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Topologically assisted optimization for rotor design

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arXiv 2023年

作者： Wang, Tianyu Yang, Yannian Chen, Xuanwu Li, Pengyu Iollo, Angelo Maceda, Guy Cornejo Y. Noack, Bernd R. School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen518055 China Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong School of Automation Science and Engineering South China University of Technology Guangzhou510640 China Institute of Aero Engine Tsinghua University Beijing10084 China Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Shenzhen518055 China Institut de Mathématiques de Bordeaux Université de Bordeaux and Memphis Team Centre Inria de l’Université de Bordeaux Talence33400 France

We develop and apply a novel shape optimization exemplified for a two-blade rotor with respect to the figure of merit (FM). This topologically assisted optimization (TAO) contains two steps. First a global evolutionary optimization is performed for the shape parameters and then a topological analysis reveals the local and global extrema of the objective function directly from the data. This non-dimensional objective function compares the achieved thrust with the required torque. Rotor blades have a decisive contribution to the performance of quadcopters. A two-blade rotor with pre-defined chord length distribution is chosen as the baseline model. The simulation is performed in a moving reference frame with a k−ω turbulence model for the hovering condition. The rotor shape is parameterized by the twist angle distribution. The optimization of this distribution employs a genetic algorithm. The local maxima are distilled from the data using a novel topological analysis inspired by discrete scalar-field topology. We identify one global maximum to be located in the interior of the data and five further local maxima related to errors from non-converged simulations. The interior location of the global optimum suggests that small improvements can be gained from further optimization. The local maxima have a small persistence, i.e., disappear under a small Ε perturbation of the figure of merit values. In other words, the data may be approximated by a smooth mono-modal surrogate model. Thus, the topological data analysis provides valuable insights for optimization and surrogate modeling. Copyright © 2023, The Authors. All rights reserved.

关键词： Turbulence models

STGCNFormer: Spatio-Temporal Dual-Stream Graph Convolutional networks and Transformers for Traffic Forecasting

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IEEE Transactions on Vehicular Technology 2025年

作者： Yang, Changzhi Pan, Huihui Wang, Jue Harbin Institute of Technology Research Institute of Intelligent Control and Systems Harbin150001 China Tongji University National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai201210 China Ningbo Institute of Intelligent Equipment Technology Company Ltd. Ningbo315200 China University of Science and Technology of China Department of Automation Hefei230027 China

In intelligent transportation systems, traffic prediction constitutes the fundamental component of traffic optimization, aiming to precisely forecast traffic flow. Modeling spatio-temporal interactions is a crucial step in achieving long-term and accurate predictions. Traditional methods are mostly based on single-stream networks, which alternately extract spatio-temporal features through sequential iterations. However, time and space are inherently two different modalities. Employing a single-stream network to address multi-modal tasks is suboptimal. In this paper, we propose Spatio-Temporal Dual-Stream Graph Convolutional networks and Transformers (STGCNFormer), a dual-stream network for traffic forecasting, decoupling spatio-temporal features in parallel through a temporal stream and a spatial stream. The core of STGCNFormer is a STGCNFormer block, comprising three components. The first component is a GCN block, which restricts the kernel of graph convolution and adopts Chebyshev polynomials of the first kind to approximate the convolutional kernel, capturing spatial interactions and achieving localized convolution. Secondly, we present a Transformer block, designed as a simplified version of vanilla Transformers, which captures temporal interactions and significantly reduces computational complexity. Thirdly, we propose a cross attention mechanism, consisting of a temporal attention mechanism and a spatial attention mechanism, which serves as a bidirectional bridge for sharing information between the dual streams. Experimental results demonstrate that STGCNFormer achieves superior prediction accuracy with reduced computational complexity compared with previous state-of-the-art methods. © 1967-2012 IEEE.

