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Event-Triggered Robust Parallel Optimal Consensus Control for Multiagent systems

IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 40-53页

作者： Qinglai Wei Shanshan Jiao Qi Dong Fei-Yue Wang IEEE the Institute of Systems Engineering Macau University of Science and Technology the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences the School of Artificial Intelligence University of Chinese Academy of Sciences China Academy of Electronics and Information Technology the State Key Laboratory of Multimodal Artificial Intelligence Systems the School of Artificial Intelligence University of Chinese Academy of Sciences

This paper highlights the utilization of parallel control and adaptive dynamic programming(ADP) for event-triggered robust parallel optimal consensus control(ETRPOC) of uncertain nonlinear continuous-time multiagent systems(MASs).First, the parallel control system, which consists of a virtual control variable and a specific auxiliary variable obtained from the coupled Hamiltonian, allows general systems to be transformed into affine systems. Of interest is the fact that the parallel control technique's introduction provides an unprecedented perspective on eliminating the negative effects of disturbance. Then, an eventtriggered mechanism is adopted to save communication resources while ensuring the system's stability. The coupled HamiltonJacobi(HJ) equation's solution is approximated using a critic neural network(NN), whose weights are updated in response to events. Furthermore, theoretical analysis reveals that the weight estimation error is uniformly ultimately bounded(UUB). Finally,numerical simulations demonstrate the effectiveness of the developed ETRPOC method.

关键词： Adaptive dynamic programming (ADP) critic neural network (NN) event-triggered control optimal consensus control robust control

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Residential Energy Scheduling With Solar Energy Based on Dyna Adaptive Dynamic Programming

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 403-413页

作者： Kang Xiong Qinglai Wei Hongyang Li the Institute of Systems Engineering Macau University of Science and Technology IEEE the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences the School of Artificial Intelligence University of Chinese Academy of Sciences

Learning-based methods have become mainstream for solving residential energy scheduling problems. In order to improve the learning efficiency of existing methods and increase the utilization of renewable energy, we propose the Dyna actiondependent heuristic dynamic programming(Dyna-ADHDP)method, which incorporates the ideas of learning and planning from the Dyna framework in action-dependent heuristic dynamic programming. This method defines a continuous action space for precise control of an energy storage system and allows online optimization of algorithm performance during the real-time operation of the residential energy model. Meanwhile, the target network is introduced during the training process to make the training smoother and more efficient. We conducted experimental comparisons with the benchmark method using simulated and real data to verify its applicability and performance. The results confirm the method's excellent performance and generalization capabilities, as well as its excellence in increasing renewable energy utilization and extending equipment life.

关键词： Adaptive dynamic programming (ADP) dynamic residential scenarios optimal residential energy management smart grid

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Emergence of machine language: towards symbolic intelligence with neural networks

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National Science Review 2024年第4期11卷 27-30页

作者： Yuqi Wang Xu-Yao Zhang Cheng-Lin Liu Tieniu Tan Zhaoxiang Zhang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of Sciences

Representation learning is a core issue in artificial intelligence (AI). Currently,there exists a disparity in the choice of representation between humans and machines. Humans rely on discrete language for communicati... 详细信息

关键词： intelligence emergence language with machine networks neural symbolic towards

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Neural Liénard system: learning periodic manipulation skills through dynamical systems

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Science China(Information Sciences) 2024年第12期67卷 256-271页

作者： Haoyu ZHANG Long CHENG Yu ZHANG Yifan WANG School of Artificial Intelligence University of Chinese Academy of Sciences State Key Laboratory for Multi-modal Artificial Intelligence Systems Institute of AutomationChinese Academy of Sciences

Learning from demonstrations provides effective methods for teaching robot manipulation ***, capturing periodic manipulation skills remains challenging with the current techniques. To address this gap, we introduce the neural Liénard system(neural LS), a novel neural network framework that utilizes Liénard-type differential equations to create dynamical systems with stable and distinct limit cycles. We also introduce an innovative technique, which not only manages periodic trajectories across various dimensions but also handles trajectories with intersections, enhancing the capability of the robot for complex tasks. We provide a thorough theoretical analysis of neural LS, focusing on its stability and representational *** evaluations show that neural LS achieves superior performance in modeling complex limit cycles,surpassing the existing methods. We particularly emphasize its effectiveness in handling high-dimensional periodic motions and trajectories with intersections. In addition, we explore the adaptability and robustness of neural LS. A practical application involving the Franka Emika robot in a drawing task further demonstrates the real-world utility of neural LS, confirming its effectiveness and potential to equip robots with advanced periodic manipulation skills.

