness of sensing tasks and the importance of subarea selection for data inference. Using the subarea evaluation values derived from this method, we establish an online urban sensing task assignment model which is subject to constraints of sensing cycle time and cost budget. This model aims to find the task assignment result that minimizes data inference error by maximizing the comprehensive utility value. Considering the characteristics of the task assignment model, we propose an evolutionary algorithm named OTA-EA, which is based on an improved genetic algorithm. Its enhanced evolutionary operators can avoid generating infeasible solutions while maintaining robust search and optimization performance. Lastly, we conduct experimental evaluations of these methods on the real-world datasets. The results demonstrate that our subarea evaluation method can significantly reduce the data inference error, and our evolutionary task assignment algorithm can achieve better task assignment results than the baseline algorithms. © 2014 IEEE.

关键词： Genetic algorithms

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A policy gradient algorithm integrating long and short-term rewards for soft continuum arm control

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Science China(Technological Sciences) 2022年第10期65卷 2409-2419页

作者： DONG Xiang ZHANG Jing CHENG Long XU WenJun SU Hang MEI Tao School of Electrical Engineering and Automation Anhui UniversityHefei 230601China State Key Laboratory for Control and Management of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Robotics Research Center Peng Cheng LaboratoryShenzhen 518055China

The soft continuum arm has extensive application in industrial production and human life due to its superior safety and flexibility. Reinforcement learning is a powerful technique for solving soft arm continuous control problems, which can learn an effective control policy with an unknown system model. However, it is often affected by the high sample complexity and requires huge amounts of data to train, which limits its effectiveness in soft arm control. An improved policy gradient method, policy gradient integrating long and short-term rewards denoted as PGLS, is proposed in this paper to overcome this issue. The shortterm rewards provide more dynamic-aware exploration directions for policy learning and improve the exploration efficiency of the algorithm. PGLS can be integrated into current policy gradient algorithms, such as deep deterministic policy gradient(DDPG). The overall control framework is realized and demonstrated in a dynamics simulation environment. Simulation results show that this approach can effectively control the soft arm to reach and track the targets. Compared with DDPG and other model-free reinforcement learning algorithms, the proposed PGLS algorithm has a great improvement in convergence speed and performance. In addition, a fluid-driven soft manipulator is designed and fabricated in this paper, which can verify the proposed PGLS algorithm in real experiments in the future.

关键词： soft arm control Cosserat rod deep reinforcement learning policy gradient algorithm high sample complexity

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A Nonsmooth Observer Based on Friction Model Compensation for Sensorless Permanent Magnet Synchronous Motor 42

A Nonsmooth Observer Based on Friction Model Compensation fo...

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42nd Chinese control Conference, CCC 2023

作者： Zhang, Zaishan Zhu, Wenwu Yu, Ke Wang, Xiangyu Li, Shihua School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China School of Electrical Engineering and Automation Hefei University of Technology Hefei230009 China

ISBN: (纸本)9789887581543

In this paper, a nonsmooth observer considering friction compensation is proposed for the sensorless permanent magnet synchronous motor (PMSM) to estimate the rotor position and speed. Friction is one of the main factors affecting the tracking accuracy of the motor at low speed, especially in the back and forth motion of the motor. Firstly, based on the Stribeck friction model, a nonlinear friction model of the motor is established. Secondly, a nonsmooth observer with friction compensation is designed to estimate the position and speed of the rotor, and the lumped disturbance of the system at the same time. Further, the stability analysis of this nonsmooth observer is given. Finally, the effectiveness of the proposed method is verified by the simulation results. © 2023 Technical Committee on control Theory, Chinese Association of automation.

关键词： Sensorless control

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Attention Markets of Blockchain-Based Decentralized Autonomous Organizations

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1370-1380页

作者： Juanjuan Li Rui Qin Sangtian Guan Wenwen Ding Fei Lin Fei-Yue Wang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China IEEE

