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IEEE Transactions on Intelligent Vehicles 2023年 1-14页

作者： Wang, Fei-Yue Hu, Jia Lai, Jintao State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing China ZhongTe Distinguished Chair in Cooperative Automation Professor Key Laboratory of Road and Traffic Engineering of the Ministry of Education Tongji University Shanghai China Key Laboratory of Road and Traffic Engineering of the Ministry of Education Tongji University Shanghai China

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Exploring Latent Transferability of feature components

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PATTERN RECOGNITION 2025年 160卷

作者： Wang, Zhengshan Chen, Long He, Juan Yang, Linyao Wang, Fei-Yue Univ Macau Fac Sci & Technol Dept Comp & Informat Sci Macau 999078 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

Feature disentanglement techniques have been widely employed to extract transferable (domain-invariant) features from non-transferable (domain-specific) features in Unsupervised Domain Adaptation (UDA). However, due to the complex interplay among high-dimensional features, the separated "non-transferable" features may still be partially informative. Suppressing or disregarding them, as commonly employed in previous methods, can overlook the inherent transferability. In this work, we introduce two concepts: Partially Transferable Class Features and Partially Transferable Domain Features (PTCF and PTDF), and propose a succinct feature disentanglement technique. Different with prior works, we do not seek to thoroughly peel off the nontransferable features, as it is challenging practically. Instead, we take the two-stage strategy consisting of rough feature disentanglement and dynamic adjustment. We name our model as ELT because it can systematically Explore Latent Transferability of feature components. ELT can automatically evaluate the transferability of internal feature components, dynamically giving more attention to features with high transferability and less to features with low transferability, effectively solving the problem of negative transfer. Extensive experimental results have proved its efficiency. The code and supplementary file will be available at https://***/ njtjmc/ELT.

关键词： Unsupervised domain adaptation Feature disentanglement Adversarial learning Dynamic learning

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Incremental networked predictive control of multi-agent systems with plant-model mismatch and random communication constraints

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IET control THEORY AND APPLICATIONS 2022年第1期16卷 51-65页

作者： Dong, Zhe Zheng, Chang-Bing Pang, Zhong-Hua Sun, Jian North China Univ Technol Key Lab Fieldbus Technol & Automat Beijing Beijing 100144 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Beijing Inst Technol Sch Automat State Key Lab Intelligent Control & Decis Comple Beijing Peoples R China

In this paper, we discuss the cooperative output tracking problem for networked multi-agent systems (NMASs) with plant-model mismatch as well as random communication constraints in the forward and feedback channels of each agent. In order to compensate actively for random communication constraints, that is, network-induced delays and packet dropouts, an incremental networked predictive control scheme based on a state observer is proposed, which consists of a data buffer, an incremental networked predictor, and a network delay compensator. For both the plant-model mismatch case and the plant-model match case, the stability conditions of resulting closed-loop NMASs are derived, respectively. Using the proposed method, the simulation results for three DC motor systems are presented to indicate that desired tracking performance can be achieved.

关键词： Algebra control system analysis and synthesis methods Stability in control theory Optimal control Multivariable control systems Self-adjusting control systems Nonlinear control systems

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Constrained-Cost Adaptive Dynamic Programming for Optimal control of Discrete-Time Nonlinear systems

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systems 2024年第3期35卷 3251-3264页

作者： Wei, Qinglai Li, Tao 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 Macau 999078 Peoples R China

For discrete-time nonlinear systems, this research is concerned with optimal control problems (OCPs) with constrained cost, and a novel value iteration with constrained cost (VICC) method is developed to solve the optimal control law with the constrained cost functions. The VICC method is initialized through a value function constructed by a feasible control law. It is proven that the iterative value function is nonincreasing and converges to the solution of the Bellman equation with constrained cost. The feasibility of the iterative control law is proven. The method to find the initial feasible control law is given. Implementation using neural networks (NNs) is introduced, and the convergence is proven by considering the approximation error. Finally, the property of the present VICC method is shown by two simulation examples.

关键词： Costs Cost function Performance analysis Optimal control Nonlinear systems Dynamic programming Convergence Adaptive dynamic programming (ADP) approximate dynamic programming constrained cost optimal control reinforcement learning value iteration (VI)

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IMTRAC: Image-Based Modeling for Transformative Traffic control via Foundation Models 17

IMTRAC: Image-Based Modeling for Transformative Traffic Cont...

