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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 1039-1050页

作者： Chi Ma Dianbiao Dong the school of mechanical engineering Northwestern Polytechnical UniversityXi’an 710072China

This paper studies the problem of time-varying formation control with finite-time prescribed performance for nonstrict feedback second-order multi-agent systems with unmeasured states and unknown nonlinearities.To eliminate nonlinearities,neural networks are applied to approximate the inherent dynamics of the system.In addition,due to the limitations of the actual working conditions,each follower agent can only obtain the locally measurable partial state information of the leader agent.To address this problem,a neural network state observer based on the leader state information is designed.Then,a finite-time prescribed performance adaptive output feedback control strategy is proposed by restricting the sliding mode surface to a prescribed region,which ensures that the closed-loop system has practical finite-time stability and that formation errors of the multi-agent systems converge to the prescribed performance bound in finite time.Finally,a numerical simulation is provided to demonstrate the practicality and effectiveness of the developed algorithm.

关键词： Finite-time control multi-agent systems neural network prescribed performance control time-varying formation control

Nanofluidic memristor based on the elastic deformation of nanopores with nanoparticle adsorption

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National Science Review 2024年第4期11卷 46-54页

作者： Xi Zhou Yuanyuan Zong Yongchang Wang Miao Sun Deli Shi Wei Wang Guanghua Du Yanbo Xie Department of Chemistry and Chemical engineering Northwestern Polytechnical University school of Physical Science and Technology Northwestern Polytechnical University school of Aeronautics and Institute of Extreme Mechanics Northwestern Polytechnical University Institute of Modern Physics Chinese Academy of Sciences

The memristor is the building block of neuromorphic computing. We report a new type of nanofluidic memristor based on the principle of elastic strain on polymer nanopores. With nanoparticles absorbed at the wall of a single conical polymer nanopore, we find a pinched hysteresis of the current within a scanning frequency range of 0.01–0.1 Hz, switching to a diode below 0.01 Hz and a resistor above 0.1 Hz. We attribute the current hysteresis to the elastic strain at the tip side of the nanopore, caused by electrical force on the particles adsorbed at the inner wall surface. Our simulation and analytical equations match well with experimental results, with a phase diagram for predicting the system transitions. We demonstrate the plasticity of our nanofluidic memristor to be similar to a biological synapse. Our findings pave a new way for ionic neuromorphic computing using nanofluidic memristors.

关键词： nanofluidics memristor neurocomputing elastic deformation

A toroidal SAW gyroscope with focused IDTs for sensitivity enhancement

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Microsystems & Nanoengineering 2024年第2期10卷 47-56页

作者： Lu Tian Haitao Zhao Qiang Shen Honglong Chang Ministry of Education Key Laboratory of Micro and Nano Systems for Aerospace School of Mechanical EngineeringNorthwestern Polytechnical UniversityXi’an710072China

A surface acoustic wave(SAW)gyroscope measures the rate of rotational angular velocity by exploiting a phenomenon known as the SAW gyroscope effect.Such a gyroscope is a great candidate for application in harsh environments because of the simplification of the suspension vibration mechanism necessary for traditional microelectromechanical system(MEMS)gyroscopes.Here,for the first time,we propose a novel toroidal standing-wave-mode SAW gyroscope using focused interdigitated transducers(FIDTs).Unlike traditional SAW gyroscopes that use linear IDTs to generate surface acoustic waves,which cause beam deflection and result in energy dissipation,this study uses FIDTs to concentrate the SAW energy based on structural features,resulting in better focusing performance and increased SAW amplitude.The experimental results reveal that the sensitivity of the structure is 1.51µV/(°/s),and the bias instability is 0.77°/s,which are improved by an order of magnitude compared to those of a traditional SAW gyroscope.Thus,the FIDT component can enhance the performance of the SAW gyroscope,demonstrating its superiority for angular velocity measurements.This work provides new insights into improving the sensitivity and performance of SAW gyroscopes.

关键词： angular deflection gyroscope

Prediction of collapse process and tipping points for mutualistic and competitive networks with k-core method

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Chinese Physics B 2024年第5期33卷 173-180页

作者：段东立毕菲菲李思凡吴成星吕长春蔡志强 school of Information and Control engineering Xi'an University of Architecture and TechnologyXi'an 710311China school of mechanical engineering Northwestern Polytechnical UniversityXi'an 710072China

Ecosystems generally have the self-adapting ability to resist various external pressures or disturbances,which is always called resilience.However,once the external disturbances exceed the tipping points of the system resilience,the consequences would be catastrophic,and eventually lead the ecosystem to complete collapse.We capture the collapse process of ecosystems represented by plant-pollinator networks with the k-core nested structural method,and find that a sufficiently weak interaction strength or a sufficiently large competition weight can cause the structure of the ecosystem to collapse from its smallest k-core towards its largest k-core.Then we give the tipping points of structure and dynamic collapse of the entire system from the one-dimensional dynamic function of the ecosystem.Our work provides an intuitive and precise description of the dynamic process of ecosystem collapse under multiple interactions,and provides theoretical insights into further avoiding the occurrence of ecosystem collapse.

