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Adaptive Fuzzy Nonsingular Fixed-Time Bipartite Consensus Tracking Using Adding Power Integration Technique for Stochastic Nonlinear Constrained MASs

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IEEE TRANSACTIONS ON FUZZY systems 2025年第3期33卷 947-958页

作者： Niu, Ben Zhu, Yan Liu, Yingying Shang, Zihao Wang, Ding Wang, Huanqing Wang, Wencheng Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Peoples R China Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Bohai Univ Dept Math Sci Jinzhou 121000 Peoples R China Bohai Univ Automatizat Inst Jinzhou 121000 Peoples R China Weifang Univ Coll Machinery & Automat Weifang 261061 Peoples R China

In this article, the adaptive fuzzy fixed-time bipartite consensus tracking control problem is studied for stochastic nonlinear multi-agent systems with unknown control gains and time-varying output constraints. First, in order to address the difficulties arising from the unknown control gains, the Nussbaum technique is employed. In the meantime, the $tan$-type nonlinear mapping function is introduced, which guarantees the predefined output constraints are not violated. Then, different from the previous control strategies in which they only focused on the balanced directed topology, the classification optimization algorithm is presented to accomplish the bipartite consensus tracking control according to the structurally unbalanced directed topology. Besides, by combining the adaptive backstepping technique with the adding power integration methodology, the nonsingular fixed-time control strategy is proposed. The proposed adaptive fuzzy fixed-time control strategy ensures that the bipartite consensus tracking errors converge to a region near zero in fixed time and all the signals in the closed-loop system are bounded in probability. Last, the effectiveness of the presented control scheme is demonstrated with a simulation example.

关键词： Adding power integration technique bipartite consensus control fixed-time fixed-time stochastic nonlinear multi-agent systems (MASs) stochastic nonlinear multi-agent systems (MASs) time-varying output constraints time-varying output constraints time-varying output constraints

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Safety-Balanced Driving-Style Aware Trajectory Planning in Intersection Scenarios With Uncertain Environment

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第4期8卷 2888-2898页

作者： Wang, Xiao Tang, Ke Dai, Xingyuan Xu, Jintao Xi, Jinhao Ai, Rui Wang, Yuxiao Gu, Weihao Sun, Changyin Anhui Univ Sch Artificial Intelligence Hefei 230031 Peoples R China Haomo Technol Co Ltd Beijing 100192 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 266114 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

This paper proposes a two-stage trajectory planning method for self-driving vehicles (SDVs) in intersection scenarios with uncertain social circumstances while considering other traffic participants, which are human-driving vehicles (HDVs) with different driving styles. The mixture-of-experts approach is first utilized to learn from human-driving trajectory data to construct a multimodal motion planner, which uses a Transformer to model the interactions between vehicles by explicitly considering their driving styles to facilitate the integrated network to achieve scene-consistent multimodal trajectory prediction and candidate trajectory generation. Second, based on the generated trajectories for the SDV and the predicted trajectories for the other HDVs, each candidate planning trajectory is evaluated via a safety-balanced value function. After that, the trajectory with the highest value is selected for implementation. Such a method plans a safe and efficient driving trajectory in complex and uncertain scenarios. The experimental results demonstrate the efficiency and effectiveness of the designed method as well as the robustness and reasonableness of the SDVs' maneuver decisions at an intersection considering the behavioral dynamics of HDVs.

关键词： Trajectory Planning Trajectory planning Safety Predictive models Training Data models Autonomous vehicles trajectory planning social interactions uncertain circumstances

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Neuro-Optimal Trajectory Tracking With Value Iteration of Discrete-Time Nonlinear Dynamics

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systems 2023年第8期34卷 4237-4248页

作者： Wang, Ding Ha, Mingming Cheng, Long Beijing Univ Technol Fac Informat Technol Beijing Key Lab Computat Intelligence & Intellige Beijing Lab Smart Environm Protect Beijing 100124 Peoples R China Beijing Univ Technol Beijing Inst Artificial Intelligence Beijing 100124 Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 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

