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Immersion and Invariance Adaptive control for a Class of Nonlinear systems With Uncertain Parameters

INTERNATIONAL JOURNAL OF control AUTOMATION AND systems 2024年第9期22卷 2746-2757页

作者： Wang, Jian-Hui He, Guang-Ping Bian, Gui-Bin Yuan, Jun-Jie Geng, Shi-Xiong Zhang, Cheng-Jie Zhao, Cheng-Hao Beijing Univ Technol Fac Mat & Mfg Beijing 100124 Peoples R China North China Univ Technol Dept Mech & Elect Engn Beijing 100144 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

An adaptive control method based on immersion and invariance (I&I) is presented in a class of nonlinear systems with time-varying uncertain parameters. A parameter estimation law based on reference models using I&I is designed to accelerate the convergence of estimated parameters to the true value, enabling the closed-loop system to reach the predefined target system on the manifold more quickly and reducing the energy consumption of the system. The inherent integrability obstacles in I&I are overcome by using dynamic scaling techniques, reducing the complexity of controller design. Stability analysis of the closed-loop system demonstrates that the proposed control method can achieve asymptotic stability control of the target system, and verified the robustness of the closed-loop system in the face of external disturbances. Finally, simulations of attitude tracking control demonstrate the effectiveness and superiority of the proposed method.

关键词： Adaptive control immersion and invariance nonlinear system parameter estimation

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A DAOs-Based Publishing System for Advancing Open Access 4

A DAOs-Based Publishing System for Advancing Open Access

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4th IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Ma, Siji Li, Juanjuan Guan, Sangtian Ni, Qinghua Lin, Fei Zhang, Tengchao Huang, Jun Wang, Fei-Yue Macau University of Science and Technology Faculty of Innovation Engineering 999078 China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Institute of Automation Beijing100190 China

ISBN: (纸本)9798350349252

The development of the scientific publishing system has remarkably enhanced global accessibility to research findings and substantially increased the visibility and dissemination of academic publications. However, significant challenges still exist in effectively safeguarding the intellectual property rights of contributors, such as the unauthorized usage of materials, the complexity of enforcing intellectual property rights across various legal jurisdictions, and high instances of plagiarism and content misuse. Additionally, financial barriers related to open access may restrict the participation of economically disadvantaged researchers, potentially biasing scientific records towards more affluent research initiatives. To address these issues, a novel decentralized framework is formulated to ensure truly open access. This framework leverages blockchain for immutable record-keeping and clear attribution of authorship, to prevent unauthorized usage and plagiarism. Besides, it also utilizes a copyright-sharing model based on decentralized autonomous organizations and operations (DAOs), where smart contracts automatically enforce copyright and access policies to ensure fair compensation for authors and researchers. Furthermore, the copyright sharing model based on non-fungible tokens (NFT) and gradual ownership optimization (GOO) mechanism is proposed to ensure fair and accurate recognition and compensation for scholarly contributions. © 2024 IEEE.

关键词： Decentralized finance

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A Novel Approach for Predictable Governance of Decentralized Autonomous Organizations Based on Parallel Intelligence

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2023年第5期53卷 3092-3103页

作者： Ding, Wenwen Liang, Xiaolong Hou, Jiachen Li, Juanjuan Rouabah, Younes Yuan, Yong Wang, Fei-Yue Macau Univ Sci & Technol Inst Syst Engn Macau Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Renmin Univ China Res Ctr Finance Computat & Digital Engn Sch Math & Engn Minist Educ Beijing 100872 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Macau Inst Syst Engn Macau Peoples R China

Decentralized autonomous organizations (DAOs) have become an indispensable part of digital infrastructure in recent years. The unique organizational characteristics and functional structure empower them to become an effective tool for solving corporate governance issues, including contract risks, principal-agent dilemmas, etc. However, DAOs themselves also face a variety of governance issues. On one hand, as a new economic organization model, the existing corporate governance theories and methods are no longer fully applicable to DAOs. On the other hand, unpredictable logic vulnerabilities and code loopholes in the governance mechanism might cause devastating damage to DAOs. The parallel intelligence theory based on the ACP method (i.e., artificial systems, computational experiments, and parallel execution) is an elegant research paradigm and a practical approach tailored to solving these challenges. As such, we propose a novel parallel governance framework for DAOs based on the parallel intelligence theory and further discuss its technical methodology and implementation model. Furthermore, we construct a parallel governance system for GnosisDAO and conduct computational experiments to validate the effectiveness of its governance mechanism. The experimental results not only confirm the defects of the GnosisDAO governance mechanism but also illustrate parallel governance as a useful research direction to solve existing governance problems of DAOs.

