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10th IEEE Data Driven control and Learning systems Conference, DDCLS 2021

作者： Ma, Ruichen Wang, Yu Wang, Rui Wang, Shuo University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781665424233

This paper proposes an omnidirectional drift control on an underwater biomimetic vehicle-manipulator system (UB-VMS). The UBVMS has two biomimetic propellers, they obtain propulsive force by actuating their undulating fins. A configuration of the system, the dynamics of the UBVMS and the kinematics of the fins are given respectively. Then the control problem is designed as a Markov decision process (MDP). A reinforcement learning method based on the twin delayed deep deterministic policy gradient (TD3) is proposed for this MDP. A control policy is well trained by the reinforcement learning method and tested in eight different simulations. An analysis of the simulation results is also given. © 2021 IEEE.

关键词： Reinforcement learning

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Multimodal Unknown Surface Material Classification and Its Application to Physical Reasoning

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2022年第7期18卷 4406-4416页

作者： Wei, Junhang Cui, Shaowei Hu, Jingyi Hao, Peng Wang, Shuo Lou, Zheng Univ Chinese Acad Sci Sch Future Technol Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Shanghai 200031 Peoples R China Chinese Acad Sci State Key Lab Superlattices & Microstruct Inst Semicond Beijing 100083 Peoples R China

Unknown surface material classification (SMC) can inform a robot about material properties, enabling it to interact with environments appropriately. Recent research has leveraged multimodal data using deep learning to improve the performance of SMC. In this article, we present a deep learning model, multimodal temporal convolutional neural network (MTCNN), which integrates energy spectrum, dilated convolutions, and sequence poolings into a unified network architecture. The proposed model can learn material representations from auditory and multitactile (i.e., acceleration, normal force, and friction force) data generated by dragging a tool along surfaces, and distinguish unknown object surface materials into categories. For surface material data collection, a tool is also designed to detect different object surfaces. The performance of MTCNN is evaluated on a public dataset and the highest classification accuracy is 87.55%. A robotic curling example is provided to illustrate how the presented model helps the robot in manipulation.

关键词： Robots Convolutional neural networks Visualization Informatics Feature extraction Task analysis Haptic interfaces Auditory and haptic information deep learning multimodal fusion physical reasoning unknown surface material classification (USMC)

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Practical Predefined-Time Output-Feedback Consensus Tracking control for Multiagent systems

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第8期53卷 5311-5322页

作者： Zou, An-Min Liu, Yangyang Hou, Zeng-Guang Hu, Zhipei Shantou Univ Coll Engn Dept Elect & Informat Engn Guangdong Prov Key Lab Digital Signal & Image Pro Shantou 515063 Peoples R China Shantou Univ Minist Educ Key Lab Intelligent Mfg Technol Shantou 515063 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Shantou Univ Coll Engn Guangdong Prov Key Lab Digital Signal & Image Pro Shantou 515063 Peoples R China Shantou Univ Coll Engn Dept Elect & Informat Engn Shantou 515063 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Macau Univ Sci & Technol Inst Syst Engn CASIA MUST Joint Lab Intelligence Sci & Technol Macau Peoples R China

This article addresses the issue of output-feedback consensus control of multiagent systems under the directed topology and subject to bounded external disturbances. By employing a smooth time-varying function, a distributed practical predefined-time (PPT) observer is developed to estimate the reference trajectory for the entire team (i.e., the leader's state) and a practical preset-time extended-state observer is also proposed to estimate bounded disturbances and unmeasurable system states. Next, a novel continuous and nonsingular PPT consensus control law is designed on the basis of the observers. Furthermore, the designed control protocol can achieve PPT stability, that is, consensus tracking errors are enforced to a neighborhood around zero within a predetermined time, which can be specified a priori, independent of initial states of agents and/or any other design parameters. Finally, illustrative numerical examples, including a comparative one, are provided to demonstrate the performance of the present predefined-time control approach.

关键词： Consensus control Observers Sliding mode control Numerical stability Multi-agent systems Topology Stability criteria Consensus control multiagent systems (MASs) practical predefined-time (PPT) stability sliding-mode control

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Evaluating the Generalization of 3D Detectors with Hybrid Point Clouds 2

Evaluating the Generalization of 3D Detectors with Hybrid Po...

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2nd International Conference on Computer Science and management Technology, ICCSMT 2021

作者： Tian, Yonglin Wang, Xiao University of Science and Technology of China Department of Automation Hefei China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing China

ISBN: (纸本)9781665420631

With the development of LiDAR technologies and deep learning algorithms, tremendous methods have been proposed for 3D detection in intelligent transportation. However, the generalization of 3D detectors under different environments is not fully evaluated due to the lack of diverse testing datasets. Two of the main obstacles in building testing benchmark of point clouds are the huge cost of the annotation of point clouds and the low flexibility in data collection. In this paper, we propose to use hybrid point clouds to evaluate the generalization of multiple 3D detectors when they are transferred from real data to hybrid data. Compared with evaluating 3D detectors with real point clouds dataset, our approach is much more flexible and demands less human labor. © 2021 IEEE.

