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

作者： Wang, Xin Sun, Jian Wang, Gang State Key Lab of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

The present paper considers the data-driven control of unknown linear time-invariant discrete-time systems under an event-triggering transmission scheme. To this end, we begin by presenting a dynamic event-triggering scheme (ETS) based on periodic sampling, and a discrete-time looped-functional (DLF) approach, through which a model-based stability condition is derived. Combining the model-based condition with a recent data-based system representation, a data-driven stability criterion in the form of linear matrix inequalities (LMIs) is established, which offers a way of co-designing the ETS matrix and the controller using pre-collected noisy input-state data. Finally, numerical simulations showcase the efficacy of ETS in reducing data transmissions as well as of the proposed co-design methods. © 2022 IEEE.

关键词： Stability criteria

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Physical-Informed Neural Network for MPC-Based Trajectory Tracking of Vehicles With Noise Considered

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON intelligent VEHICLES 2024年第3期9卷 4493-4503页

作者： Jin, Long Liu, Longqi Wang, Xingxia Shang, Mingsheng Wang, Fei-Yue Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Chinese Acad Sci Chongqing Inst Green & Intelligent Technol Chongqing Key Lab Big Data & Intelligent Comp Chongqing 400714 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

The trajectory tracking plays a vital role in unmanned driving technology. Although traditional control schemes may yield satisfactory outcomes in dealing with simple linear tasks, they may fall short when handling dynamic systems with time-varying characteristics or lack of ability to complete a given task with the disturbance of noise. Therefore, a predictive control scheme under the framework of artificial systems, computational experiments, and parallel execution (ACP) is proposed. Within the ACP framework, the scheme integrates a model predictive control (MPC) controller and a physical-informed neural network (PINN) model to tackle intricate trajectory tracking tasks effectively with noise considered. Moreover, soft constraints that can enhance model robustness and improve solution efficiency are considered in the scheme. Then, theoretical analyses on the PINN model are provided with rigorous mathematical proofs. Finally, experiments and comparisons with existing works are conducted to illustrate the effectiveness and superiority of the constructed PINN model for MPC-based trajectory tracking of vehicles.

关键词： Mathematical models Trajectory tracking Task analysis Predictive models intelligent vehicles Computational modeling Trajectory Artificial systems computational experiments model predictive control (MPC) controller parallel execution (ACP) physical-informed neural network (PINN) trajectory tracking tasks

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A Survey on Social Manufacturing: A Paradigm Shift for Smart Prosumers

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2023年第5期10卷 2504-2522页

作者： Xiong, Gang Tamir, Tariku Sinshaw Shen, Zhen Shang, Xiuqin Wu, Huaiyu Wang, Fei-Yue Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing Engn Res Ctr Intelligent Syst & Technol Beijing 100190 Peoples R China Chinese Acad Sci Guangdong Engn Res Ctr 3D Printing & Intelligent Dongguan 523808 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Qingdao Acad Intelligent Ind Intelligent Mfg Ctr Qingdao 266109 Peoples R China Chinese Acad Sci Res Ctr Precis Sensing & Control Inst Automat Beijing 100190 Peoples R China

The intelligent manufacturing is a complex engineering system, and the cyber-physical systems (CPSs) and the industrial Internet are the preliminary infrastructures. When cyber-physical-social systems (CPSSs) are formed by extending CPS into the social aspect, Societies 5.0 era is coming. In the Societies 5.0 era, social manufacturing (SM) is an innovative manufacturing solution for intelligent manufacturing. In this article, a survey on SM is introduced. It includes the definition and theory of SM, and comparison between SM and other manufacturing paradigms. Moreover, the key supporting technologies are presented, which can be used to realize SM, such as blockchain, 3-D printing, and big data. Then, the applications of SM to industries are illustrated. The SM has broad application prospects in the high-end customized, distributed manufacturing, and other intelligent manufacturing. Finally, the challenges and future trends are discussed.

关键词： Manufacturing Production Industries Market research Solid modeling Big Data Three-dimensional printing 3-D printing cyber-physical-social systems (CPSSs) intelligent manufacturing social manufacturing (SM) Societies 5 0

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A Novel Parallel control Method for Continuous-Time Linear Output Regulation With Disturbances

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第6期53卷 3760-3770页

作者： Wei, Qinglai Li, Hongyang Wang, Fei-Yue 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 Macau Univ Sci & Technol Inst Syst Engn Taipa Macao Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China

In this article, a novel linear parallel control method is developed for output regulation problems with disturbances. The traditional feedback regulators are passive regulation methods. In order to solve this problem, the parallel controllers are presented, where the time variation of the control is constructed instead of the control value itself, to stabilize the output of the system. The main contributions of the developed method include two aspects: 1) a novel parallel regulator structure is presented, which can provide greater flexibility comparing with traditional methods and 2) the necessary and sufficient conditions for the existence of parallel regulators are analyzed. First, the structure of the linear parallel regulators is provided. Next, considering the situations that the full system information is obtained and the information of error is obtained, respectively, the properties of the parallel regulators are analyzed, and the regulator designs for the two situations are provided. Finally, numerical examples are provided to verify the correctness of the present method.