关键词： Blockchain decentralized autonomous organizations (DAOs) GnosisDAO parallel governance parallel intelligence smart contracts

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An Improved Strategy for Active Visual Odometry Based on Robust Adaptive Unscented Kalman Filter

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2024年第8期71卷 9172-9181页

作者： Yuwen, Xuan Chen, Lu Chen, Long Zhang, Hui Beihang Univ Sch Transportat Sci & Engn Beijing 102206 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 102206 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

For active visual odometry (VO), the visual information detected by the positioning camera matters. By actively controlling the gaze of the camera, the VO tends to retrieve some effective factors, such as textured objects with rich feature points. However, the active rotation of the camera can introduce more uncertainties, which may cause additional gross errors. Therefore, it is a considerable problem for the active VO to avoid the adverse effects of the active rotation while benefiting from it. To address the issue, this article proposes an improved strategy based on a robust adaptive unscented Kalman filter (RAUKF) and the relative posture of the active camera for the active VO. The pose outputted from the VO is transformed to the vehicle pose by means of the pose of the pan-tilt, and the transformed pose is treated as the measurement of the vehicle motion. Subsequently, the measurement is fed to the RAUKF to generate a refined estimation of the vehicle pose, which is then inversely transformed to obtain a more precise camera pose. Finally, the feature points cloud of the VO can be corrected according to the refined camera pose. The proposed method effectively improves the positioning accuracy of the active VO, as demonstrated through numerical and real-vehicle tests. The relative translation error and the relative rotation error of the proposed method are 1.6% and 0.0037 deg/m in average, which reduce 96.22% and 94.79% compared with the raw outputs of the active VO.

关键词： Active simultaneous localization and mapping (SLAM) outlier detection and correction robust adaptive unscented Kalman filter (RAUKF) visual odometry (VO)

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Chat with ChatGPT on Industry 5.0:Learning and Decision-Making for Intelligent Industries

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 831-834页

作者： Fei-Yue Wang Jing Yang Xingxia Wang Juanjuan Li Qing-Long Han State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia

THE current ChatGPT phenomenon has signaled a new era of Artificial Intelligence moving from Algorithmic Intelligence to Linguistic Intelligence where interactive activities between actual and artificial,real and virtual,human and machine play an active and important role online and in *** IEEE/CAA JAS,we are interested in investigating the impact and significance of this new era on industrial development,especially control and automation for manufacturing and production.

关键词： GPT artificial automation

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Metacracy: A New Governance Paradigm Beyond Bounded Intelligence

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2024年第6期11卷 7072-7085页

作者： Wang, Fei-Yue Qin, Rui Li, Juanjuan Kovacs, Levente Hu, Bin Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Obuda Univ Univ Res & Innovat Ctr Physiol Controls Res Ctr H-1034 Budapest Hungary Beijing Inst Technol Sch Med Technol Beijing 100081 Peoples R China

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Geometry-Guided Neural Implicit Surface Reconstruction

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2024年第6期11卷 7898-7908页

作者： Li, Keqiang Wu, Huaiyu Zhao, Mingyang Fang, Qihang Yang, Jian Zhang, Ao Shen, Zhen Xiong, Gang 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 Hong Kong Inst Sci & Innovat Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Multiview 3-D reconstruction holds considerable promise across a wide applications in social manufacturing. Conducting in-depth research on precise and robust multiview 3-D reconstruction holds the potential to significantly empower the domain of social manufacturing. Recently, there has been a burgeoning interest in the domain of neural implicit surfaces learning through volume rendering for the purpose of multiview reconstruction without 3-D supervision. Conventional approaches often overlook explicit multiview geometry constraints, resulting in shortcomings in generating consistent surface reconstructions and recovering fine details. To solve this, we propose geometry-guided neural implicit surface (GG-NeuS), a geometry-guided neural implicit surfaces learning method for multiview surface reconstruction. Our model places a stronger emphasis on maintaining geometry consistency, significantly enhancing the quality of reconstruction. First, we enforce multiview geometry constraints on the surface points by locating the zero-level set of signed distance function (SDF). Second, we incorporate normal cues, predicted by general-purpose monocular estimators, to substantially recover fine geometric details. Additionally, we introduce a voxel-based surface reconstruction methodology that strikes an optimal balance between training time and reconstruction quality. Through comprehensive qualitative and quantitative experiments and analyses, we demonstrate that GG-NeuS successfully reconstructs fine-grained surface details and achieves superior surface reconstruction quality than state-of-the-art approaches.