关键词： Deep learning

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Advanced Air Mobility: An Innovation for Future Diversified Transportation and Society

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第2期9卷 3106-3110页

作者： Guo, Jingqiu Chen, Long Li, Lingxi Na, Xiaoxiang Vlacic, Ljubo Wang, Fei-Yue Tongji Univ Minist Educ Key Lab Rd & Traff Engn Shanghai 201804 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Indiana Univ Purdue Univ Purdue Sch Engn & Technol Dept Elect & Comp Engn Indianapolis IN 46202 USA Univ Cambridge Dept Engn Cambridge CB2 1TN England Griffith Univ Inst Intelligent & Integrated Syst Nathan Qld 4111 Australia Griffith Univ Sch Engn & Built Environm Nathan Qld 4111 Australia Macau Univ Sci & Technol Inst Engn Macau 999078 Peoples R China

Advanced Air Mobility (AAM) is an innovative transportation system that will provide safe and efficient mobility of cargo, passenger and emergency services in low-altitude airspace. This paper highlights AAM's vision and addresses fundamental research challenges and opportunities, e.g., vertical lift aircraft technologies, infrastructure and operation, unmanned traffic management and system security. The overall structure of the AAM ecosystem is discussed from a technology and service perspective. The SEEDS evaluation criteria for the AAM system are explored in terms of promoting safety, sustainability and service quality during the development, verification and validation, and operation phases. As a key sector of the Mobility 5.0 paradigm, new air mobility needs to be integrated into multimodal transport and logistics systems to deliver diverse, sustainable and trustworthy solutions in the near future.

关键词： Active appearance model Aircraft Transportation Air traffic control Batteries Industries Regulation Advanced air mobility low-altitude VTOL unmanned aircraft transportation

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A parallel intelligent system for optimizing high-speed railway rescheduling by learning 1

A parallel intelligent system for optimizing high-speed rail...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Yang, Pengxin Cui, Dongliang Dai, Xuewu Lv, Yisheng Dong, Hairong Wang, Xinghao Liu, Ruiguang State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Institute of Automation Beijing China Beijing Jiaotong University State Key Laboratory of Rail Traffic Control and Safety Beijing China

ISBN: (纸本)9781665433372

The railway system is a complex system because of many constraints, randomness and high security requirements, so it is difficult to establish an accurate mathematical model for it, which brings great challenges to railway scheduling. Parallel intelligence theory can effectively solve the modeling, analysis and control of complex systems, so it has a wide range of applications in the field of intelligent transportation. In this paper, we build an artificial system according to the actual system, and use the surrogate modelling to carry out the calculation experiments, and proposed a parallel scheduling system based on the parallel intelligence method. Select a case to design scenario and perform computational experiment, the scheduling strategy is given and the scheduling strategy is analyzed. © 2021 IEEE.

关键词： Intelligent systems

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Refined Crack Detection via LECSFormer for Autonomous Road Inspection Vehicles

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第3期8卷 2049-2061页

作者： Chen, Junzhou Zhao, Nan Zhang, Ronghui Chen, Long Huang, Kai Qiu, Zhijun Sun Yat sen Univ Sch Intelligent Syst Engn Shenzhen Campus Shenzhen 518107 Guangdong Peoples R China Sun Yat sen Univ Guangdong Prov Key Lab Fire Sci & Intelligent Emer Guangzhou 510006 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Sun Yat sen Univ Sch Data & Comp Sci Guangzhou 510275 Peoples R China Univ Alberta Dept Civil & Environm Engn Edmonton AB T6G 2R3 Canada

Due to the rising cost of human resources in road maintenance and the pursuit of efficiency, autonomous road inspection vehicles are developed for intelligent detection of road disease to prevent severe traffic disasters in the early stages. Nevertheless, as a prevalent road disease, road cracks are diverse and susceptible to shadows, weather changes, and noise in data acquisition. Moreover, they usually appear with thin shapes that are hard to detect correctly by existing methods. To handle this problem, more details of the road cracks need to be better analyzed. In this article, we propose a refined road crack detection method named locally enhanced cross-shaped windows transformer (LECSFormer), which adopts a delicate design of the encoder-decoder structure. The encoder employs window-based transformer blocks to model long-range dependencies. Each transformer block ensembles the locally enhanced module to enrich the local contextual information, and token shuffle operation is applied to build cross-windows connections. The decoder uses dense connections to fuse multi-scale information, and the feature fusion module fuses hierarchical features and reweights them by the channel attention mechanism. The proposed method outperforms other state-of-the-art methods with ODS of 0.963, 0.917, 0.952, and 0.953 on four challenging datasets, CrackTree260, CrackLS315, Stone331, and CRKWH100. It can accurately detect cracks in road surfaces and support intelligent preventive maintenance of roads.

