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Broadband low-frequency sound absorption of multifunctional composite metastructure

science China(Technological sciences) 2025年第2期68卷 114-124页

作者： Yilong YANG Shanshan LIU Yan LI Tong GAO Yongdong PAN Yong LI Yabin JIN School of Aerospace Engineering and Applied Mechanics Tongji UniversityShanghai 200092China State International Joint Research Center of Aerospace Design and Additive Manufacturing Northwestern Polytechnical UniversityXi’an 710072China Institute of Acoustics School of Physics Science and EngineeringTongji UniversityShanghai 200092China Shanghai Key Laboratory of Intelligent Sensing and Detection Technology School of Mechanical and Power EngineeringEast China University of Science and TechnologyShanghai 200237China Shanghai Institute of Aircraft Mechanics and Control Shanghai 200092China

In this study,we fabricated multifunctional metastructures from carbon fiber-reinforced plastic composites using additive manufacturing *** metastructures are characterized by their lightweight,load-bearing capacity,and broadband low-frequency sound absorption *** metastructure consists of 36 unit cells,and non-local coupling mechanism was considered for designing the sound absorption *** developed an acoustic impedance theory tailored for the metastructure,facilitating an analysis of thermal and viscous dissipation *** is proven theoretically and experimentally that the proposed composite metastructure can achieve a noise reduction with an average sound absorption coefficient greater than 0.9 across frequencies in the rage of 330-1500 *** also studied the metastructure’s quasi-static and cyclic compression performance,confirming its efficient absorption capabilities after cyclic *** proposed design and additive manufacturing method for composite metastructures provides a novel pathway for creating lightweight,multifunctional structures with diverse applications,such as aerospace engineering.

关键词： composite metastructure broadband sound absorption additive manufacturing

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Learning Agile Quadrotor Flight in Restricted Environments with Safety Guarantees

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on intelligent Vehicles 2024年第9期9卷 1-8页

作者： Chen, Shiyu Li, Yanjie Lou, Yunjiang Lin, Ke Wu, Xinyu Department of Control Science and Engineering Harbin Institute of Technology Shenzhen and Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Shenzhen China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

With the increasing requirement for agile and efficient controllers in safety-critical scenarios, controllers that exhibit both agility and safety are attracting attention, especially in the aerial robotics domain. This paper focuses on the safety issue of Reinforcement Learning (RL)-based control for agile quadrotor flight in restricted environments. To this end, we propose a unified Adaptive Safety Predictive Corrector (ASPC) to certify each output action of the RL-based controller in real-time, ensuring its safety while maintaining agility. Specifically, we develop the ASPC as a finite-horizon optimal control problem, formulated by a variant of Model Predictive control (MPC). Given the safety constraints determined by the restricted environment, the objective of minimizing loss of agility can be optimized by reducing the difference between the actions of RL and ASPC. As the safety constraints are decoupled from the RL-based control policy, the ASPC is plug-and-play and can be incorporated into any potentially unsafe controllers. Furthermore, an online adaptive regulator is presented to adjust the safety bounds of the state constraints with respect to the environment changes, extending the proposed ASPC to different restricted environments. Finally, simulations and real-world experiments are demonstrated in various restricted environments to validate the effectiveness of the proposed ASPC. IEEE

关键词： Reinforcement learning

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Accurate 3D geometry measurement for non-cooperative spacecraft with an unfocused light-field camera

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Journal of Systems Engineering and Electronics 2022年第1期33卷 11-21页

作者： XU Shengming LU Shan HOU Yueyang SHI Shengxian School of Mechanical Engineering Shanghai Jiao Tong UniversityShanghai 200240China Shanghai Key Laboratory of Space Intelligent Control Technology Shanghai Aerospace Control Technology InstituteShanghai 201109China

This work explores an alternative 3D geometry measurement method for non-cooperative spacecraft guiding navigation and proximity *** one snapshot of an unfocused light-field camera, the 3D point cloud of a non-cooperative spacecraft can be calculated from sub-aperture images with the epipolar plane image(EPI) based light-field rendering algorithm.A Chang'e-3 model(7.2 cm×5.6 cm×7.0 cm) is tested to validate the proposed *** measurement distances(1.0 m, 1.2 m, 1.5 m) are considered to simulate different approaching *** errors are quantified by comparing the light-field camera data with a high precision commercial laser *** mean error distance for the three cases are 0.837 mm, 0.743 mm, and 0.973 mm respectively, indicating that the method can well reconstruct 3D geometry of a non-cooperative spacecraft with a densely distributed 3D point cloud and is thus promising in space-related missions.