关键词： Surface reconstruction Image reconstruction Rendering (computer graphics) Three-dimensional displays Image color analysis Accuracy Surface treatment Explicit geometry constraints multiview images neural implicit learning surface reconstruction

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A New Era of Intelligent Vehicles and Intelligent Transportation systems: Digital Twins and Parallel Intelligence

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第4期8卷 2619-2627页

作者： Wang, Ziran Lv, Chen Wang, Fei-Yue Purdue Univ Coll Engn W Lafayette IN 47907 USA Nanyang Technol Univ Sch Mech & Aerosp Engn Singapore 639798 Singapore Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing Peoples R China

By March 25, our transaction has received a total of 619 original submissions. Our current average number of submissions per day (SPD) rate is 7.37. Given this SPD, I would like to expand our editorial board to includ...

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A Novel Scenarios Engineering Methodology for Foundation Models in Metaverse

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2023年第4期53卷 2148-2159页

作者： Li, Xuan Tian, Yonglin Ye, Peijun Duan, Haibin Wang, Fei-Yue Peng Cheng Lab Dept Math & Theories Shenzhen 518000 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Key Lab Virtual Real Technol & Syst Beijing 100083 Peoples R China

Foundation models are used to train a broad system of general data to build adaptations to new bottlenecks. Typically, they contain hundreds of billions of hyperparameters that have been trained with hundreds of gigabytes of data. However, this type of black-box vulnerability places foundation models at risk of data poisoning attacks that are designed to pass on misinformation or purposely introduce machine bias. Moreover, ordinary researchers have not been able to completely participate due to the rise in deployment standards. This study introduces the theoretical framework of scenarios engineering (SE) for building accessible and reliable foundation models in metaverse, namely, "SE-enabled foundation models in metaverse." Particularly, the research framework comprises a six-layer architecture (infrastructure layer, operation layer, knowledge layer, intelligence layer, management layer, and interaction layer), which can provide controllability, trustworthiness, and interactivity for the foundation models in metaverse. This creates closed-loop, virtual-real, and human-machine environments that provides the best indices and goals for the foundation models, which allows us to fully validate and calibrate the corresponding models. Then, examples of use cases from the automotive industry are listed to provide transparency on the possible use and benefits of our approach. Finally, the open research topics of related frameworks are discussed.

关键词： Foundation models knowledge automation management metaverse parallel intelligence scenarios engineering (SE)

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Event-Triggered Communication Network With Limited-Bandwidth Constraint for Multi-Agent Reinforcement Learning

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systems 2023年第8期34卷 3966-3978页

作者： Hu, Guangzheng Zhu, Yuanheng Zhao, Dongbin Zhao, Mengchen Hao, Jianye 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 Huawei Noahs Ark Lab Beijing 100085 Peoples R China

Communicating agents with each other in a distributed manner and behaving as a group are essential in multi-agent reinforcement learning. However, real-world multi-agent systems suffer from restrictions on limited bandwidth communication. If the bandwidth is fully occupied, some agents are not able to send messages promptly to others, causing decision delay and impairing cooperative effects. Recent related work has started to address the problem but still fails in maximally reducing the consumption of communication resources. In this article, we propose an event-triggered communication network (ETCNet) to enhance communication efficiency in multi-agent systems by communicating only when necessary. For different task requirements, two paradigms of the ETCNet framework, event-triggered sending network (ETSNet) and event-triggered receiving network (ETRNet), are proposed for learning efficient sending and receiving protocols, respectively. Leveraging the information theory, the limited bandwidth is translated to the penalty threshold of an event-triggered strategy, which determines whether an agent at each step participates in communication or not. Then, the design of the event-triggered strategy is formulated as a constrained Markov decision problem and reinforcement learning finds the feasible and optimal communication protocol that satisfies the limited bandwidth constraint. Experiments on typical multi-agent tasks demonstrate that ETCNet outperforms other methods in reducing bandwidth occupancy and still preserves the cooperative performance of multi-agent systems at the most.

关键词： Bandwidth Protocols Reinforcement learning Task analysis Optimization Communication networks Multi-agent systems Event trigger limited bandwidth multi-agent communication multi-agent reinforcement learning (MARL)

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