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

作者： Li, Hongyang Wei, Qinglai School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Institute of Systems Engineering Macau University of Science and Technology Macau999078 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

In this paper, a data-driven optimal control method is provided for the half-vehicle suspension system via adaptive dynamic programming. The main contribution of this paper is that the data-driven adaptive dynamic programming method is applied to the optimal control problem of half-vehicle suspension system, which only requires the input-state data of the system. First, the structure of the half-vehicle suspension system is analyzed, and the optimal control problem is introduced. Next, the model-based adaptive dynamic programming method is provided. Based on the model-based method, a data-driven adaptive dynamic programming method is given. The properties of the provided methods are analyzed. Finally, simulation example is given to show the effectiveness of the data-driven adaptive dynamic programming method. © 2022 IEEE.

关键词： Dynamic programming

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Zero-Sum Differential Game-Based Fault-Tolerant Tracking control for Nonlinear systems With Multiplicative Failures

Zero-Sum Differential Game-Based Fault-Tolerant Tracking Con...

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2022 Chinese automation Congress, CAC 2022

作者： Tan, Cong Lin, Mingduo Xia, Hongbing Zhao, Bo Xu, Yancai School of Systems Science Beijing Normal University Beijing100875 China Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781665465335

A novel fault-tolerant tracking control (FTTC) approach for affine nonlinear systems is developed from the perspective of zero-sum differential games (ZSDG) to deal with unknown multiplicative actuator failures in this paper. By regarding the control input and the multiplicative actuator failure as two players of a zero-sum game, a performance index function reflecting the trajectory tracking error, the control policy and the unknown failure is established. To handle the tracking Hamilton-Jacobi-Isaacs (HJI) equation associated with the established performance index function, a critic neural network is utilized. Then, the Nash equilibrium solution can be obtained, and the FTTC is achieved. The stability of the faulty nonlinear system is examined through Lyapunov-based approach. Finally, simulation results of a spring system is provided to ensure the utility and the realizability of the ZSDG-based FTTC approach. © 2022 IEEE.

关键词： Nonlinear 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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A Parallel control Method for Zero-Sum Game with Unknown Time-Varying System

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The International Journal of Intelligent control and systems 2024年第1期29卷 37-41页

作者： Qinglai Wei Zhenhua Zhu Jie Zhang Fei-Yue Wang State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China

In this paper,based on ACP(ACP:artificial societies,computational experiments,and parallel execution)approach,a parallel control method is proposed for zero-sum games of unknown time-varying *** process of constructing a sequence of artificial systems,implementing the computational experiments,and conducting the parallel execution is *** artificial systems are constructed to model the real *** experiments adopting adaptive dynamic programming(ADP)are shown to derive control laws for a sequence of artificial *** purpose of the parallel execution step is to derive the control laws for the real ***,simulation experiments are provided to show the effectiveness of the proposed method.

关键词： Zero-sum games parallel control ACP(ACP:artificial societies computational experiments and parallel execution) adaptive dynamic programming(ADP)

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A New Self-Learning Optimization management Scheme for Smart Grid systems

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The International Journal of Intelligent control and systems 2024年第2期29卷 66-70页

作者： Qinglai Wei Zehua Liao School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China

This paper presents a new energy optimization management scheme for the smart grid system(SGS),which considers not only the inclusion of renewable energy in energy management but also the feedback sale of redundant renewable energy to the grid,so as to obtain better optimization management results and avoid energy waste.A new performance index function is constructed,which aims to save users’electricity costs and protect energy storage *** construct a periodic iterative self-learning method based on the characteristics that the relevant data of SGS are approximately *** proposed self-learning optimization method is based on adaptive dynamic programming(ADP).The initial iterative control law is obtained by pre-training,and the designed method can be realized by neural *** experiments are carried out by using the relevant data of SGS for four *** experimental results show that the new energy management scheme can help users save a lot of electricity costs,and is more effective than some common methods.

关键词： Smart grid system energy management renewable energy self-learning adaptive dynamic programming

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Learning impedance control of robots with enhanced transient and steady-state control performances

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Science China(Information Sciences) 2020年第9期63卷 204-216页

作者： Tairen SUN Long CHENG Liang PENG Zengguang HOU Yongping PAN State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences School of Data and Computer Science Sun Yat-sen University

This study proposes a learning impedance controller comprising a proportional feedback control term, a composite-learning-based uncertainty estimation term, and a robot-environment interaction control term. The impedance control problem is converted into a particular reference-trajectory tracking problem based on a generated reference trajectory. The proposed controller ensures the exponential convergence of the auxiliary tracking error and the uncertainty estimation error. The interaction control term improves the transient control performance through suppression/encouragement of the incorrect/correct robot *** composite-learning update law enhances the transient and steady-state control performances based on the exponential convergence of the uncertainty estimation error and auxiliary tracking error. Finally, the effectiveness and advantages of the proposed impedance controller are validated by theoretical analysis and simulations on a parallel robot.