关键词： learning from demonstrations stable dynamical system periodic motions

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Learning future representation with synthetic observations for sample-efficient reinforcement learning

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Science China(Information Sciences) 2025年第5期68卷 20-37页

作者： Xin LIU Yaran CHEN Haoran LI Dongbin ZHAO State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences

Image-based reinforcement learning(RL) has proven its effectiveness for continuous visual control of embodied agents, where upstream representation learning largely determines the effectiveness of policy learning. Employing selfsupervised auxiliary tasks allows the agent to enhance visual representation in a targeted manner, thereby improving the policy performance and the RL sample efficiency. Prior advanced self-supervised RL methods all try to design better auxiliary objectives to extract more information from agent experience, while ignoring the training data constraints caused by experience limitations in RL training. In this article, we first try to break through this auxiliary training data constraint,proposing a novel RL auxiliary task named learning future representation with synthetic observations(LFS), which improves the self-supervised RL by enriching auxiliary training data. Firstly, a novel training-free method, named frame mask, is proposed to synthesize novel observations that may contain future information. Next, the latent nearest-neighbor clip(LNC)is correspondingly proposed to alleviate the impact of unqualified noise in synthetic observations. The remaining synthetic observations and real observations then together serve as the auxiliary training data to achieve a clustering-based temporal association task for advanced representation learning. LFS allows the agent to access and learn observations that are not present in the current experience but will appear in future training, thus enabling comprehensive visual understanding and an efficient RL process. In addition, LFS does not rely on rewards or actions, which means it has a wider scope of application(e.g., learning from video) than recent advanced RL auxiliary tasks. We conduct extensive experiments on challenging continuous visual control of complex embodied agents, including robot locomotion and manipulation. The results demonstrate that our LFS exhibits state-of-the-art sample efficiency

关键词： deep reinforcement learning (DRL) RL for embodied agents RL for continuous control image-based RL self-supervised learning RL visual pre-training

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Lightweight multifunctional metamaterial with low-frequency vibroacoustic reduction and load-bearing performances

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Applied Mathematics and Mechanics(English Edition) 2025年第3期46卷 403-422页

作者： Qi JIA Dianlong YU Donghai HAN Jihong WEN National Key Laboratory of Equipment State Sensing and Smart Support College of Intelligence Science and TechnologyNational University of Defense TechnologyChangsha 410073China

Metamaterials can control and manipulate acoustic/elastic waves on a subwavelength scale using cavities or additional ***,the large cavity and weak stiffness components of traditional metamaterials may cause a conflict between vibroacoustic reduction and load-bearing capacity,and thus limit their ***,we propose a lightweight multifunctional metamaterial that can simultaneously achieve low-frequency sound insulation,broadband vibration reduction,and excellent load-bearing performance,named as vibroacoustic isolation and bearing metamaterial(VIBM).The advent of additive manufacturing technology provides a convenient and reliable method for the fabrication of VIBM *** results show that the compressive strength of the VIBM is as high as 9.71 MPa,which is nearly 87.81%higher than that of the conventional grid structure(CGS)under the same volume ***,the vibration and sound transmission are significantly reduced over a low and wide frequency range,which agrees well with the experimental data,and the reduction degree is obviously larger than that obtained by the *** design strategy can effectively realize the key components of metamaterials and improve their application scenarios.

关键词： multifunctional metamaterial low-frequency sound insulation broadband vibration reduction load bearing

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Automatic Control of Magnetic Helical Microrobots Docking with Target Objects in Liquid Environments

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Journal of Bionic Engineering 2025年第2期22卷 574-584页

作者： Fu Zhao Haoran Rong Lefeng Wang State Key Laboratory of Robotics and Systems Harbin Institute of TechnologyHarbin150080China Key Laboratory of Advanced Manufacturing and Intelligent Technology Ministry of EducationHarbin University of Science and TechnologyHarbin150080China

Bio-inspired magnetic helical microrobots have great potential for biomedical and micromanipulation applications. Precise interaction with objects in liquid environments is an important prerequisite and challenge for helical microrobots to perform various tasks. In this study, an automatic control method is proposed to realize the axial docking of helical microrobots with arbitrarily placed cylindrical objects in liquid environments. The docking process is divided into ascent, approach, alignment, and insertion stages. First, a 3D docking path is planned according to the positions and orientations of the microrobot and the target object. Second, a steering-based 3D path-following controller guides the helical microrobot to rise away from the container bottom and approach the target along the path. Third, based on path design with gravity compensation and steering output limits, alignment of position and orientation can be accomplished simultaneously. Finally, the helical microrobot completes the docking under the rotating magnetic field along the target orientation. Experiments verified the automatic docking of the helical microrobot with static targets, including connecting with micro-shafts and inserting into micro-tubes. The object grasping of a reconfigurable helical microrobot aided by 3D automatic docking was also demonstrated. This method enables precise docking of helical microrobots with objects, which might be used for capture and sampling, in vivo navigation control, and functional assembly of microrobots.