关键词： complex networks tipping points dimension reduction k-core

Influencer identification of dynamical networks based on an information entropy dimension reduction method

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Chinese Physics B 2024年第4期33卷 375-384页

作者：段东立纪思源袁紫薇 school of Information and Control engineering Xi'an University of Architecture and TechnologyXi'an 710311China school of mechanical engineering Northwestern Polytechnical UniversityXi'an 710072China

Identifying critical nodes or sets in large-scale networks is a fundamental scientific problem and one of the key research directions in the fields of data mining and network science when implementing network attacks, defense, repair and control.Traditional methods usually begin from the centrality, node location or the impact on the largest connected component after node destruction, mainly based on the network structure.However, these algorithms do not consider network state changes.We applied a model that combines a random connectivity matrix and minimal low-dimensional structures to represent network connectivity.By using mean field theory and information entropy to calculate node activity,we calculated the overlap between the random parts and fixed low-dimensional parts to quantify the influence of node impact on network state changes and ranked them by importance.We applied this algorithm and the proposed importance algorithm to the overall analysis and stratified analysis of the C.elegans neural network.We observed a change in the critical entropy of the network state and by utilizing the proposed method we can calculate the nodes that indirectly affect muscle cells through neural layers.

关键词： dynamical networks network influencer low-dimensional dynamics network disintegration

Reheat effect on the improvement in efficiency of the turbine driven by pulse detonation

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Defence Technology（防务技术） 2024年第1期31卷 200-210页

作者： Junyu Liu Zhiwu Wang Zixu Zhang Junlin Li Weifeng Qin Jingjing Huang school of Power and Energy Northwestern Polytechnical UniversityXi’an 710072China Shenzhen Research Institute of northwestern polytechnical university Shenzhen 518057China mechanical engineering College Xi’an Shiyou UniversityXi’an 710065China

Due to the strong unsteadiness of pulse detonation,large flow losses are generated when the detonation wave interacts with the turbine blades,resulting in low turbine efficiency.Considering that the flow losses are dissipated into the gas as heat energy,some of them can be recycled during the expansion process in subsequent stages by the reheat effect,which should be helpful to improve the detonationdriven turbine efficiency.Taking this into account,this paper developed a numerical model of the detonation chamber coupled with a two-stage axial turbine,and a stoichiometric hydrogen-air mixture was used.The improvement in turbine efficiency attributable to the reheat effect was calculated by comparing the average efficiency of the stages with the efficiency of the two-stage turbine.The research indicated that the first stage was critical in suppressing the flow unsteadiness caused by pulse detonation,which stabilized the intake condition of the second stage and consequently allowed much of the flow losses from the first stage to be recycled,so that the efficiency of the two-stage turbine was improved.At a 95%confidence level,the efficiency improvement was stable at 4.5%—5.3%,demonstrating that the reheat effect is significant in improving the efficiency of the detonation-driven turbine.

关键词： Pulse detonation turbine engine Hydrogen detonation Turbine efficiency Reheat effect Multi-cycle detonation

Metal–phenolic coordination crystals derived magnetic hollow carbon spheres for ultrahigh electromagnetic wave absorption

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Nano Research 2024年第3期17卷 1616-1624页

作者： Hanxiao Xu Zizhuang He Yiruo Wang Xiangru Ren Panbo Liu school of Chemistry and Chemical engineering Northwestern Polytechnical UniversityXi’an 710129China

Owing to the tunable compositions and versatile functionality,the development of eco-friendly metal–phenolic coordination crystals derivatives is highly anticipated for electromagnetic wave absorption.In this study,three kinds of magnetic hollow carbon spheres(HCSs)with macro-meso-microporous characteristics,including Fe/HCS,Co/HCS,and CoNi/HCS,are successfully fabricated via the co-operative hard template and self-assembling process,in which magnetic particles are encapsulated in carbon shell matrix after the pyrolysis of metal–polyphenol coordination crystals and further subsequent template removal.On the one hand,hierarchical macro-meso-micropores effectively balance the impedance gap between absorbers and air and introduce structural defects or distortion,leading to matched impedance and enhanced dipolar/defect polarization.On the other hand,wrapped magnetic particles provide uncountable hetero-interfaces and induce ferromagnetic resonance,resulting in strengthened interfacial polarization and additional magnetic loss.In particular,enhanced minimum reflection loss(RL,min)and broadband effective absorption bandwidth(EAB)are achieved with only 10 wt.%filler loading.Specifically,the RL,min and EAB values are-57.5 dB and 7.2 GHz for Fe/HCS,-50.0 dB and 5.8 GHz for Co/HCS,and-52.1 dB and 6.7 GHz for CoNi/HCS,respectively.Moreover,this work provides us a modular-assembly strategy to regulate the hollow cavity of absorbers and simultaneously manipulates the chemical components of absorbers to regulate electromagnetic wave absorption performance.