关键词： Air traffic control

The Solution of Drone Attitudes on Lie Groups

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The Solution of Drone Attitudes on Lie Groups

International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Shaoji Yang Hailong Pei Ministry of Education Key Laboratory of Autonomous Systems and Networked Control Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong South China University of Technology Guangzhou China

ISBN: (数字)9781728164793

ISBN: (纸本)9781728164809

In this paper, we generalize the Extended Kalman Filter algorithm on Lie Groups for the attitude propagation and update of the unmanned helicopters. The data noises obtained by sensors are assumed to be independent as Gaussian distributions, and the posterior distribution of the estimated state is also Gaussian distribution. The attitude of the drone can be obtained from the accelerometers, the gyroscopes, and the magnetometers. These three sensors need to be fused to obtain the optimal estimated value. Among them, the method of multiple sub-samples is used to compensate for the error generated by the long sampling period of the gyroscope, and we use a Chi-square detection method to determine whether the observation information is valid during the updating process.

关键词： Accelerometers Algebra Kalman filters Sensors Gaussian distribution Gyroscopes Magnetometers

Ranging-aided Aerobridge Navigation Using Dual Quaternion Based Multiplicative Extended Kalman Filter

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Ranging-aided Aerobridge Navigation Using Dual Quaternion Ba...

International Symposium on autonomous systems (ISAS)

作者： Ruican Xia Hailong Pei Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong South China University of Technology Guangzhou China

ISBN: (数字)9781665418829

ISBN: (纸本)9781665447997

The autopilot of aerobridge has attracted considerable attention recently since human error and high labor costs. To estimate the pose of an aerobridge, this paper proposes a navigation algorithm with the aid of pairwise distances and inertial measurements. We first locate the joints of the aerobridge with the distance measurement. Then the pose of the aerobridge is calculated using its kinematic in closed-form and the associated covariance is derived. A dual quaternion based multiplicative extended Kalman filter is designed to fuse inertial measurements with the estimated pose, which produces a smooth pose. To verify the precision of this algorithm, a simulation is constructed based on Robotics Toolbox and the experiment results are positive.

关键词： Navigation Fuses Robot kinematics Quaternions Pose estimation Kinematics Kalman filters

Discrete Communication and control Updating in Event-Triggered Consensus

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arXiv 2022年

作者： Cheng, Bin Lv, Yuezu Li, Zhongkui Duan, Zhisheng Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai200120 China Advanced Research Institute of Multidisciplinary Sciences Beijing Institute of Technology Beijing100081 China Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing314000 China State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Peking University Beijing100871 China

This paper studies the consensus control problem faced with three essential demands, namely, discrete control updating for each agent, discrete-time communications among neighboring agents, and the fully distributed fashion of the controller implementation without requiring any global information of the whole network topology. Noting that the existing related results only meeting one or two demands at most are essentially not applicable, in this paper we establish a novel framework to solve the problem of fully distributed consensus with discrete communication and control. The first key point in this framework is the design of controllers that are only updated at discrete event instants and do not depend on global information by introducing time-varying gains inspired by the adaptive control technique. Another key point is the invention of novel dynamic triggering functions that are independent of relative information among neighboring agents. Under the established framework, we propose fully distributed state-feedback event-triggered protocols for undirected graphs and also further study the more complexed cases of output-feedback control and directed graphs. Finally, numerical examples are provided to verify the effectiveness of the proposed event-triggered protocols. Copyright © 2022, The Authors. All rights reserved.

关键词： Directed graphs

A Learning Convolutional Neural network Approach for network Robustness Prediction

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arXiv 2022年

作者： Lou, Yang Wu, Ruizi Li, Junli Wang, Lin Li, Xiang Chen, Guanrong The Department of Computing and Decision Sciences Lingnan University Hong Kong The Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The College of Computer Science Sichuan Normal University Chengdu610066 China The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The Department of Control Science and Engineering Tongji University Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