关键词： Magnetic actuation Helical microrobots Visual servo Motion control Docking

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Mission-oriented capability evaluation for combat network based on operation loops

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Defence technology（防务技术） 2024年第12期42卷 156-175页

作者： Yang Wang Junyong Tao Xiaoke Zhang Guanghan Bai Yunan Zhang Laboratory of Science and Technology on Integrated Logistics Support College of Intelligence Science and TechnologyNational University of Defense TechnologyChangsha 410073China National Key Laboratory for Complex Systems Simulation Beijing 100101China

With continuous growth in scale,topology complexity,mission phases,and mission diversity,challenges have been placed for efficient capability evaluation of modern combat *** at the problems of insufficient mission consideration and single evaluation dimension in the existing evaluation approaches,this study proposes a mission-oriented capability evaluation method for combat systems based on operation ***,a combat network model is given that takes into account the capability properties of combat ***,based on the transition matrix between combat nodes,an efficient algorithm for operation loop identification is proposed based on the Breadth-First *** the mission-capability satisfaction of nodes,the effectiveness evaluation indexes for operation loops and combat network are proposed,followed by node importance *** a case study of the combat scenario involving space-based support against surface ships under different strategies,the effectiveness of the proposed method is *** results indicated that the ROI-priority attack method has a notable impact on reducing the overall efficiency of the network,whereas the O-L betweenness-priority attack is more effective in obstructing the successful execution of enemy attack missions.

关键词： Combat network Operation loop identification Mission-oriented Network reliability Network effectiveness evaluation Strike strategies

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A Diffusion Model for Traffic Data Imputation

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 606-617页

作者： Bo Lu Qinghai Miao Yahui Liu Tariku Sinshaw Tamir Hongxia Zhao Xiqiao Zhang Yisheng Lv Fei-Yue Wang the School of Artificial Intelligence University of Chinese Academy of Sciences Baidu Inc. IEEE the State Key Laboratory for Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Meituan Guangdong University of Technology the Department of Transportation Engineering School of Transportation Science and Technology Harbin Institute of Technology

Imputation of missing data has long been an important topic and an essential application for intelligent transportation systems(ITS) in the real world. As a state-of-the-art generative model, the diffusion model has proven highly successful in image generation, speech generation, time series modelling *** now opens a new avenue for traffic data imputation. In this paper, we propose a conditional diffusion model, called the implicit-explicit diffusion model, for traffic data imputation. This model exploits both the implicit and explicit feature of the data simultaneously. More specifically, we design two types of feature extraction modules, one to capture the implicit dependencies hidden in the raw data at multiple time scales and the other to obtain the long-term temporal dependencies of the time series. This approach not only inherits the advantages of the diffusion model for estimating missing data, but also takes into account the multiscale correlation inherent in traffic data. To illustrate the performance of the model, extensive experiments are conducted on three real-world time series datasets using different missing *** experimental results demonstrate that the model improves imputation accuracy and generalization capability.

关键词： Data imputation diffusion model implicit feature time series traffic data

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Data-Driven Fault-Tolerant Bipartite Consensus Tracking for Multi-Agent systems With a Non-Autonomous Leader

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 279-281页

作者： Yan Zhou Guanghui Wen Jialing Zhou Tao Yang the Department of Systems Science Southeast University the State Key Laboratory of CNS/ATM Beijing Institute of Technology the Beijing Institute of Technology (Zhuhai) the State Key Laboratory of Synthetical Automation for Process Industries Northeastern University

Dear Editor,This letter studies the bipartite consensus tracking problem for heterogeneous multi-agent systems with actuator faults and a leader's unknown time-varying control input. To handle such a problem, the continuous fault-tolerant control protocol via observer design is developed. In addition, it is strictly proved that the multi-agent system driven by the designed controllers can still achieve bipartite consensus tracking after faults occur.

关键词： Time varying control systems

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