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cooperative formation control of multiple aerial vehicles based on guidance route in a complex task environment

Chinese Journal of Aeronautics 2020年第2期33卷 701-720页

作者： Guibin SUN Rui ZHOU Kun XU Zhi WENG Yuhang ZHANG Zhuoning DONG Yingxun WANG School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100083China State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100083China College of Electronic Information Engineering Inner Mongolia UniversityHohhot 010021China

In recent years,formation control of multi-agent has been a significant research subject in the field of cooperative control.However,previous works have mainly concentrated on formation control for simple point-mass model and linear model.In contrast,this paper presents a novel cooperative algorithm for multiple air vehicles formation control,which aims to devise a control strategy based on guidance route to achieve precisely coordinated formation control for a group of fixed-wing aircraft in a complex task environment.The proposed method introduces the leader-follower structure for effective organization of the multi-agent coordination.Moreover,the Partial Integrated Formation and Control(PIFC)is adopted to design the control law for Guidance-Route based Formation Control(GRFC).Additionally,the proposed approach designs two guidance-route generation strategies for two special situations to demonstrate the effectiveness of GRFC in complex task environments.Theoretical analysis reveals that the proposed control protocol for guidance command can ensure the overall stability and tracking accuracy of the system.Numerical simulations are performed to illustrate the theoretical results,and verify that the proposed approach can achieve coordinated formation control precisely in a complex task environment.

关键词： Complex task environments cooperative formation control Fixed-wing aircraft Guidance route Leader-follower structure Partial integrated formation and control

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cooperative guidance of seeker-less missile considering localization error

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Chinese Journal of Aeronautics 2019年第8期32卷 1933-1945页

作者： Jianbo ZHAO Shuxing YANG Fenfen XIONG School of Aerospace Engineering Beijing Institute of TechnologyBeijing 100081China Xi’an Modern Control and Technology Institute Xi’an 710065China

To perform an accurate fire-and-forget attack of seeker-less missile with the consideration of the localization error, a new cooperative guidance strategy of multi-missiles is proposed in this work. Firstly, an estimated position of the seeker-less missile is carefully selected, based on which the slant angle of the line between the estimated and real positions of the seeker-less missile is approximately derived inspired by the localization theory of GPS(Global Position System).Then, respectively based on the guidance law considering the impact angle and time constraints, the consensus theory and the finite-time convergence approach, three cooperative guidance laws employing the previously obtained slant angle are derived for intensive attack to address either a stationary or maneuvering target. Simulation results well demonstrate the effectiveness and advantages of the proposed cooperative guidance strategy.

关键词： cooperative guidance Fire-and-forget Intensive attack Localization error Missile guidance Seeker-less

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cooperative localization methods of autonomous underwater vehicle swarm integrated with inverted ultrashort baseline

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Chinese Journal of Acoustics 2024年第1期43卷 1-22页

作者： LIU Feng WANG Yuxiong CHEN Huifang XIE Lei College of Information Science and Electronic Engineering Zhejiang UniversityHangzhou 310027 Zhejiang Provincial Laboratory of Information Processing Communication and NetworkingHangzhou 310027 Zhoushan Ocean Research Center Zhejiang UniversityZhoushan 316021

A cooperative localization factor graph for the autonomous underwater vehicle(AUV) swarm integrated with inverted ultrashort baseline(iUSBL) is constructed. Two cooperative localization methods of slave AUV in an AUV swarm are proposed, namely the extended Kalman filter(EKF)-based cooperative localization method and particle belief propagation(BP)-based cooperative localization method, where the effect of the positioning error of leader AUV is mitigated. For the EKF-based cooperative localization method, the state of the slave AUV is reconstructed with the positioning information of leader AUV to achieve cooperative localization. In the particle-BP-based cooperative localization method,the measurement particles are generated to realize cooperative localization. The proposed methods can solve the poor real-time problem of traditional methods, and only require oneway acoustic positioning transmission measurement to realize the localization of the slave AUV. Simulation results show that the two proposed cooperative localization methods work well for slave AUVs with different localization abilities. Compared with the dead-reckoning method, the localization errors of two proposed methods are reduced by more than 80%,and the positioning error accumulation is effectively suppressed. Furthermore, the proposed cooperative localization methods can operate normally under a high packet loss rate of the underwater acoustic channel, and the localization performance is not significantly degraded.

关键词： underwater autonomous cooperative

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cooperative Relay Based on Exploiting Hybrid ARQ

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China Communications 2019年第9期16卷 187-200页

作者： Mingxuan Li Jie Lin Liang Zhong Mengting Wang Wei Lv China University of Geosciences School of Mechanical Engineering and Electronic Information Department of Electronics and Information EngineeringWuhan 430074China Air Force Early Warning Academy Wuhan 430010China

In order to compensate the primary performance degradation,which is caused by the co-existing secondary communications or the heavy fading of primary link,a cooperative relay scheme is proposed in cognitive radio systems by taking advantage of the opportunities that are arisen by the primary ARQ(Automatic Repeat-reQuest)retransmission.With the help of this scheme,nontrivial relay assistances for the primary retransmission are achieved without initialization phase.The cooperation among secondary users provides additional spatial diversity to facilitate the primary transmission.This can be accomplished with an oblivious primary system and without assuming any non-causal information about the primary data.The scheme are further extended to the scenarios where multiple cognitive pairs exist.Performance analyses of the schemes are devised and the effectiveness is verified via simulations.

