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High-order Fully Actuated System Approach based Attitude Stabilization for Underactuated Rigid and Flexible Spacecraft

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IEEE Transactions on Automation Science and Engineering 2025年 22卷 15094-15105页

作者： Jia, Shixiang Qiu, Jianbin Wang, Tong Harbin Institute of Technology State Key Laboratory of Robotics and Systems Harbin 150001 China Harbin Institute of Technology Research Institute of Intelligent Control and Systems Harbin 150080 China

This paper addresses the challenge of attitude stabilization for a class of underactuated rigid and flexible spacecrafts by utilizing only two control inputs. The attitude stabilization problem of underactuated spacecraft poses substantial challenges to the High-Order Fully Actuated (HOFA) system approach, due to the fact that it heavily depends on the full actuation characteristics of the system. To circumvent the aforementioned limitation, we first derive a HOFA system for underactuated rigid spacecraft using homogeneity theory and averaged system approach. Subsequently, we design a controller based on the HOFA system approach, which yields a linear closed-loop system. This methodology is then extended to address the simplified attitude stabilization problem of underactuated flexible spacecraft. By utilizing the HOFA system approach in tandem with an observer for estimating modal variables, we propose a control strategy that not only stabilizes the attitude but also actively suppresses elastic vibration. The performance of the proposed control schemes are demonstrated through simulation studies. © 2004-2012 IEEE.

关键词： Attitude stabilization Flexible spacecraft High-order fully actuated system approach Underactuated spacecraft

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Spatial-Temporal Zero-Inflated Negative Binomial Graph Neural Network-Powered Multi-Port Vessel Traffic Flow Prediction

Spatial-Temporal Zero-Inflated Negative Binomial Graph Neura...

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International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Cao, Jining Zhu, Man Wen, Yuanqiao Liu, Yihao Zheng, Xinyi Sanya Science and Education Innovation Park of Wuhan University of Technology Wuhan University of Technology Sanya572000 China State Key Laboratory of Maritime Technology and Safety Wuhan University of Technology Wuhan430070 China Intelligent Transportation Systems Research Center Wuhan University of Technology Wuhan430070 China

ISBN: (纸本)9789819639564

High-precision prediction of vessel traffic flow is crucial for managing traffic during adverse weather conditions and enhancing navigation efficiency. Aiming to eliminate the deficiencies of traditional prediction methods and improve prediction precision and adaptability, this paper proposed a vessel traffic flow prediction method based on an origin-destination (O-D) matrix and a Spatio-Temporal Zero-Inflated Negative Binomial Graph Neural Network (STZINB-GNN) for port cluster navigational networks. To understand the internal coupling relationships within the traffic network from a macro perspective, a port cluster navigational network was constructed based on vessel origin-destination (O-D) data. Traditional prediction methods often overlook the Euclidean spatial characteristics between network nodes and face challenges due to the sparsity of vessel traffic flow. To address these issues, a spatial-temporal graph neural network prediction method incorporating a sparsity parameter π was proposed. A case study with the Qiongzhou Strait port group demonstrated that the STZINB-GNN achieved the highest prediction accuracy across three temporal resolutions compared to four established methods: STNB-GNN, STG-GNN, STGCN, and HA. Specifically, the STZINB-GNN attained superior accuracy when the temporal resolution was extended to one day. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Graph neural networks

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Efficient Trajectory Planning in Polar Ice Regions: Enhancing Ship Maneuverability and Path Optimization

Efficient Trajectory Planning in Polar Ice Regions: Enhancin...

