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Formulation of Optimization problems with Radiation Field Phases as Design Variables for Pattern Synthesis of Linear Antenna Arrays

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China Communications 2022年第12期19卷 86-100页

作者： Fei Zhao Jun He Sanyou Zeng Changhe Li Qinghui Xu Zhigao Zeng School of Mechanical Engineering and Electronic Information China University of GeosciencesWuhan 430074China Science and Technology on Blind Signal Processing Laboratory Southwest Electronics and Telecommunication Technology Research InstituteChengdu 610041China School of Automation China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074China College of Computer Hunan University of TechnologyZhuzhou CityHunan Province412007China

Pattern synthesise of antenna arrays is usually complicated optimization problems,while evolutionary algorithms(EAs)are promising in solving these *** paper does not propose a new EA,but does construct a new form of optimization *** new optimization formulation has two differences from the common *** is the objective function is the field error between the desired and the designed,not the usual amplitude error between the desired and the *** difference is beneficial to decrease complexity in some *** second difference is that the design variables are changed as phases of desired radiation field within shaped-region,instead of excitation *** difference leads to the reduction of the number of design variables.A series of synthesis experiments including equally and unequally spaced linear arrays with different pattern shape requirements are applied,and the effectiveness and advantages of the proposed new optimization problems are *** results show that the proposing a new optimization formulation with less complexity is as significant as proposing a new algorithm.

关键词： pattern synthesis evolutionary algorithms linear antenna arrays

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mechanical Properties of 3d-Printed Biomimetic Nacreous Composites: Experiments and Theory

SSRN

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SSRN 2024年

作者： Yan, Yi Zhao, Zheng-Ze Li, Jia Gao, Yuan Feng, Xi-Qiao Deng, Zi-Chen Department of Engineering Mechanics MIIT Key Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical University Xi’an710072 China Institute of Biomechanics and Medical Engineering AML Department of Engineering Mechanics Tsinghua University Beijing100084 China School of Mechanical Engineering Hebei University of Technology Tianjin300401 China

Nacre exhibits exceptional mechanical properties, which are attributed to its brick–mortar microstructure with an integration of stiff mineral platelets and soft organic interfaces. The rapidly developing 3D printing technique has been used to make nacre-inspired composites with similar brick–mortar structure. To date, however, there is still lack of a theoretical model to exactly predict the entire deformation and failure process of biomimetic nacreous composites consisting of various constituent phases with different geometric sizes. Based on a mesomechanical theoretical model, we derive the stress–strain response and macroscopic strength of the brick–mortar structure under uniaxial tension. The brick–mortar structure shows three typical failure modes, according to the occurrence of strain hardening and platelet fracture. Furthermore, we investigate how the mechanical properties of the brick–mortar structure depends on its geometry and constituent properties. It is found that increasing the aspect ratio of the platelets and the stiffness of the soft phase both enhance the strength of the brick–mortar structure. However, increasing the aspect ratio of the platelets promotes strain hardening, while increasing the stiffness of the soft phase leads to the disappearance of strain hardening. Finally, we utilize bi-material 3D printing technology to prepare biomimetic nacre samples and conduct uniaxial tensile mechanical tests, confirming the guiding significance of the theoretical model for the design of 3D-printed biomimetic nacreous composites. © 2024, The Authors. All rights reserved.

关键词： Biomimetics

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A Collaborative Robot Torque Prediction Method Based on CNN-TCN Model

A Collaborative Robot Torque Prediction Method Based on CNN-...

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IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Lina Tong Decheng Cui Chen Wang Liang Peng School of Mechanical Electronic and Information Engineering China University of Mining and Technology Beijing Beijing China State Key Laboratory of Management and Control for Complex Systems (SKLMCCS) Institute of Automation Chinese Academy of Science Beijing China

The traditional dynamical models show lower accuracy when predicting joint movement, and should be compensated. This paper proposed a model combined with the convolutional network(CNN) and temporal convolutional network(TCN) to compensate for the joint torque prediction values that are calculated from the sensing information. The experiments on the Cooperative Universal Robotic Assistant 6 DoF(CURA6) open dataset, including multi-load and multi-velocity, showed the prediction error can be reduced by 20% compared to other network models. Since there are many kinds of joint movement information, the input data form of the deep learning model should be improved. Thus, the kinetic linearization model is proposed to modify the input of sensing data. According to the different motion types of the CURA6 dataset, comparative experiments were taken, and the mean absolute error was less than 6.8%.