关键词： 3D geometry measurement non-cooperative spacecraft unfocused light-field camera

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Aggressive and robust low-level control and trajectory tracking for quadrotors with deep reinforcement learning

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Neural Computing and Applications 2025年第3期37卷 1223-1240页

作者： Chen, Shiyu Li, Yanjie Lou, Yunjiang Lin, Ke Department of Control Science and Engineering Harbin Institute of Technology Shenzhen Shenzhen518055 China Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Shenzhen518055 China

Executing accurate trajectory tracking tasks using a high-performance low-level controller is crucial for quadrotors to be applied in various scenarios, especially those involving uncertain disturbances. However, due to the uncertainties in disturbed environments, developing effective low-level controllers with traditional model-based control schemes is challenging. This paper presents an aggressive and robust reinforcement learning (RL)-based low-level control policy for quadrotors. The policy maps the observed quadrotor state directly to motor thrust commands, without requiring the quadrotor dynamics. Additionally, a trajectory generation pipeline is developed to improve the accuracy of trajectory tracking tasks based on differential flatness. With the learned low-level control policy, extensive simulations and real-world experiments are implemented to validate the performance of the policy. The results indicate that our RL-based low-level control policy outperforms traditional proportional–integral–derivative (PID) control methods and related learning-based policies in terms of accuracy and robustness, particularly in environments with uncertain disturbances. Furthermore, the proposed RL-based control policy exhibits an aggressive response in trajectory tracking, even when the speed of the desired trajectory is increased to 6 m/s. Moreover, the learned policy demonstrates strong vibration suppression capabilities and enables the quadrotor to recover to a hovering state from random initial conditions with shorter response time. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Deep reinforcement learning

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Trust-Based Identity Privacy for Edge Computing 24

Trust-Based Identity Privacy for Edge Computing

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14th International Conference on Communication and Network Security, ICCNS 2024

作者： Dong, Jie Wen Kang, Xi Nai Peng, Jin Jiang, Xuan Ming Zhu, Tao Jun Information Technology Department National Key Laboratory of Seience and Technology on Aerospace Intelligence Control Beijing Aerospace Automatic Control Institute BeiJing China School of Computer Science Beijing University of Technology BeiJing China

ISBN: (纸本)9798400717994

College of Computer science Beijing University of technology, Beijing 100124, China, 1374622525@*** This paper proposes a trust collaboration technology for edge computing, addressing trust isolation and security issues through a unified trust value calculation method and blockchain technology. The designed trust management model evaluates the identity, capability, and behavior of terminal nodes using static and dynamic trust measurements. A consortium blockchain stores trust information to ensure immutability and efficient sharing. Simulation experiments validate the scheme's effectiveness in countering malicious node attacks, improving task completion rates, and reducing system risks. This solution offers an efficient and secure approach to trust management in edge computing environments. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Differential privacy

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Trust Collaboration for Edge Computing 24

Trust Collaboration for Edge Computing

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14th International Conference on Communication and Network Security, ICCNS 2024

ISBN: (纸本)9798400717994

As edge computing becomes an increasingly important computing model, trust management and security issues are becoming more severe. Problems such as malicious node attacks and trust isolation pose threats to the security and stability of edge computing environments. Therefore, designing an efficient and secure trust management mechanism is imperative. This paper proposes a trust collaboration technology for edge computing, addressing trust isolation and security issues through a unified trust value calculation method and blockchain technology. The designed trust management model evaluates the identity, capability, and behavior of terminal nodes using static and dynamic trust measurements. A consortium blockchain stores trust information to ensure immutability and efficient sharing. Simulation experiments validate the scheme's effectiveness in countering malicious node attacks, improving task completion rates, and reducing system risks. This solution offers an efficient and secure approach to trust management in edge computing environments. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Mobile edge computing

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Status and Application of Artificial Intelligence-Based Cooperative Positioning and Navigation technology for UAV Clusters 1

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3rd International Conference on Autonomous Unmanned Systems, ICAUS 2023

作者： Sheng, Danting Cheng, Jin Li, Fengyi Hao, Mingrui Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China Intelligent Science and Technology Academy Limited of CASIC Beijing100041 China

ISBN: (数字)9789819710911

ISBN: (纸本)9789819710904

Aiming at the UAV cluster cooperative navigation, this paper tries to summarize the development experience and existing results on two issues, namely, the application of filtering and artificial intelligence in UAV cluster cooperative navigation. The application of filtering in UAV cluster cooperative navigation briefly describes the application of Kalman filtering and extended Kalman filtering, traceless Kalman filtering and other nonlinear filters, intelligent optimization algorithms combined with particle filtering in cooperative navigation. Artificial Intelligence Methods in Cooperative Navigation of UAV Clusters describes the application of deep feed-forward networks, brain-like information fusion methods based on integrated learning, multivariate interpolation of radial-based neural networks and Kalman filtering combined in the field of cooperative navigation. The results of existing applications of filtering and artificial intelligence in UAV cluster cooperative navigation are analyzed and summarized, and the future development of the field is outlooked. © Beijing HIWING Scientific and Technological Information Institute 2024.