In this article, a novel neuro-optimal tracking control approach is developed toward discrete-time nonlinear systems. By constructing a new augmented plant, the optimal trajectory tracking design is transformed into an optimal regulation problem. For discrete-time nonlinear dynamics, the steady control input corresponding to the reference trajectory is given. Then, the value-iteration-based tracking control algorithm is provided and the convergence of the value function sequence is established. Therein, the approximation error between the iterative value function and the optimal cost is estimated. The uniformly ultimately bounded stability of the closed-loop system is also discussed in detail. Moreover, the iterative heuristic dynamic programming (HDP) algorithm is implemented by involving the critic and action components, where some new updating rules of the action network are provided. Finally, two examples are used to demonstrate the optimality of the present controller as well as the effectiveness of the proposed method.

关键词： Trajectory Heuristic algorithms Convergence Trajectory tracking Stability criteria Optimal control Dynamic programming Adaptive critic design discrete-time nonlinear plants neuro-optimal trajectory tracking uniformly ultimately bounded stability value iteration

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control Sequences Generation for Testing Vehicle Extreme Operating Conditions Based on Latent Feature Space Sampling

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第4期8卷 2712-2722页

作者： Zhu, Yuxuan Li, Zhiheng Wang, Feiyue Li, Li Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Pearl River Delta Res Inst Tsinghua Guangzhou 510530 Peoples R China Tsinghua Univ Dept Automat BNRist Beijing 100084 Peoples R China Tsinghua Univ Grad Sch Shenzhen Shenzhen 518055 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100080 Peoples R China

Extreme operating conditions refer to the critical dynamic state during vehicle operation. The lack of experimental data under critical conditions is one of the fundamental problems in the study. To solve the problem, we design an LSTM-VAE based generating model to generate rational control sequences that can push vehicles toward extreme operating conditions and used simulation tests to analyze them. Specifically, we train the Encoder to study the basic driving logic of the control sequences collected during free-drive tests by human drivers, forming a low-dimension latent feature space. Then, we sample from specified regions in the latent feature space and use the Decoder to generate new control sequences. Finally, we use the sequences as the control input of the 27-DoF high-precision vehicle dynamic simulation platform and analyze the variations of simulated vehicle dynamics. We conduct different experiments and validate the method from different aspects. Results reveal that by sampling from specific regions of the latent feature space, we get a higher chance to generate desired control sequences for extreme operating conditions.

关键词： Aerospace electronics Vehicle dynamics Trajectory Space vehicles Data models Intelligent vehicles Computational modeling Extreme operating conditions parallel learning vehicle testing

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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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Configuration and characters of novel Li2TiO3 tritium breeder cubic units with tuned channel structure via lithography-based 3D printing

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JOURNAL OF THE EUROPEAN CERAMIC SOCIETY 2023年第5期43卷 2151-2163页

作者： Cai, Liang Tan, Guangfan Hu, Xin Shen, Zhen Wan, Li Zhang, Yingchun Univ Sci & Technol Beijing Sch Mat Sci & Engn 30 Xueyuan Rd Beijing 100083 Peoples R China Chinese Acad Sci Inst Automat Beijing Engn Res Ctr Intelligent Syst & Technol State Key Lab Management & Control Complex Syst 95 Zhongguancun E Rd Beijing 100190 Peoples R China Ten Dimens Guangdong Technol Co Ltd 33 Informat Rd Foshan 528225 Peoples R China

Li2TiO3 is regarded as a vital tritium breeder candidate due to its favorable properties in Helium-Cooled Pebble Bed. However, the point contact stress breakage for traditional pebble structure can cause the blocking of tritium recovery system, and seriously reduce the safety and tritium self-sufficiency ability of fusion reactor. For addressing above problems, in this paper, complex-shaped Li2TiO3 cubic units were manufactured firstly via lithography-based 3D printing. According to the principle of tritium transport, the cubic unit structures with ordered channels were built and evaluated by finite element analysis. The photocuring parameters were opti-mized to improve printing accuracy by establishing an overgrowth model. After optimizing the sintering schedule, the Li2TiO3 cubic units with excellent relative density (92.3%TD) and packing factor (80.99%) were yielded, and it also had an outstanding compressive strength (84.4 MPa). The results of thermal cycling test shown that the Li2TiO3 cubic units possessed a prominent thermal cyclic durability.