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AdvDiffuser: Generating Adversarial Safety-Critical Driving Scenarios via Guided Diffusion

arXiv

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arXiv 2024年

作者： Xie, Yuting Guo, Xianda Wang, Cong Liu, Kunhua Chen, Long School of Computer Science and Engineering Sun Yat-Sen University China School of Computer Science Wuhan University China State Key Laboratory for Management and Control of Complex Systems The Institute of Automation Chinese Academy of Sciences China School of Mechanical and Automotive Engineering Qingdao University of Technology China

Safety-critical scenarios are infrequent in natural driving environments but hold significant importance for the training and testing of autonomous driving systems. The prevailing approach involves generating safety-critical scenarios automatically in simulation by introducing adversarial adjustments to natural environments. These adjustments are often tailored to specific tested systems, thereby disregarding their transferability across different systems. In this paper, we propose AdvDiffuser, an adversarial framework for generating safety-critical driving scenarios through guided diffusion. By incorporating a diffusion model to capture plausible collective behaviors of background vehicles and a lightweight guide model to effectively handle adversarial scenarios, AdvDiffuser facilitates transferability. Experimental results on the nuScenes dataset demonstrate that AdvDiffuser, trained on offline driving logs, can be applied to various tested systems with minimal warm-up episode data and outperform other existing methods in terms of realism, diversity, and adversarial performance. © 2024, CC BY.

关键词： Automatic guided vehicles

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Data-driven control of consensus tracking for discrete-time multi-agent systems

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Journal of the Franklin institute 2023年第7期360卷 4661-4674页

作者： Xiufeng Zhang Gang Wang Jian Sun Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Kunming 650504 China Yunnan International Joint Laboratory of Intelligent Control and Application of Advanced Equipment Kunming 650504 China School of Automation Beijing Institute of Technology Beijing 100081 China Key Lab of Intelligent Control and Decision of Complex Systems Beijing 100081 China

This paper investigates the consensus tracking problem of leader-follower multi-agent systems. Different from most existing works, dynamics of all the agents are assumed completely unknown, whereas some input-output data about the agents are available. It is well known from the Willems et al. Fundamental Lemma that when inputs of a linear time-invariant (LTI) system are persistently exciting, all possible trajectories of the system can be represented in terms of a finite set of measured input-output data. Building on this idea, the present paper proposes a purely data-driven distributed consensus control policy which allows all the follower agents to track the leader agent’s trajectory. It is shown that for a linear discrete-time multi-agent system, the corresponding controller can be designed to ensure the global synchronization with local data. Even if the data are corrupted by noises, the proposed approach is still applicable under certain conditions. Numerical examples corroborate the practical merits of the theoretical results.

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Investigating the dynamic memory effect of human drivers via ON-LSTM

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Science China(Information Sciences) 2020年第9期63卷 83-93页

作者： Shengzhe DAI Zhiheng LI Li LI Dongpu CAO Xingyuan DAI Yilun LIN Department of Automation Tsinghua University Shenzhen International Graduate School Tsinghua University Department of Mechanical and Mechatronics Engineering University of Waterloo State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

It is a widely accepted view that considering the memory effects of historical information(driving operations) is beneficial for vehicle trajectory prediction models to improve prediction accuracy. However,many commonly used models(e.g., long short-term memory, LSTM) can only implicitly simulate memory effects, but lack effective mechanisms to capture memory effects from sequence data and estimate their effective time range(ETR). This shortage makes it hard to dynamically configure the most suitable length of used historical information according to the current driving behavior, which harms the good understanding of vehicle motion. To address this problem, we propose a modified trajectory prediction model based on ordered neuron LSTM(ON-LSTM). We demonstrate the feasibility of ETR estimation based on ON-LSTM and propose an ETR estimation method. We estimate the ETR of driving fluctuations and lane change operations on the NGSIM I-80 dataset. The experiment results prove that the proposed method can well capture the memory effects during trajectory prediction. Moreover, the estimated ETR values are in agreement with our intuitions.

关键词： driving behavior memory effect trajectory prediction historical information ON-LSTM

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Design and locomotion characteristic analysis of a novel tensegrity hopping robot*

Design and locomotion characteristic analysis of a novel ten...

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IEEE International Conference on Robotics and Biomimetics

作者： Jixue Mo Changqing Gao Hao Fang Qingkai Yang Department of Strategic and Advanced Interdisciplinary Research Peng Cheng Laboratory Shenzhen China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China School of Automation Beijing Institute of Technology Beijing China Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing China

In consideration of the poor locomotion ability of most traditional tensegrity robot, a novel tensegrity hopping robot powered by push-pull electromagnets was proposed with better locomotivity. It is able to conduct stable consecutive progressing action and turning action through hopping. This paper covers the structural design, theoretical modeling of the robot’s hopping process, as well as its self-righting analysis, simulation and experimental verification. The average moving speed and relative moving speed were measured to be 0.334m/s and 0.641 body length/s respectively, which obviously surpass the general moving ability of most traditional tensegrity robot. In addition, the robot was also validated to have good self-righting characteristic for stable consecutive hopping. Thus this robot can be potentially applied for search, rescue, detection, etc. in outdoor and unstructured environment.