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International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Sun, Youguo Wu, Da Zhang, Jinfen Han, Zhepeng State Key Laboratory of Maritime Technology and Safety Wuhan University of Technology Wuhan430063 China National Engineering Research Center for Water Transport Safety Wuhan University of Technology Wuhan430063 China Intelligent Transportation Systems Research Center Wuhan University of Technology Wuhan430063 China

ISBN: (纸本)9789819639601

As global temperatures rise and Arctic sea ice rapidly melts, the development of Arctic shipping has accelerated, posing significant challenges due to the complex polar navigation environment. This paper presents a trajectory planning method that enables rapid planning while ensuring compliance with ship maneuverability requirements. A sea ice risk model based on POLARIS is constructed, followed by the creation of a three-dimensional navigation risk grid map, which is subsequently sliced into two-dimensional maps. The JPS algorithm is employed to identify paths, and key nodes are filtered and pruned. These nodes are then consolidated into a single plane, with duplicates removed, and a roadmap is constructed. The Dijkstra algorithm is used to find the shortest path, which is further optimized by applying constraints on ship maneuverability and navigation corridors using an optimal control model. Experimental results demonstrate that the proposed method generates a path with risks comparable to those produced by the 3D-A* and 3D-JPS algorithms, but is 85 km and 22 km shorter, respectively, with only two turns. The planning time is significantly reduced, and the optimized trajectory exhibits minimal angular acceleration variation. This method effectively plans a safe, short, and maneuverable route for ships navigating polar ice regions. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Sea ice

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Preface to Special Issue on Optoelectronic Neuromorphic Devices

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Journal of Semiconductors 2025年第2期46卷 2-4页

作者： Zhenyi Ni Zhongqiang Wang Jia Huang Xiaodong Pi State key Laboratory of Silicon and Advanced Semiconductor Materials&School of Materials Science and Engineering Zhejiang UniversityHangzhou 310027China Shangyu Institute of Semiconductor Materials Shaoxing 312300China Key Laboratory of UV-Emitting Materials and Technology of Ministry of Education Northeast Normal UniversityChangchun 130024China School of Materials Science and Engineering&National Key Laboratory of Autonomous Intelligent Unmanned Systems Tongji UniversityShanghai 201804China Institute of Advanced Semiconductors&Zhejiang Provincial Key Laboratory of Power Semiconductor Materials and Devices ZJ-UHangzhou Global Scientific and Technological Innovation CenterZhejiang UniversityHangzhou 311200China

The rapid advancement of artificial intelligence(AI),recognized with Nobel Prizes in both Physics and Chemistry in 2024,has been revolutionizing countless aspects of modern life,driving innovations across diverse fields and reshaping how people interact with *** its great advances,the computing of AI systems is now approaching the limit of traditional computing based on the von Neumann architecture,which is characterized by separated memory and process units-a challenge known as the"von Neumann bottleneck".

关键词： approaching separated Neumann

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Influence Enhanced Sparse Coordination Graphs for Multi-Agent Reinforcement Learning

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Neural Networks 2025年 188卷 107454页

作者： Zhang, Xiwen Chen, Jie Gan, Ming-Gang Chen, Haoxiang State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

In contemporary Multi-Agent Reinforcement Learning (MARL), effectively enhancing the expressive capacity of value functions has been a persistent research focus. Many studies have employed value decomposition methods;however, due to the neglect of inter-agent collaboration, these methods fall short of achieving optimal performance. Subsequent research introduced coordination graphs into value decomposition methods;nevertheless, these approaches often rely on simplistic rules to evaluate inter-agent collaboration and fail to adequately describe the collaborative relationships of agents in complex environments. Consequently, we propose Influence Enhanced Sparse Coordination Graphs (IESCG) as a solution to provide insights into the aforementioned problem. In this study, we propose influence networks as quantitative descriptions of the importance of collaboration among agents, utilizing them as crucial basis for constructing the topology of Sparse Time-Varying Coordination Graphs. Additionally, we propose Recurrent Payoff Function Networks (RPFN) to incorporate temporal information while providing necessary input to influence networks. Furthermore, Sparse Graph Advantage Selection Coefficients (SGASC) are introduced to stabilize the overall value function across different time steps, ensuring training stability. Experimental investigations conducted on the StarCraft II micromanagement and MACO benchmark indicate that our algorithm not only accelerates convergence and improves winning probabilities but also exhibits more pronounced advantages in complex scenarios. © 2025