关键词： 3D printing Digital light process Tritium breeder material Lithium titanate

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Real-Time Marker Localization Learning for GelStereo Tactile Sensing

Real-Time Marker Localization Learning for GelStereo Tactile...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Liu, Shujuan Cui, Shaowei Zhang, Chaofan Cai, Yinghao Wang, Shuo University of Science and Technology Beijing China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Chinese Academy of Sciences Institute of Automation Beijing100190 China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai200031 China

ISBN: (纸本)9781665481090

Visuotactile sensing technology is becoming more popular in tactile sensing, but the effectiveness of the existing marker detection/localization methods remains to be further explored. Instead of contour-based blob detection, this paper presents a learning-based marker localization network for GelStereo visuotactile sensing called Marknet. Specifically, the Marknet presents a grid regression architecture to incorporate the distribution of the GelStereo markers. Furthermore, a marker rationality evaluator (MRE) is modelled to screen suitable prediction results. The experimental results show that the Marknet combined with MRE achieves 93.90% precision for irregular markers in contact areas, which outperforms the traditional contour-based blob detection method by a large margin of 42.32%. Meanwhile, the proposed learning-based marker localization method can achieve better realtime performance beyond the blob detection interface provided by the OpenCV library through GPU acceleration, which we believe will lead to considerable perceptual sensitivity gains in various robotic manipulation tasks. © 2022 IEEE.

关键词： Chemical detection

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Kinematics Analysis of a Manipulator for the Power Transmission Line Maintenance Robot 42

Kinematics Analysis of a Manipulator for the Power Transmiss...

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42nd Chinese control Conference, CCC 2023

作者： Chen, Minghao Li, En Zhang, Feng Guo, Rui Liang, Zize Zhang, Yuwei Institute of Automation The State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Shandong Luneng Intelligence Technology Co. Ltd. Jinan250001 China The State Grid Shandong Electric Power Company Jinan250001 China

ISBN: (纸本)9789887581543

With the remarkable development of the power grid, traditional manual maintenance modes cannot meet the requirement of efficiency and reliability. Robotic technology is applied for power line maintenance to bring down risks and ensure inspectors' safety. Based on the power transmission line maintenance platform, a 5-DOF manipulator is designed to perform the maintenance tasks for the quad bundle conductors. Then the kinematics analysis of the manipulator is analyzed. The manipulator doesn't meet the Pieper criterion. So an improved adaptive particle swarm optimization (APSO) is proposed for figuring out the inverse kinematics. The weights of the pose and the constraint of the prismatic joint are introduced to the algorithm. What's more, half of the particles are initialized around the incipient value of joints and half of the particles are randomly initialized with the constraint of joints in this algorithm. Results show that the manipulator is able to work in the environment of quad bundle conductors and the proposed algorithm can improve the precision of inverse kinematics and prevent the leap of joints. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Maintenance

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Learning Dynamic Graph for Overtaking Strategy in Autonomous Driving

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2023年第11期24卷 11921-11933页

作者： Hu, Xuemin Liu, Yanfang Tang, Bo Yan, Junchi Chen, Long Hubei Univ Sch Artificial Intelligence Wuhan 430062 Hubei Peoples R China Worcester Polytech Inst Dept Elect & Comp Engn Worcester MA 01609 USA Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Waytous Inc Beijing 100083 Peoples R China