关键词： Regulators Regulation control systems Stability analysis Asymptotic stability Eigenvalues and eigenfunctions control theory Continuous-time linear systems output regulation parallel control theory parallel regulators parallel systems

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Hierarchical Deep Reinforcement Learning With Experience Sharing for Metaverse in Education

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2023年第4期53卷 2047-2055页

作者： Hare, Ryan Tang, Ying Rowan Univ Dept Elect & Comp Engn Glassboro NJ 08028 USA Qingdao Acad Intelligent Ind Inst Smart Educ Qingdao 266000 Peoples R China Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Dalian Maritime Univ Sch Informat Sci & Technol Dalian 116026 Peoples R China

Metaverse has gained increasing interest in education, with much of literature focusing on its great potential to enhance both individual and social aspects of learning. However, little work has been done to address the systems and technologies behind providing meaningful Metaverse learning. This article proposes a technical framework to address this research gap, where a hierarchical multiagent reinforcement learning approach with experience sharing is developed to augment the intelligence of nonplayer characters in Metaverse learning for personalization. The utility and benefits of the proposed framework and methodologies are demonstrated in Gridlock, a Metaverse learning game, as well as through extensive simulations.

关键词： ACP experience sharing metaverse learning reinforcement learning (RL)

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What Does ChatGPT Say:The DAO from Algorithmic Intelligence to Linguistic Intelligence

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IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 575-579页

作者： Fei-Yue Wang Qinghai Miao Xuan Li Xingxia Wang Yilun Lin School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China Virtual Reality Fundamental Research Laboratory Department of Mathematics and TheoriesPeng Cheng LaboratoryShenzhen 518000China Urban Computing Lab Department of Applied ResearchShanghai AI LaboratoryShanghai 200232China IEEE

THE well-known ancient Chinese philosopher Lao Tzu(老子)or Laozi(6th~4th century BC during the Spring and Autumn period)started his classic Tao Teh Ching《道德经》or Dao De Jing(see Fig.1)with six Chinese characters:"道(Dao)可(Ke)道(Dao)非(Fei)常(Chang)道(Dao)",which has been traditionally interpreted as“道可道,非常道”or"The Dao that can be spoken is not the eternal Dao".

关键词： DAO Intelligence Spring

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Probabilistic Data-Driven Modeling of a Melt Pool in Laser Powder Bed Fusion Additive Manufacturing

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 4908-4925页

作者： Fang, Qihang Xiong, Gang Zhao, Meihua Tamir, Tariku Sinshaw Shen, Zhen Yan, Chao-Bo Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat Beijing Engn Res Ctr Intelligent Syst & Technol State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Chinese Acad Sci Guangdong Engn Res Ctr Printing & Intelligent Mfg Cloud Comp Ctr Dongguan 523808 Peoples R China Guangdong Univ Technol State Key Lab Precis Elect Mfg Technol & Equipment Guangzhou Peoples R China Debre Markos Univ Inst Technol Debre Markos 269 Ethiopia Xi An Jiao Tong Univ Fac Elect & Informat Engn State Key Lab Mfg Syst Engn Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ Fac Elect & Informat Engn Sch Automat Sci & Engn Xian 710049 Shaanxi Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

The widespread adoption of laser powder bed fusion (LPBF) additive manufacturing is hampered by process unreliability problems. Modeling the melt pool behavior in LPBF is crucial to develop process control methods. While data-driven models linking melt pool dynamics to specific process parameters have shown appreciable advancements, existing models often oversimplify these relationships as deterministic, failing to account for the inherent instability of LPBF processes. Such simplifications can lead to overconfident and unreliable predictions, potentially resulting in erroneous process decisions. To address this critical issue, we propose a probabilistic data-driven approach to melt pool modeling that incorporates process noise and uncertainty. Our framework formulates a problem that includes distribution approximation and uncertainty quantification. Specifically, the Gaussian distribution with higher order priors, aided with variational inference and importance sampling, is used to approximate the probability distribution of melt pool characteristics. The uncertainty inherent in both LPBF process data and the modeling approach itself are then decomposed and approximated by using Monte Carlo sampling. The melt pool model is improved further by using a novel grid-based representation for the neighborhood of a fusion point, and a neural network architecture designed for effective feature fusion. This approach not only refines the accuracy of the model but also quantifies the uncertainty of the predictions, thereby enabling more informed decision-making with reduced risk. Two potential applications, including LPBF process planning and anomaly detection, are discussed. The implementation of our model is available at https://***/qihangGH/probabilistic_melt_pool_model. Note to Practitioners-Modeling the melt pool behavior in laser powder bed fusion (LPBF) processes is pivotal for enhancing its quality control. However, a problem is that most existing data-driven me