关键词： cognitive radio cooperative relay ARQ coexistence outage probability

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cooperative Spectrum Sensing in Cognitive Radio Using Bayesian Updating with Multiple Observations

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Journal of Electronic Science and Technology 2019年第3期17卷 252-259页

作者： Yung-Fa Huang Jyu-Wei Wang the Department of Information and Communication Engineering Chaoyang University of TechnologyTaichung 41368 the Department of Photonics and Communication Engineering Asia UniversityTaichung 41354

We study cooperative spectrum sensing in cognitive radio (CR) networks using the hidden Markov model (HMM) for opportunistic spectrum access (OSA). We assume that the primary channel operates in a time division multiple address (TDMA) manner. Thus, spectrum sensing is operating in a slot-by-slot basis. In contrast to the conventional Bayesian updating using only one observation, in this work, we propose to perform the update in a concatenated fashion with all the observations available from the secondary users (SUs). In the proposed scheme, a predefined threshold on the belief is used for determining the channel activity. With the threshold, the proposed scheme is more flexible in the system operation than the simple majority vote scheme, in which no such threshold is available. We compare, by simulations, the performance of the proposed concatenated update scheme with that of the majority vote scheme and show that the probabilities of correctly detecting a busy state and an idle state are about 1 as the number of SUs is as large as 15, so the effects of the further increase in the number of SUs are limited.

关键词： Bayesian updating cognitive radio(CR) cooperative sensing hidden Markov model(HMM)

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cooperative Beamforming for Multi-Relay Networks with Limited Feedback

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China Communications 2019年第10期16卷 56-64页

作者： Jia Zhu Ying Yao Yulong Zou Tong Wu School of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications

In this paper, we consider a multi-relay cooperative communication network that consists of a source node transmitting to its destination with the help of multiple decode-and- forward (DF) relays. Specifically, the DF relays that succeed in decoding the source signal are allowed to re-transmit their decoded results simultaneously to the destination in a cooperative beamforming manner. In order to carry out the cooperative beamforming, the destination needs to send the quantized channel state information (CSI) to the relays through a limited feedback channel in the face of channel quantization errors (CQE). We propose a CQE oriented multi-relay beamforming (MRB) scheme, denoted CQE-MRB for short, for the sake of improving the throughput of relay-destination transmissions. An effective throughput defined as the difference between the transmission rate and the feedback rate is used to measure an outage probability of the source-destination transmission. Simulation results demonstrate that the outage performance of proposed CQEMRB scheme is improved substantially with an increasing number of relays. Moreover, it is shown that the number of channel quantization bits can be further optimized to minimize the outage probability of proposed CQE-MRB scheme.

关键词： cooperative beamforming relay selection effective throughout limited feedback quantization error

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Modelling of a Human Driver’s Interaction with Vehicle Automated Steering Using cooperative Game Theory

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IEEE/CAA Journal of Automatica Sinica 2019年第5期6卷 1095-1107页

作者： Xiaoxiang Na David J.Cole Department of Engineering University of Cambridge

The introduction of automated driving systems raised questions about how the human driver interacts with the automated system. Non-cooperative game theory is increasingly used for modelling and understanding such interaction, while its counterpart, cooperative game theory is rarely discussed for similar applications despite it may be potentially more suitable. This paper describes the modelling of a human driver’s steering interaction with an automated steering system using cooperative game theory. The distributed Model Predictive Control approach is adopted to derive the driver’s and the automated steering system’s strategies in a Pareto equilibrium sense, namely their cooperative Pareto steering strategies. Two separate numerical studies are carried out to study the influence of strategy parameters, and the influence of strategy types on the driver’s and the automated system’s steering performance. It is found that when a driver interacts with an automated steering system using a cooperative Pareto steering strategy, the driver can improve his/her performance in following a target path through increasing his/her effort in pursuing his/her own interest under the driver-automation cooperative control goal. It is also found that a driver’s adoption of cooperative Pareto steering strategy leads to a reinforcement in the driver’s steering angle control, compared to the driver’s adoption of non-cooperative Nash strategy. This in turn enables the vehicle to return from a lane-change maneuver to straight-line driving swifter.