The attention is a scarce resource in decentralized autonomous organizations(DAOs),as their self-governance relies heavily on the attention-intensive decision-making process of“proposal and voting”.To prevent the negative effects of pro-posers’attention-capturing strategies that contribute to the“tragedy of the commons”and ensure an efficient distribution of attention among multiple proposals,it is necessary to establish a market-driven allocation scheme for DAOs’***,the Harberger tax-based attention markets are designed to facilitate its allocation via continuous and automated trading,where the individualized Harberger tax rate(HTR)determined by the pro-posers’reputation is ***,the Stackelberg game model is formulated in these markets,casting attention to owners in the role of leaders and other competitive proposers as *** equilibrium trading strategies are also discussed to unravel the intricate dynamics of attention ***,utilizing the single-round Stackelberg game as an illustrative example,the existence of Nash equilibrium trading strategies is ***,the impact of individualized HTR on trading strategies is investigated,and results suggest that it has a negative correlation with leaders’self-accessed prices and ownership duration,but its effect on their revenues varies under different *** study is expected to provide valuable insights into leveraging attention resources to improve DAOs’governance and decision-making process.

关键词： Attention decentralized autonomous organizations Harberger tax Stackelberg game.

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Predefined-Time Distributed Fault-Tolerant control for Nonlinear Multiagent systems Suffering From Nonaffine Faults

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IEEE Transactions on systems, Man, and Cybernetics: systems 2025年第5期55卷 3300-3311页

作者： Cheng, Shuai Xin, Bin Chen, Jie Deng, Fang Qingdao University School of Automation Qingdao266071 China Beijing Institute of Technology School of Automation Beijing100081 China Beijing Institute of Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Beijing100081 China Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Department of Control Science and Engineering Shanghai200092 China

This article investigates the distributed predefined-time (PT) fault-tolerant control for nonlinear multiagent systems (NNMSs) with nonaffine faults. A novel distributed PT control scheme is proposed based on a new PT theorem. The switching functions are introduced into distributed control signals to avoid the singularity problem. The second-order filter technology is designed to solve "explosion of complexity" issues. The newly designed PT compensation signals are utilized to eliminate filter errors. The proposed distributed PT controller based on local information can guarantee that all signals of the closed-loop system are PT bounded, and consensus errors of NNMSs can be enforced to a small region around the origin within a predefined time. Compared with the finite/fixed time, the predefined time is an exact value given in advance, which remains constant regardless of initial states and control parameters. Finally, two comparative simulations are provided to demonstrate the presented strategy. © 2013 IEEE.

关键词： Adaptive control systems

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Understanding the Influence of Random Impulse Noise on Visual SLAM 41

Understanding the Influence of Random Impulse Noise on Visua...

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第41届中国控制会议

作者： Nan Zhang Zhihong Peng Wei Quan School of Automation Beijing Institute of Technology Key Lab of Intelligent Control and Decision of Complex Systems

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

The localization accuracy of visual SLAM depends on the image ***,in postdisaster rescue missions,the images obtained by the camera often contain considerable noise,which affects the pose estimation based on visual *** this paper,we study the influence of random impulse noise in images on the localization accuracy of visual SLAM,and reduce these influences by denoising and removing ***,the camera image is preprocessed by the traditional image noise reduction *** at the problem of a large number of mismatches in optical flow tracking due to the influence of residual noise,the improved random sample consensus method is adopted to remove *** judge the correct matching probability of optical flow tracking results by normalized cross-correlation matching before random *** use guided sampling to select matching points to estimate the camera motion model,to increase the robustness of the SLAM ***,our method is verified in the open-source solution *** show that after random impulse noise is added to the KITTI dataset,the pose estimation accuracy of the improved SLAM is higher than the pose estimation accuracy after noise reduction only,and it is also higher than the pose estimation results of the original images in multiple sequences of the KITTI dataset.

关键词： Visual SLAM Denoise NCC

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Distributed Sensor FD for Sensor Networks 32

Distributed Sensor FD for Sensor Networks

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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Zhang, Jiarui Ding, Steven X. Li, Linlin University of Duisburg-Essen Institute for Automatic Control and Complex Systems Duisburg Germany University of Science and Technology Beijing School of Automation and Electrical Engineering Beijing China

ISBN: (纸本)9798350399714

The main objective of this paper is to develop a distributed sensor fault detection (FD) approach for sensor networks in the presence of noise and transmission time of information exchange. To be specific, the approach optimizes the performance of FD by minimizing the detection uncertainty and the online implementation of the proposed method is in a distributed and recursive manner. Finally, a numerical example is illustrated to show that the distributed approach can successfully and efficiently accomplish the FD task. © 2023 IEEE.

关键词： Sensor networks

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