关键词： robot adaptive control neural network impedance control parameter convergence

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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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GAP: Goal-Aware Prediction with Hierarchical Interactive Representation for Vehicle Trajectory 7th

GAP: Goal-Aware Prediction with Hierarchical Interactive Re...

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7th International Conference on Data Mining and Big Data, DMBD 2022

作者： Li, Ding Zhang, Qichao Lu, Shuai Pan, Yifeng Zhao, Dongbin School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Baidu Inc. Beijing100085 China

ISBN: (纸本)9789811992964

Predicting the future trajectories of surrounding vehicles plays a vital role in ensuring the safety of autonomous driving. It is extremely challenging for the pure imitation method due to the high degree of multimodality and uncertainty in the future. In fact, when driving in most traffic scenarios, vehicles should obey some traffic rules such as "vehicles follow the lane and do not collide with each other". Inspired by this, this paper proposes a goal-aware prediction (GAP) framework to predict the multimodal trajectories, where goals are chosen in the lanes with hierarchical interactive representation and a multi-task loss. Based on the graph-based vectorized input, a novel hierarchical interactive representation module is first designed to obtain the fine-grained goal features, which progressively models interactions between goal-to-goal, goal-to-lane, and lane-to-agent, corresponding to the individual, local and global levels, respectively. Then, an auxiliary collision loss is developed to take into account learning from demonstration and injecting common sense of collision avoidance, and is served as a part of the multi-task loss to guide the generation of multimodal plausible trajectories. In the end, the proposed method is verified on the Baidu In-house Cut-in dataset, which includes more than 370K interactive scenarios collected in the real road testing. The comparative results demonstrate the superior performance of our proposed GAP model than the mainstream prediction methods. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Trajectories

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automation 5.0: The key to systems Intelligence and Industry 5.0

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IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1723-1727页

作者： Ljubo Vlacic Hailong Huang Mariagrazia Dotoli Yutong Wang Petros A.Ioannou Lili Fan Xingxia Wang Raffaele Carli Chen Lv Lingxi Li Xiaoxiang Na Qing-Long Han Fei-Yue Wang Institute of Intelligent and Integrated Systems and the School of Engineering and Built Environment Griffith UniversityNathanQLD 4111Australia Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic UniversityHong KongChina Department of Electrical and Information Engineering Polytechnic of Bari70126 BariItaly State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190and also with the Qingdao Academy of Intelligent IndustriesQingdao 266114China Department of Electrical Engineering-Systems University of Southern CaliforniaLos AngelesCA 90007 USA School of Automation Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049 Beijing Huairou Academy of Parallel Sensing Beijing 101499China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University IndianapolisIndianapolisIN 46202 USA Department of Engineering University of CambridgeCB21TN CambridgeU.K. School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Dazhou Artificial Intelligence Institute Dazhouand the Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China

automation has come a long way since the early days of mechanization,i.e.,the process of working exclusively by hand or using animals to work with *** rise of steam engines and water wheels represented the first generation of industry,which is now called Industry Citation:***,***,***,***,***,***,***,***,***,***,***,Q.-***,and F.-***,“automation 5.0:The key to systems intelligence and Industry 5.0,”IEEE/CAA ***,vol.11,no.8,pp.1723-1727,Aug.2024.

关键词： automation machinery intelligence

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An Optimal control Based Trajectory Planning Method for Cooperative Merging Maneuvers 3

An Optimal Control Based Trajectory Planning Method for Coop...

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3rd International Symposium on Intelligent Robotics and systems, ISoIRS 2023

作者： Wang, Qi Ge, Hao Duan, Xuting Zhou, Jianshan School of Transportation Science and Engineering Beihang University State Key Lab of Intelligent Transportation System Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control State Key Laboratory of Air Traffic Management System and Technology Beijing China Nanjing Research Institute of Electronic Engineering State Key Laboratory of Air Traffic Management System and Technology Nanjing China School of Transportation Science and Engineering Beihang University State Key Lab of Intelligent Transportation System Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control Beijing China

ISBN: (纸本)9798350339963

Due to the prediction errors and diverse driving styles of surrounding vehicles, traffic congestion and even accidents can occur in the traffic bottleneck area where vehicles merge into the main road. To ensure that connected and automated vehicles (CAVs) can quickly and efficiently merge into the main road, this paper proposes a cooperative trajectory planning method based on optimal control for vehicle cut-in scenarios. A optimal control model is constructed, and a quintic polynomial is used to generate the initial solution of the optimal control problem to accelerate the solution process and improve the success rate. Finally, The obtained merging trajectory is then synchronized with the movement of vehicles on the main road to optimize their actions and ensure a safe merging process. Joint simulation with Robot Operating System (ROS) and RVIZ is utilized, and the results show that the proposed algorithm can quickly obtain cooperative results, the locally successful merging trajectory meets the requirements of smoothness and safety, and if the solution does not allow merging, the merging vehicle is instructed to terminate the execution. © 2023 IEEE.

关键词： Trajectories

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