关键词： Roads Transformers Feature extraction Diseases Inspection Shape Intelligent vehicles Refined crack detection window-based trans-former semantic segmentation autonomous road inspection vehicles

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Consistent Extended Kalman Filter Design for Maneuvering Target Tracking and Its Application on Hand Position Tracking

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Guidance, Navigation and control 2022年第2期2卷 33-58页

作者： Lin Tian Yang Xu Wenchao Xue Long Cheng The State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190P.R.China School of Arti-cial Intelligence University of Chinese Academy of SciencesBeijing 100049P.R.China The Key Laboratory of Systems and Control National Center for Mathematics and Interdisciplinary SciencesAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190P.R.China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049P.R.China

This paper propees the consistent extended Kalman flter(CEKF)for the maneuvering target tracking(MTT)with nonlinear uncertain dynamics,and applies it on hand poition *** general modlel of the MTT system i presented with unmodleled dynamics in terms of nonlinear unknown function of *** CEKF is propoeed to ensure that the bounds of the estimation error's covariance matrix are av ailable through the flter *** a result,the creponding accuracy of the flter approach can be achieved ***,a CEKF-baaed MTT algorithm is constructed via the tumning aw of the critical parameter matrix QE Finally,the efectiveness of CEKF i verified by MTT numerical simulations and hand tacking expeiments under dilferent ***,two indices are employed to compare the CEKF with extended Kalman filter(EKF):the mean square errors(MSEa)and the bounded percentage,ie the percentage of the rang w bere the estimation error is encboed by the bound calculated by *** MSEs of CEKF are smaller than thoee of EKF,where the worst MSEa of CEKF and EKF are0.14 and 418 in the simulation,a8 well 80.11 and 059 in the expeiments,respectively;all bounded percentages of CEKF are larger than thoee of EKF,where the wonst average bounded percentages of CEKF and EKF ame 87.86%and 14.58%,8 well as 97.41%and 41.79%in the experiments,reapectively.

关键词： Maneuvering target tracking hand position tracking extended Kalman¯lter(EKF) consistency

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Nearly optimal stabilization of unknown continuous-time nonlinear systems: A new parallel control approach

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NEUROCOMPUTING 2024年 578卷

作者： Lu, Jingwei Wang, Xingxia Wei, Qinglai Wang, Fei-Yue Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266114 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

This paper develops a novel online nearly optimal control (ONOC) method for unknown continuous -time (CT) nonaffine nonlinear systems without recovering unknown systems. First, a dynamic control law is proposed for CT nonaffine nonlinear systems using parallel control. To achieve the proposed dynamic control law, an affine augmented system (AAS) is constructed according to the original system, and an augmented performance index (API) is constructed on the basis of the original performance index (OPI). Then, the stability relationship between the original system and the AAS is provided, and it is proven that, by selecting a suitable parameter in the API, optimal control of the AAS with the API is equivalent to near -optimal control of the original system with the OPI. Subsequently, based on the proposed dynamic control law, we extend integral reinforcement learning (IRL) to completely unknown CT nonaffine systems, and it is further proved that closed -loop signals are uniformly ultimately bounded (UUB) without the assumption that the input dynamics are bounded. Furthermore, the OPI can be set to an arbitrary positive -definite form, and the UUB bound for the state vector can be predetermined. Lastly, simulations are offered to exhibit the correctness of the developed ONOC method. Source code of this paper is available at: https://***/lujingweihh/Adaptive-dynamic-programmingalgorithms/tree/main/model_free_integral_reinforcement_learning.

关键词： Adaptive dynamic programming (ADP) Integral reinforcement learning (IRL) Nearly optimal control Nonaffine nonlinearity Parallel control Unknown nonlinear systems

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The Development of AgriVerse: Past, Present, and Future

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2023年第6期53卷 3718-3727页

作者： Kang, Mengzhen Wang, Xiujuan Wang, Haoyu Hua, Jing de Reffye, Philippe Wang, Fei-Yue Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Beijing Engn Res Ctr Intelligent Syst & Technol Beijing 100190 Peoples R China Univ Montpellier AMAP CIRAD CNRSINRAEIRD F-34398 Montpellier France Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Macau Univ Sci & Technol Macao Inst Syst Engn Macau Peoples R China

Agricultural metaverse (AgriVerse) aims to optimize the production chain by saving costs, increasing efficiencies, and breaking information silos, in order to achieve sustainable agriculture. While AgriVerse is featured by the virtual-real interaction of the agriculture-related processes based on heterogeneous data, knowledge, and models, the link between AgriVerse and the intensively studied plant modeling is vague. This article presents briefly the research contents of plant modeling, analyzes the ongoing transition at the age of artificial intelligence (AI), and envisions future AgriVerse with the support of the agricultural foundation model, the decentralized agricultural organization (DAO) and the decentralized science (DeSci) of the plant model. Three AgriVerse application scenarios are presented. The opportunities and challenges of AgriVerse are discussed. This work is expected to identify the key research issues of AgriVerse and bring practitioners of diverse backgrounds together into the AgriVerse community.

关键词： Computational modeling Biological systems Biomass Biological system modeling Production Plants (biology) Metaverse Agricultural metaverse (AgriVerse) agriculture cyber-physical-social system (CPSS) artificial systems computational experiments and parallel execution (ACP) decentralized science (DeSci) knowledge-and-data driven modeling parallel agriculture plant model

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