关键词： Laser powder bed fusion (LPBF) additive manufacturing melt pool uncertainty quantification quality control process planning anomaly detection Laser powder bed fusion (LPBF) additive manufacturing melt pool uncertainty quantification quality control process planning anomaly detection

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ClusterST: Clustering Spatial-Temporal Network for Traffic Forecasting

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IEEE TRANSACTIONS ON intelligent TRANSPORTATION systems 2023年第1期24卷 706-717页

作者： Luo, Guiyang Zhang, Hui Yuan, Quan Li, Jinglin Wang, Fei-Yue Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Shaanxi Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100876 Peoples R China Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Innovat Ctr Parallel Vis Qingdao 266000 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Taipa Macao Peoples R China

Traffic forecasting aims to capture complex spatial-temporal dependencies and non-linear dynamics, which plays an indispensable role in intelligent transportation systems and other domains like neuroscience, climate, etc. Most recent works rely on graph convolutional networks (GCN) to model the dependencies and the dynamics. However, the over-smoothing issue of GCN would produce indistinguishable features among nodes, leading to poor expressivity and weak capability of modeling complex dependencies and dynamics. To address this issue, we present a novel clustering spatial-temporal (ClusterST) unit, which incorporates unsupervised learning into GCN for extracting discriminative features. Specifically, we first exploit a neural network to learn a dynamic clustering, i.e., learning to partition the neighbors of each node into clusters at each time step. Two probabilistic losses are proposed to improve the separability of clusters. Then, the extracted features of different clusters can be distinguished. Based on the dynamically formed clusters, a vanilla GCN is applied to aggregate features within each cluster. By purely exploiting such a ClusterST unit, large improvements over the state-of-the-art are achieved. Furthermore, ClusterST units with a different number of clusters can be regarded as basic components to construct an inception-like ClusterST network for going deeper. We evaluate the framework on two real-world large-scale traffic datasets and observe an average improvement of $18.19\%$ and $7.62\%$ over state-of-the-art baselines, respectively. The code and models will be publicly available.

关键词： Index Terms- Traffic forecasting graph convolutional network spatial-temporal networks over-smoothing

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A Framework and Operational Procedures for Metaverses-Based Industrial Foundation Models

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2023年第4期53卷 2037-2046页

作者： Wang, Jiangong Tian, Yonglin Wang, Yutong Yang, Jing Wang, Xingxia Wang, Sanjin Kwan, Oliver Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China China North Ind North Automatic Control Technol Re North Automat Control Technol Inst Taiyuan 030006 Peoples R China Mot G Inc Shenzhen 518071 Peoples R China

Industrial processes are typical cyber-physical-social systems (CPSSs), where the effective management of employees and the efficient control of machines play important roles. Traditional industries heavily rely on human labor and neglect the development of collection-utilization-transmission integrated information loops, thereby leading to high costs and low efficiency in operational procedures. To facilitate the natural interactions and smart operations for humans and machines, industrial foundation models (IFMs) based on metaverses are proposed in this article, serving as the operating systems of industrial parallel machines that provide sustainable data resources and scenarios for management and control experiments. On this basis, IFM comprised of vision foundation models, language foundation models, as well as operational foundation models, are constructed to manage resources in industrial parallel machines and provides comprehensive services for industrial procedures. On the one hand, IFM can efficiently manage various resources including computing power, digital assets, enterprise resources, and platform I/O via the proposed CPSS-based competing, sharing, scheduling, monitoring, allocating, and recovering mechanisms. On the other hand, imaginative intelligence, linguistic intelligence, and algorithmic intelligence can be achieved through vivid visualization of vision foundation models, natural conversations of language foundation models, and smart manipulation of operational foundation models. With the proposed IFM, cyber-physical-social intelligence (CPSI) can be achieved to enhance the efficient management and control of industrial processes.

关键词： Cyber-physical-social intelligence (CPSI) cyber-physical-social systems (CPSSs) industrial foundation models (IFMs) intelligent enterprises metaverses operational processes parallel intelligence

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Attitude tracking control of spacecraft with preset-time preset-bounded convergence

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2022年第18期32卷 10162-10179页

作者： Zou, An-Min Liu, Yangyang Shantou Univ Coll Engn Dept Elect & Informat Engn Shantou 515063 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Guangdong Prov Key Lab Digital Signal & Image Pro Shantou Peoples R China Shantou Univ Key Lab Intelligent Mfg Technol Minist Educ Shantou Peoples R China

This article addresses the issue of attitude tracking control of rigid spacecraft in the existence of bounded external disturbances. By introducing a smooth time-varying scaling function, a preset-time sliding mode surface is designed. Then, a nonsingular and continuous preset-time preset-bounded attitude tracking control scheme is developed. The distinctive feature of the present control law is that the size of the convergence region and the settling time can be prescribed a priori. In addition, the proposed control scheme exhibits superiority in reducing the magnitude of the applied control torques and saving energy. At last the feasibility of the derived control protocol is illustrated by numerical simulations including a comparative example.

关键词： attitude control preset-time preset-bounded stability rigid spacecraft sliding mode control

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