关键词： Markov processes

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Techno-economic analysis of fuel cell trucks with different powertrain hybridization and hydrogen resources by 2040: case study of China

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International Journal of Hydrogen Energy 2025年 132卷 174-182页

作者： Nan, Long Xu, Feiyang Sui, Pang-Chieh Feng, Yanbiao Hao, Han Li, Shunxi Huang, Feng Hubei Key Laboratory of Advanced Technology for Automotive Components Hubei Collaborative Innovation Center for Automotive Components Technology and Hubei Research Center for New Energy & Intelligent Connected Vehicle School of Automotive Engineering Wuhan University of Technology Hubei 430070 China Institute for Integrated Energy Systems University of Victoria VictoriaBCV8W 2Y2 Canada School of Mechanical Engineering University of Science & Technology Beijing Beijing100083 China State Key Laboratory of Automotive Safety and Energy Tsinghua University Beijing100084 China

This study conducts a techno-economic analysis of fuel cell electric trucks with different hybridization factors (i.e., the power distribution ratio between batteries and fuel cells) by evaluating their life-cycle carbon dioxide (CO2) emissions and total cost of ownership (TCO) from 2020 to 2040. It integrates the energy consumption of trucks with varying hybridization factors under different driving cycles with China's hydrogen production, transportation models, and electric grid structure. Through this analysis, the TCO and emissions of individual trucks are examined across different scenarios. The study reveals that highly hybridized trucks offer advantages in both emissions reduction and cost savings across all hydrogen production and transport pathways. A 67% hybridization factor results in a 5%-9% reduction in emissions and a 34%-46% decrease in TCO compared to the lowest hybridization factor. The energy-saving and emission-reducing effects of highly hybridized fuel cell electric vehicles (FCEVs) also depend on the driving cycle, with optimal performance observed in the Urban Dynamometer Driving Schedule for Heavy-Duty Vehicles (UDDSHDV) cycle, while minimal advantages appear in the China Heavy-Duty Commercial Vehicle Test Cycle for Heavy Trucks (CHTC-HT) cycle. Furthermore, life-cycle emissions of trucks decrease gradually over time for all hydrogen production pathways, with the electrolysis of grid electricity showing the fastest decline. By 2040, trucks powered by hydrogen from grid electricity will have slightly lower emissions than those using coal, while green hydrogen remains the cleanest option. In conclusion, adopting fuel cell trucks with high hybridization factors, powered by green hydrogen, presents significant economic and environmental benefits for the future of transportation. © 2025 Hydrogen Energy Publications LLC

关键词： Commercial vehicles

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A Stability-guaranteed Model Predictive Control for Autonomous Surface Vessel Trajectory Tracking based on Lyapunov Theory

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IEEE Transactions on Transportation Electrification 2025年

作者： Chen, Linying Qiu, Qianqian Li, Mengxia Chen, Pengfei Huang, Yaming Mou, Junmin Wuhan University of Technology State Key Laboratory of Maritime Technology and Safety School of Navigation Wuhan430063 China Wuhan University of Technology State Key Laboratory of Maritime Technology and Safety Intelligent Transportation Systems Research Center Wuhan430063 China

This article explores the trajectory tracking problem of an Autonomous Surface Vessel (ASV). As an optimization-based time-domain method, Model Predictive Control (MPC) has incomparable advantages in solving constrained multi-input multi-output system problems. However, ensuring the stability of the MPC is challenging. This article proposes a Stability-Guaranteed Model Predictive Control (SMPC) method by adding the Lyapunov constraint into the linearized MPC framework. The Lyapunov constraint transformed from the backstepping control law restricts the first action of MPC to guarantee closed-loop stability. Furthermore, the stability and recursive feasibility conditions are theoretically derived. Simulation results demonstrate that the proposed SMPC has better tracking and robustness compared with the backstepping methods. © 2025 IEEE.