关键词： hollow engineering hierarchical pores hetero-interfaces synergetic effect electromagnetic wave absorption

Deep-reinforcement-learning-based UAV autonomous navigation and collision avoidance in unknown environments

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Chinese Journal of Aeronautics 2024年第3期37卷 237-257页

作者： Fei WANG Xiaoping ZHU Zhou ZHOU Yang TANG school of Astronautics Northwestern Polytechnical UniversityXi’an 710072China school of Aeronautics Northwestern Polytechnical UniversityXi’an 710072China

In some military application scenarios,Unmanned Aerial Vehicles(UAVs)need to perform missions with the assistance of on-board cameras when radar is not available and communication is interrupted,which brings challenges for UAV autonomous navigation and collision avoidance.In this paper,an improved deep-reinforcement-learning algorithm,Deep Q-Network with a Faster R-CNN model and a Data Deposit Mechanism(FRDDM-DQN),is proposed.A Faster R-CNN model(FR)is introduced and optimized to obtain the ability to extract obstacle information from images,and a new replay memory Data Deposit Mechanism(DDM)is designed to train an agent with a better performance.During training,a two-part training approach is used to reduce the time spent on training as well as retraining when the scenario changes.In order to verify the performance of the proposed method,a series of experiments,including training experiments,test experiments,and typical episodes experiments,is conducted in a 3D simulation environment.Experimental results show that the agent trained by the proposed FRDDM-DQN has the ability to navigate autonomously and avoid collisions,and performs better compared to the FRDQN,FR-DDQN,FR-Dueling DQN,YOLO-based YDDM-DQN,and original FR outputbased FR-ODQN.

关键词： Faster R-CNN model Replay memory Data Deposit Mechanism(DDM) Two-part training approach Image-based Autonomous Navigation and Collision Avoidance(ANCA) Unmanned Aerial Vehicle(UAV)

Manipulator tracking technology based on FSRUKF

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Journal of Systems engineering and Electronics 2024年第2期35卷 473-484页

作者： SHI Guoqing ZHANG Boyan ZHANG Jiandong YANG Qiming HUANG Xiaofeng QUE Jianyao PU Junwei GENG Xiutang school of Electronics and Information Northwestern Polytechnical UniversityXi’an 710072China Northwest Institute of mechanical&Electrical engineering Xianyang 712000China

Aiming at the shortcoming that the traditional industrial manipulator using off-line programming cannot change along with the change of external environment,the key technologies such as machine vision and manipulator control are studied,and a complete manipulator vision tracking system is designed.Firstly,Denavit-Hartenberg(D-H)parameters method is used to construct the model of the manipulator and analyze the forward and inverse kinematics equations of the manipulator.At the same time,a binocular camera is used to obtain the threedimensional position of the target.Secondly,in order to make the manipulator track the target more accurately,the fuzzy adaptive square root unscented Kalman filter(FSRUKF)is proposed to estimate the target state.Finally,the manipulator tracking system is built by using the position-based visual servo.The simulation experiments show that FSRUKF converges faster and with less error than the square root unscented Kalman filter(SRUKF),which meets the application requirements of the manipulator tracking system,and basically meets the application requirements of the manipulator tracking system in the practical experiments.

关键词： square root unscented Kalman filter(SRUKF) fuzzy inference manipulator visual servo

Terminal attitude selection method of missile attack aircraft

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Chinese Journal of Aeronautics 2024年第2期37卷 385-400页

作者： Peng HOU Yang PEI Yuxue GE Mengjie ZHU school of Aeronautics Northwestern Polytechnical UniversityXi’an 710072China Research Department Shanghai Electro-Mechanical Engineering InstituteShanghai 201108China

The appropriate terminal attitude of missile is important to improve the damage effectiveness of fragmentation warhead against aircraft targets.In this paper,three missile terminal attitude selection methods are proposed to solve the problem of terminal attitude selection in different situations according to their respective evaluation indexes.The MVE-based method uses the damage probability of each detonation point around the aircraft at a given attitude of missile to calculate the Mean Volume of Effectiveness(MVE)at the corresponding attitude,and then uses the MVE for different attitudes to select the terminal strike attitude.The detonation position-based method addresses the case where the missile detonation position can be assessed in advance.Given the effects of missile guidance errors and fuze activation position errors,Monte Carlo simulations are used to calculate the damage probability of aircraft with different strike attitudes,from which the terminal strike attitude is selected.The BP-ANN model-based method uses the constructed Back Propagation Artificial Neural Network(BP-ANN)model instead of simulation to calculate the evaluation indexes in the corresponding cases of MVE-based method and Detonation position-based method,which can improve the efficiency of attitude selection.Simulations are conducted for different scenarios to verify the feasibility and effectiveness of the proposed method.

关键词： Aircraft vulnerability Attitude selection BP-ANN Damage probability Damage effectiveness