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17th IFAC Symposium on control of Transportation systems (CTS)

作者： Dai, Xingyuan Tang, Yiqing Guo, Chao Chen, Yuanyuan Lv, Yisheng Wang, Fei-Yue Chinese Acad Sci State Key Lab Multimodal Artificial Intelligence Inst Automat Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Shanghai Artificial Intelligence Lab Shanghai 200232 Peoples R China

This paper introduces the Image-based Modeling for Transformative Traffic control (IMTRAC) framework, which revolutionizes urban traffic management by integrating image-based representations, foundation models, and dynamic signaling. It transitions from textual to visual data analysis, transforming complex traffic scenes into quantifiable insights. The image-based representation is then analyzed by foundation models to infer refined and effective traffic control policies. Our experimental results show marked improvements in traffic flow and reductions in waiting times, highlighting IMTRAC's ability to outperform traditional control methods. The proposed IMTRAC promises a significant leap towards smarter, efficient urban traffic systems, leveraging the synergy of advanced imaging techniques and multi-modal foundation models to enhance control decisions. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Foundation models Intelligent transportation systems Traffic control

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Computational Experiments for complex Social systems:Experiment Design and Generative Explanation

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 1022-1038页

作者： Xiao Xue Deyu Zhou Xiangning Yu Gang Wang Juanjuan Li Xia Xie Lizhen Cui Fei-Yue Wang the School of Computer Software College of Intelligence and ComputingTianjin UniversityTianjin 300350China the School of Software and the Joint SDUNTU Centre for Artificial Intelligence Research(C-FAIR) Shandong UniversityJinan 250101China the Information School Xi’an University of Financial and EconomicsXi’an 710100China the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Hainan University Haikou 570228China the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 1001901China

Powered by advanced information technology,more and more complex systems are exhibiting characteristics of the cyber-physical-social systems(CPSS).In this context,computational experiments method has emerged as a novel approach for the design,analysis,management,control,and integration of CPSS,which can realize the causal analysis of complex systems by means of“algorithmization”of“counterfactuals”.However,because CPSS involve human and social factors(e.g.,autonomy,initiative,and sociality),it is difficult for traditional design of experiment(DOE)methods to achieve the generative explanation of system *** address this challenge,this paper proposes an integrated approach to the design of computational experiments,incorporating three key modules:1)Descriptive module:Determining the influencing factors and response variables of the system by means of the modeling of an artificial society;2)Interpretative module:Selecting factorial experimental design solution to identify the relationship between influencing factors and macro phenomena;3)Predictive module:Building a meta-model that is equivalent to artificial society to explore its operating ***,a case study of crowd-sourcing platforms is presented to illustrate the application process and effectiveness of the proposed approach,which can reveal the social impact of algorithmic behavior on“rider race”.

关键词： Agent-based modeling computational experiments cyber-physical-social systems(CPSS) generative deduction generative experiments meta model

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Adaptive bias-variance trade-off in advantage estimator for actor-critic algorithms

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NEURAL NETWORKS 2024年 169卷 764-777页

作者： Chen, Yurou Zhang, Fengyi Liu, Zhiyong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Shanghai Peoples R China

Actor-critic methods are leading in many challenging continuous control tasks. Advantage estimators, the most common critics in the actor-critic framework, combine state values from bootstrapping value functions and sample returns. Different combinations balance the bias introduced by state values and the variance returned by samples to reduce estimation errors. The bias and variance constantly fluctuate throughout training, leading to different optimal combinations. However, existing advantage estimators usually use fixed combinations that fail to account for the trade-off between minimizing bias and variance to find the optimal estimate. Our previous work on adaptive advantage estimation (AAE) analyzed the sources of bias and variance and offered two indicators. This paper further explores the relationship between the indicators and their optimal combination through typical numerical experiments. These analyses develop a general form of adaptive combinations of state values and sample returns to achieve low estimation errors. Empirical results on simulated robotic locomotion tasks show that our proposed estimators achieve similar or superior performance compared to previous generalized advantage estimators (GAE).

关键词： Reinforcement Learning Policy gradient Actor-critic Value function Bias-variance trade-off

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

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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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