关键词： Predictive control systems

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Adaptive Artificial Potential Field Ship Motion Planning Based on DDPG

Adaptive Artificial Potential Field Ship Motion Planning Bas...

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International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Chen, Huimin Liu, Chenguang He, Zhibo Zhang, Guoqing Han, Bing State Key Laboratory of Maritime Technology and Safety Wuhan University of Technology Wuhan430063 China Intelligent Transportation Systems Research Center Wuhan University of Technology Wuhan430063 China School of Transportation and Logistics Engineering Wuhan University of Technology Wuhan430063 China Navigation College Dalian Maritime University Dalian116026 China State Key Laboratory of Maritime Technology and Safety Dalian116026 China National Engineering Research Center of Ship & Shipping Control System Shanghai200134 China

ISBN: (纸本)9789819639601

The paper presents a ship motion planning method that adjusts the attractive and repulsive gain parameters of the artificial potential field (APF) algorithm using the deep deterministic policy gradient (DDPG) algorithm, to address the challenges of parameter design and limited adaptability of traditional APF algorithms. This approach leverages the speed advantage of the APF planning algorithm and the exploration capability of the DDPG reinforcement learning algorithm. By optimizing the parameters of APF with DDPG, the local optima problem can be alleviated in complex obstacle environments. Simulation results demonstrate that the proposed motion planning algorithm can generate reasonable trajectories and exhibit strong adaptability in complex obstacle scenarios. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Motion planning

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Conditional Disturbance Compensation-Based Fault-Tolerant Group Consensus Control for MASs: An Event-Based Switching Method

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IEEE Transactions on Automation Science and Engineering 2025年 22卷 13828-13841页

作者： Liu, Pei-Ming Guo, Xiang-Gui Wang, Jian-Liang Che, Wei-Wei Wu, Zheng-Guang University of Science and Technology Beijing Beijing Engineering Research Center of Industrial Spectrum Imaging School of Automation and Electrical Engineering Beijing100083 China University of Science and Technology Beijing Shunde Innovation School Foshan528000 China Beihang University Hangzhou Innovation Institute Autonomous Intelligent Systems Department Hangzhou310051 China Northeastern University College of Information Science and Engineering Shenyang266071 China Zhejiang University State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Hangzhou310027 China

With respect to the group consensus control for multi-agent systems (MASs) with disturbances and actuator nonidentical and unknown direction faults (NUDFs), this paper proposes a novel fault-tolerant control scheme by incorporating an event-based switching function and a saturation function. The event-based switching function is implemented to directly switch to the desired working mode, thereby reducing invalid switching after a reverse fault. Concurrently, the saturation function, acting at the software level, can not only prevent the instantaneous impact of excessive reverse input caused by a reverse fault, but also eliminate input peaking and state chattering after switching the working mode. It is worth mentioning that the proposed switching-based fault-tolerant control method can avoid the control chocks and excessive control gains as in the existing methods by using Nussbaum functions and thus improve the transient performance. Additionally, for disturbances, the designed conditional disturbance compensation mechanism ensures a smaller steady-state error, and less energy consumption by utilizing beneficial disturbances and compensating for harmful ones. Besides, the conditional compensation mechanism also reduces the saturation error. Finally, the effectiveness of the proposed control scheme are verified through a simulation example of uncrewed aerial vehicles (UAVs). © 2004-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Distributed event-triggered Nash equilibrium seeking for aggregative game with second-order dynamics

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IEEE/CAA Journal of Automatica Sinica 2025年

作者： Huang, Yi Sun, Jian Fei, Qing State Key Laboratory of Autonomous Intelligent Unmanned Systems School of Automation Beijing Institute of Technology Beijing100081 China

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