关键词： Automated driving system cooperative game theory driver modelling

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Efficient Multiagent Policy Optimization Based on Weighted Estimators in Stochastic cooperative Environments

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Journal of Computer Science & Technology 2020年第2期35卷 268-280页

作者： Yan Zheng Jian-Ye Hao Zong-Zhang Zhang Zhao-Peng Meng Xiao-Tian Hao College of Intelligence and Computing Tianjin UniversityTianjin 300350China National Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China

Multiagent deep reinforcement learning (MA-DRL) has received increasingly wide attention. Most of the existing MA-DRL algorithms, however, are still inefficient when faced with the non-stationarity due to agents changing behavior consistently in stochastic environments. This paper extends the weighted double estimator to multiagent domains and proposes an MA-DRL framework, named Weighted Double Deep Q-Network (WDDQN). By leveraging the weighted double estimator and the deep neural network, WDDQN can not only reduce the bias effectively but also handle scenarios with raw visual inputs. To achieve efficient cooperation in multiagent domains, we introduce a lenient reward network and scheduled replay strategy. Empirical results show that WDDQN outperforms an existing DRL algorithm (double DQN) and an MA-DRL algorithm (lenient Q-learning) regarding the averaged reward and the convergence speed and is more likely to converge to the Pareto-optimal Nash equilibrium in stochastic cooperative environments.

关键词： deep reinforcement learning multiagent system weighted double estimator lenient reinforcement learning cooperative Markov game

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Identification of Factors Influencing Out-of-county Hospitalizations in the New cooperative Medical Scheme

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Current Medical Science 2019年第5期39卷 843-851页

作者： Wan-rong LU Wen-jie WANG Chen LI Huang-guo XIONG Yi-lei MA Mi LUO Hong-yu PENG Zong-fu MAO Ping YIN Department of Epidemiology and Bio statistics School of Public HealthTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China Department of Social Medicine and Health Management School of Health SciencesWuhan UniversityWuhan 430071China

Summary:Throughout the duration of the New cooperative Medical Scheme(NCMS),it was found that an increasing number of rural patients were seeking out-of^county medical treatment,which posed a great burden on the NCMS fund.Our study was conducted to examine the prevalence of out-of^county hospitalizations and its related factors,and to provide a scientific basis for follow?up health insurance policies.A total of 215 counties in central and western China from 2008 to 2016 were selected.The total out-of-county hospitalization rate in nine years was 16.95%,which increased from 12.37%in 2008 to 19.21%in 2016 with an average annual growth rate of 5.66%.Its related expenses and compensations were shown to increase each year,with those in the central region being higher than those in the western region.Stepwise logistic regression reveals that the increase in out-of-county hospitalization rate was associated with region(XI),rural population(X2),per capita per year net income(X3),per capita gross domestic product(GDP)(X4),per capita funding amount of NCMS(X5),compensation ratio of out-of^county hospitalization cost(X6),per time average in-county(X7)and out-of-county hospitalization cost(X8).According to Bayesian network(BN),the marginal probability of high out-of^county hospitalization rate was as high as 81.7%.Out-of^county hospitalizations were directly related to X8,X3,X4 and X6.The probability of high out-of-county hospitalization obtained based on hospitalization expenses factors,economy factors,regional characteristics and NCMS policy factors was 95.7%,91.1%,93.0% and 88.8%,respectively.And how these factors affect out-of-county hospitalization and their interrelationships were found out.Our findings suggest that more attention should be paid to the influence mechanism of these factors on out-of-county hospitalizations,and the increase of hospitalizations outside the county should be reasonably supervised and controlled and our results will be used to help guide the formulation of proper intervention policies.

关键词： New cooperative Medical Scheme(NCMS) out-of-county hospitalization rate Bayesian network(BN) Max-Min Hill-Climbing algorithm related factors

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Multiple-constraint cooperative guidance based on two-stage sequential convex programming

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Chinese Journal of Aeronautics 2020年第1期33卷 296-307页

作者： Wei DONG Qiuqiu WEN Qunli XIA Shengjiang YANG School of Aerospace Engineering Beijing Institute of TechnologyBeijing 100081China Beijing Aerospace Technology Institute Beijing 100074China

An improved approach is presented in this paper to implement highly constrained cooperative guidance to attack a stationary target.The problem with time-varying Proportional Navigation(PN)gain is first formulated as a nonlinear optimal control problem,which is difficult to solve due to the existence of nonlinear kinematics and nonconvex constraints.After convexification treatments and discretization,the solution to the original problem can be approximately obtained by solving a sequence of Second-Order Cone Programming(SOCP)problems,which can be readily solved by state-of-the-art Interior-Point Methods(IPMs).To mitigate the sensibility of the algorithm on the user-provided initial profile,a Two-Stage Sequential Convex Programming(TSSCP)method is presented in detail.Furthermore,numerical simulations under different mission scenarios are conducted to show the superiority of the proposed method in solving the cooperative guidance problem.The research indicated that the TSSCP method is more tractable and reliable than the traditional methods and has great potential for real-time processing and on-board implementation.

关键词： Convex optimization cooperative guidance Guidance Multiple constraints Second-order cone programming Sequential convex programming

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