network robustness is critical for various societal and industrial networks again malicious attacks. In particular, connectivity robustness and controllability robustness reflect how well a networked system can maintain its connectedness and controllability against destructive attacks, which can be quantified by a sequence of values that record the remaining connectivity and controllability of the network after a sequence of node- or edge-removal attacks. Traditionally, robustness is determined by attack simulations, which are computationally very time-consuming or even practically infeasible. In this paper, an improved method for network robustness prediction is developed based on learning feature representation using convolutional neural network (LFR-CNN). In this scheme, higher-dimensional network data are compressed to lower-dimensional representations, and then passed to a CNN to perform robustness prediction. Extensive experimental studies on both synthetic and real-world networks, both directed and undirected, demonstrate that 1) the proposed LFR-CNN performs better than other two state-of-the-art prediction methods, with significantly lower prediction errors;2) LFR-CNN is insensitive to the variation of the network size, which significantly extends its applicability;3) although LFR-CNN needs more time to perform feature learning, it can achieve accurate prediction faster than attack simulations;4) LFR-CNN not only can accurately predict network robustness, but also provides a good indicator for connectivity robustness, better than the classical spectral measures. © 2022, CC BY.

关键词： Complex networks

Correction to “A Magnetic Capsule Robot With an Exoskeleton to Withstand Esophageal Pressure and Delivery Drug in Stomach”

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IEEE Robotics and Automation Letters 2025年第4期10卷 4004-4004页

作者： Ruomao Liu Yujun Chen Zhen Yin Jiachen Zhang Department of Biomedical Engineering City University of Hong Kong Hong Kong SAR China Department of Control Science and Engineering Tongji University Shanghai China National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai China

The name of the third author in [1] is incorrect. The correct author is shown in the red text highlighted.

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Opportunistic Wiretapping/Jamming: A New Attack Model in Millimeter-Wave Wireless networks

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TechRxiv

TechRxiv 2022年

作者： Zhang, Yuanyu Zheng, Zhumeng He, Ji Zhao, Shuangrui Qu, Qianyue Shen, Yulong Jiang, Xiaohong School of Computer Science and Technology Xidian University Shaanxi Xi’an China Shannxi Key Laboratory of Network and System Security China The Beijing Sunwise Information Technology Ltd Beijing Institute of Control Engineering Beijing China The Division of Information Science Graduate School of Science and Technology Nara Institute of Science and Technology Nara Ikoma Japan School of Systems Information Science Future University Hakodate Hokkaido Hakodate Japan

While the millimeter-wave (mmWave) communication is robust against the conventional wiretapping attack due to its short transmission range and directivity, this paper proposes a new opportunistic wiretapping and jamming (OWJ) attack model in mmWave wireless networks. With OWJ, an eavesdropper can opportunistically conduct wiretapping or jamming to initiate a more hazardous attack based on the instantaneous costs of wiretapping and jamming. We also provide three realizations of the OWJ attack, which are mainly determined by the cost models relevant to distance, path loss and received power, respectively. To understand the impact of the new attack on mmWave network security, we first develop novel approximation techniques to characterize the irregular distributions of wiretappers, jammers and interferers under three OWJ realizations. With the help of the results of node distributions, we then derive analytical expressions for the secrecy transmission capacity to depict the network security performance under OWJ. Finally, we provide extensive numerical results to illustrate the effect of OWJ and to demonstrate that the new attack can more significantly degrade the network security performance than the pure wiretapping or jamming attack. © 2022, CC BY.

关键词： Millimeter waves

Opportunistic Wiretapping/Jamming: A New Attack Model in Millimeter-Wave Wireless networks

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arXiv 2022年

作者： Zhang, Yuanyu Zheng, Zhumeng He, Ji Zhao, Shuangrui Qu, Qianyue Shen, Yulong Jiang, Xiaohong School of Computer Science and Technology Xidian University Shaanxi Xi’an China Shannxi Key Laboratory of Network and System Security China Beijing Sunwise Information Technology Ltd Beijing Institute of Control Engineering Beijing China Division of Information Science Graduate School of Science and Technology Nara Institute of Science and Technology Nara Ikoma Japan School of Systems Information Science Future University Hakodate Hokkaido Hakodate Japan

关键词： Millimeter waves