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The multi-harmonic signal frequencies estimation in finite time 13

The multi-harmonic signal frequencies estimation in finite t...

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13th Multiconference on control problems, MCCP 2020

作者： Vediakova, A.O. Vedyakov, A.A. Pyrkin, A.A. Bobtsov, A.A. Gromov, V.S. Department of Computer Applications and Systems St. Petersburg State University 7/9 Universitetskaya nab. St. Petersburg199034 Russia Department of Control Systems and Informatics Itmo University 49 Kronverkskiy pr. St. Petersburg197101 Russia Laboratory control of Complex Systems Institute of Problems of Mechanical Engineering V.O. 61 Bolshoy pr. St. Petersburg199178 Russia

The paper presents a method to estimate the frequencies of a multi-harmonic signal in finite time. We use parameterization based on applying delay operators to a measurable signal. The result is a linear regression model with an unknown vector which depends on the signal parameters. We use Dynamic Regressor Extension and Mixing method to replace the n-th order regression model with scalar regressions. After that, we estimate the parameters separately using the standard gradient descent method. In the last step, we find algebraically the finite-time parameter estimates. The set of numerical simulations demonstrates the efficiency of the proposed approach. © Published under licence by IOP Publishing Ltd.

关键词： Frequency estimation

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On the Notch Sensitivity of Nacre: A Large-Scale Numerical Investigation

SSRN

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SSRN 2024年

作者： Yan, Yi Chen, Xiao Gao, Yuan Deng, Zi-Chen Feng, Xi-Qiao Department of Engineering Mechanics MIIT Key Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical University Xi’an710072 China Department of Materials Science and Technology National University of Singapore 117575 Singapore School of Mechanical Engineering Hebei University of Technology Tianjin300401 China Institute of Biomechanics and Medical Engineering AML Department of Engineering Mechanics Tsinghua University Beijing100084 China

The brick–mortar microstructure of nacre endows it with remarkable strength and fracture toughness. Notably, nacre demonstrates robustness against flaws and minor cracks, although its microstructural mechanisms remain elusive. A significant challenge is that the fracture behavior of nacre spans a wide range of length scales, from the mineral platelet less than 1 μm to a damage process zone extending several millimeters. In this study, we employ a multiscale tension-shear-chain (TSC) network model to simulate the large-scale fracture process of nacre's brick-and-mortar structure. In particular, we investigate how the fracture behavior of nacre is influenced by notches of varying sizes. Our large-scale simulations show that with the decrease in the preexisting-crack length, an independent crack initiation gradually replaces the precrack propagation during the fracture of nacre. This observation confirms the critical transition of fracture from the process dominated by fracture toughness to the one by strength, namely from notch sensitivity to insensitivity. Further investigations are carried out on the dependence of this fracture transition on constituent and microstructural parameters, including the interfacial cohesive strength and microstructural randomness. We also propose a scaling law formulating the relationship between these parameters and the notch sensitivity, which provides insight into nacre’s tolerance to flaws at a microscale level. Our findings could guide the design of nacre-inspired artificial materials for fracture prevention. © 2024, The Authors. All rights reserved.

关键词： Fracture toughness

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Mechanics and Thermodynamics of Multivalent-Binding Induced Shrinkage of Hydrogels

SSRN

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SSRN 2024年

作者： Rajendran, Barathan Chen, Xiao Wang, Xin Wu, Tao Goh, Kek Boon Department of Mechanical Engineering School of Engineering Monash University Malaysia Jalan Lagoon Selatan Bandar Sunway Selangor 47500 Malaysia Institute of Biomechanics and Medical Engineering Department of Engineering Mechanics Tsinghua University Beijing100084 China School of Mechanics Civil Engineering and Architecture Northwestern Polytechnical University Xi’an710072 China MIIT Key Laboratory of Dynamics and Control of Complex Systems Xi’an710072 China

Understanding the fundamental mechanism of shrink hydrogel sensors necessitates a complete comprehension of analyte-centered multivalent binding that occurs within their salt-rich microenvironments. However, the mechanics and thermodynamics governing this phenomenon remain insufficiently understood. Here, we derive a theoretical framework that examines the formation of temporary cross–links between the polymeric chains induced by the specific binding interaction between the fixed receptors and the multivalent analytes. As a highlight of our theory, we use statistical thermodynamics to characterize the analyte transport and these multivalent complexations, allowing us to subsequently determine the mechanics of the shrinkage experienced by these hydrogel sensors. Consequently, our theory unveils the valency of the bindings, setting an upper bound for the performance of these shrink sensors;i.e., tuning the receptor density allows us to further modulate their performance. In summary, our theory tightly correlates the microscopic properties of the analyte and hydrogel to the macroscopic shrinking properties of these sensors, hence facilitating a structured design and optimization regime for advanced biomedical applications. © 2024, The Authors. All rights reserved.

关键词： Shrinkage

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