关键词： Kalman filters

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A Loss Management Method for the Spaceborne Multi-control Processing 7th

A Loss Management Method for the Spaceborne Multi-control Pr...

引用

7th International Conference on Computing, control and Industrial Engineering, CCIE 2023

作者： Zhang, Ying Tian, Feng Jiang, Cunsheng Wang, Shihui Tao, Yizhu Beijing Aerospace Automatic Control Institute Beijing100854 China National Key Laboratory of Science and Technology on Aerospace Intelligent Control Beijing100854 China Yuying School Beijing100018 China

ISBN: (纸本)9789819927296

The multi-control system for spaceborne task processing uses software to complete the power management and error correction of the configuration program to ensure the spaceborne task with high reliable operation capability. The system in this paper adopts software to complete the correction of the configuration program and only wakes up the control strategy of multi-redundant fault-tolerant programming before writing to the FPGA, reducing power consumption while ensuring high reliability of the program. The specific address that exceeds the error correction ability is written to the telemetry, and the ground will update the error block by means of upshooting. In the process of testing, observing the loading and power consumption control of the multiprocessor for the spaceborne task, it has been verified that the system can be realized in the power consumption. The operation for the control processing is represented by the typical controller. The multi-control power mode management for spaceborne task processing realizes the high-reliability on-orbit multi-control, that has the low-cost circuits. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Error correction

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Adaptive Online Optimization control Method Based on Reinforcement Learning

Adaptive Online Optimization Control Method Based on Reinfor...

引用

2023 China Automation Congress, CAC 2023

作者： Wang, Zhaolei Yu, Chunmei Lu, Kunfeng Hu, Ruiguang Huang, Xu Wang, Ludi National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing Aerospace Automatic Control Institute Beijing China

ISBN: (纸本)9798350303759

Large envelope deformable aircraft pose higher requirements for guidance and control technology, requiring control law parameters to be able to adaptively optimize online based on flight uncertainty and dynamically enhance flight control capabilities. At present, coefficient freezing is commonly used in engineering to design typical feature work points, but in large envelope flight, there are more and more feature work points, and traditional design methods are more complex and highly dependent on manual experience;On the other hand, traditional gain scheduling methods can only switch preset control laws based on flight status. Once the control laws are deployed, their performance is solidified, and their adaptive ability is relatively limited, making it impossible to perform online optimization. This article adopts deep reinforcement learning to design a universal PI state feedback control law parameter self-learning architecture, achieving autonomous learning of control law parameters at different working points;At the same time, the trained parameter adjustment network is deployed together with the control law, and online learning optimization of control law parameters is achieved during flight based on the control effect, enhancing adaptive ability. Finally, the effectiveness of the proposed method was verified using a specific pitch angle tracking control scenario as an example. © 2023 IEEE.

关键词： Reinforcement learning

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Improving the Reconstruction Efficiency of Dice- Stagewise Weak Orthogonal Matching Pursuit 3

Improving the Reconstruction Efficiency of Dice- Stagewise W...

引用

3rd IEEE International Conference on Computer Communication and Artificial Intelligence, CCAI 2023

作者： He, Shaoqi Xiao, Liangang Gao, Shibo Zhu, Yichuan National KeyLaboratory and Technology on Aerospace Intelligence Control Beijing Aerospace Automatic Control Institute Beijing China Committee of Science and Technologyational Key Laboratory of Science and Technology on Aerospace Beijing Aerospace Automatic Control Institute Beijing China

ISBN: (纸本)9798350335262

weak orthogonal matching pursue algorithm cannot obtain high-precision reconstructed signals in the measurement process. Thus, this study proposes an improved SWOMP algorithm called DHP-SWOMP, which is based on partial Hadamard matrix, to overcome the aforementioned shortcoming. First, Dice coefficient matching is introduced to effectively distinguish the atomic correlation and ensure the selection of the best atom for overcoming the similar atom selection in traditional SWOMP algorithm. Then, the sampling partial Hadamard matrix is proposed as the measurement matrix to overcome the issue of failing to obtain high-precision reconstructed signals when Gaussian matrix is used in SWOMP algorithm. The random independence of the matrix is used to improve the reconstruction accuracy of the algorithm. Simulation results show that the proposed algorithm improves the signal-to-noise ratio by 53.97%, shortens the reconstruction time by 87.60%, reduces the mean square error by 15.46%, and have smaller recovery residual and higher signal reconstruction rate than SWOMP algorithm based on Gaussian matrix. © 2023 IEEE.

关键词： Compressed sensing

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