Automatic overtaking is a challenging task for self-driving vehicles. Traditional rule-based methods for overtaking in autonomous driving heavily rely on many predefined rules and are difficult to apply in complex driving scenarios. Learning-based methods usually use convolutional networks, recurrent networks, and multilayer perceptrons, etc., to extract features from environments, but they fail to effectively represent geometric and interactive information among traffic participants. Classic graph convolutional networks (GCNs) have the ability of represent graph-structural information but are limited to stable relationship representation due to the fixed adjacency matrix when applied in autonomous driving. In this paper, we propose a novel dynamic graph learning method based on a graph convolutional network with a trainable adjacency matrix (TAM-GCN) to enable the learning of dynamic relationships among different nodes in an ever-changing driving scene. In addition, we develop a planning method for overtaking strategy in autonomous driving, where the proposed TAM-GCN is used to extract the spatial graph-structural features, select appropriate overtaking time, and generate efficient overtaking actions. The proposed model is trained using the imitation learning method. We conduct comprehensive experiments in both closed-loop and open-loop testing in the CARLA simulator and compare our method with state-of-the-art methods. Experimental results demonstrate the proposed method achieves better accuracy, safety and overtaking performance than existing methods.

关键词： Autonomous driving graph convolutional network trainable adjacency matrix overtaking dynamic graph

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Short-term effects of ambient oxidation, and its interaction with fine particles on first-ever stroke: A national case-crossover study in China

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SCIENCE OF THE TOTAL ENVIRONMENT 2024年 907卷 168017页

作者： Jiang, Dongxia Wang, Luyang Han, Xueyan Pan, Zhaoyang Wang, Zhaokun Wang, Yaqi Li, Jing Guo, Jian Liu, Yuanli Huang, Shaodan Guan, Tianjia Peking Univ Sch Publ Hlth Dept Occupat & Environm Hlth Sci Beijing 100191 Peoples R China Beijing Key Lab Indoor Air Qual Evaluat & Control Beijing 100084 Peoples R China Chinese Acad Med Sci & Peking Union Med Coll Sch Hlth Policy & Management Beijing 100730 Peoples R China Peking Univ Inst Child & Adolescent Hlth Sch Publ Hlth Beijing 100191 Peoples R China Chinese Acad Med Sci & Peking Union Med Coll Peking Union Med Coll Hosp Med Res Ctr State Key Lab Complex Severe & Rare Dis Beijing Peoples R China Chinese Acad Med Sci & Peking Union Med Coll Peking Union Med Coll Hosp Dept Cardiol State Key Lab Complex Severe & Rare Dis Beijing Peoples R China Peking Univ Key Lab Epidemiol Major Dis Minist Educ Beijing 100191 Peoples R China Peking Univ Sch Publ Hlth Dept Occupat & Environm Hlth Sci Beijing Peoples R China Peking Univ Key Lab Epidemiol Major Dis Minist Educ Beijing Peoples R China Chinese Acad Med Sci & Peking Union Med Coll Sch Hlth Policy & Management Beijing Peoples R China

Stroke is a significant global cause of disability and death, and its burden has been on the rise, while ambient air pollution has been conclusively linked to stroke incidence. However, knowledge about effects of atmospheric oxidation on stroke and its interactions with fine particles (PM2.5) are still limited. In this study, we investigated the short-term effects of ambient NO2, O-3, and their combined oxidation (O-wt) on first-ever stroke, based on data from the China National Stroke Screening Survey (CNSSS) conducted from 2013 to 2015. We found significant association between ambient NO2 exposure at lag0 day with first-ever stroke, with a 13.1 % (95 % CI: 3.5 %, 23.6 %) increase in the first-ever stroke risk per 10 mu g/m(3 )exposure. We also found a significant interaction between NO2 and PM2.5 (p < 0.05): first-ever stroke risk increased 23.8 % (95 % CI: 9.6 %, 39.8 %) per 10 mu g/m(3) NO2 exposure in population exposed to higher PM(2.5 )concentrations, while no significant association was found in population exposed to lower PM(2.5 )concentrations. The results of stratified analyses indicated that physical inactivity enhanced the detrimental effects of O-3 and O-wt exposure, while smoking and transient ischemic attack (TIA) history enhanced the detrimental effects of NO2 exposure. However, TIA history appeared to mitigate the adverse effects of O-3 exposure. This study is helpful to better understand the impact of ambient oxidation on stroke, as well as its interaction with PM2.5, and has implications for policies and standards for atmospheric protection and governance.

关键词： Nitrogen dioxide Ozone Ambient oxidation Fine particle First -ever stroke

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