Multi-UAV coordination control by chaotic grey wolf optimization based distributed MPC with event-triggered strategy

The paper proposes a new swarm intelligence-based distributed Model Predictive Control (MPC) approach for coordination control of multiple Unmanned Aerial Vehicles (UAVs). First, a distributed MPC framework is designed and each member only shares the information with neighbors. The Chaotic Grey Wolf Optimization (CGWO) method is developed on the basis of chaotic initialization and chaotic search to solve the local Finite Horizon Optimal Control Problem (FHOCP). Then, the distributed cost function is designed and integrated into each FHOCP to achieve multi-UAV formation control and trajectory tracking with no-fly zone constraint. Further, an event-triggered strategy is proposed to reduce the computational burden for the distributed MPC approach, which considers the predicted state errors and the convergence of cost function. Simulation results show that the CGWO-based distributed MPC approach is more computationally efficient to achieve multi-UAV coordination control than traditional method.     

Spacecraft formation-containment flying control with time-varying translational velocity

This paper investigates the problem of Spacecraft Formation-Containment Flying Control (SFCFC) when the desired translational velocity is time-varying. In SFCFC problem, there are multiple leader spacecraft and multiple follower spacecraft and SFCFC can be divided into leader spacecraft’s formation control and follower spacecraft’s containment control. First, under the condition that only a part of leader spacecraft can have access to the desired time-varying translational velocity, a velocity estimator is designed for each leader spacecraft. Secondly, based on the estimated translational velocity, a distributed formation control algorithm is designed for leader spacecraft to achieve the desired formation and move with the desired translational velocity simultaneously. Then, to ensure all follower spacecraft converge to the convex hull formed by the leader spacecraft, a distributed containment control algorithm is designed for follower spacecraft. Moreover, to reduce the dependence of the designed control algorithms on the graph information and increase system robustness, the control gains are changing adaptively and the parametric uncertainties are handled, respectively. Finally, simulation results are provided to illustrate the effectiveness of the theoretical results.     

Virtual target guidance-based distributed model predictive control for formation control of multiple UAVs

The paper proposes a Virtual Target Guidance (VTG)-based distributed Model Predictive Control (MPC) scheme for formation control of multiple Unmanned Aerial Vehicles (UAVs). First, a framework of distributed MPC scheme is designed in which each UAV only shares the information with its neighbors, and the obtained local Finite-Horizon Optimal Control Problem (FHOCP) can be solved by swarm intelligent optimization algorithm. Then, a VTG approach is developed and integrated into the distributed MPC scheme to achieve trajectory tracking and obstacle avoidance. Further, an event-triggered mechanism is proposed to reduce the computational burden for UAV formation control, which takes into consideration the predictive state errors as well as the convergence of cost function. Numerical simulations show that the proposed VTG-based distributed MPC scheme is more computationally efficient to achieve formation control of multiple UAVs in comparison with the traditional distributed MPC method.     

Time and phase synchronization using clutter observations in airborne distributed coherent aperture radars

Airborne Distributed Coherent Aperture Radar(ADCAR) is one of the most promising next-generation radars to significantly improve target detection and discrimination abilities. However, time and phase synchronization among unit radars should be done before an ADCAR is intended to cohere on a potential target. To address this problem, a time and phase synchronization technique using clutter observations is proposed in this paper. Clutter returns from different azimuths and elevations on the surfac...     

天津市宏观税收负担问题实证分析

采用计量分析方法对天津市1978～2005年数据区间的财政收入与经济总量、宏观税负与经济增长之间的关系进行研究。在结合天津市经济发展、税收增长、产业结构变化等实际情况的基础上，试图通过揭示变量之间的数量关系来印证和探寻天津市宏观税负与地区经济发展之间的关系。发现改革开放以来，天津总体税负适度，宏观税负呈下降趋势，但滨海新区的开发将为天津提供充足的税源，因而宏观税负会有所回升；20世纪90年代以来，天津第三产业发展迅速，第三产业对经济的贡献度与宏观税负的变化呈正向反应机制，而第二产业对GDP的贡献比重总体呈下降趋势；与其他直辖市相比，天津的宏观税负水平居于中游。     

一种Turbo码短交织器的设计方法

短交织器的结构对Turbo码的性能有很大的影响,通过探讨Turbo码编码器中反馈最小多项式的周期性和序列的可整除性问题,把可整除序列变为不可整除序列交织器,从而获得高码重的码字,并给出一种基于输入信息序列周期性特性的短交织器的设计方法.仿真结果表明是一种很有效的设计方法.     

再入飞行体远场二维湍流尾迹的工程计算方法

建立了再入体再入大气层形成的尾迹的工程模型,并运用边界层理论求解二维等压湍流尾迹流动。根据Ｐｒａｎｄｔｌ的湍流切应力的混合长度理论和Ｔａｙ－ｌｏｒ的涡量交换理论分别获得速度分布和温度分布,根据Ｌｙｋｏｕｄｉｓ的远场尾迹理论计算了轴心参数分布,并且获得了尾迹长度、自由电子数密度等尾迹参数。计算结果与同类文献一致。文章的目的是为计算尾迹的雷达散射截面提供初始参数。     

一种星间相对状态估计方法

编队自主导航是实现分布式遥感系统星间协同观测的基础。为实现分布式遥感系统星间相对状态的精确确定,提出一种基于MEMS激光雷达与纳型星罗盘的星间相对状态估计方法。借助相对姿态、轨道运动学方程建立了星间相对状态的无迹卡尔曼滤波(UKF)算法,解决了MEMS激光雷达测量与参考航天器姿态耦合的问题,并利用预定入轨参数及高精度轨道外推(HPOP)对方法进行仿真校验。结果表明,该方法具有可行性和实用性,估计精度满足分布式遥感任务需求,能够较好解决中远距离星间相对状态估计问题。     

航空发动机分布式控制系统不确定性鲁棒H∞容错控制

为减小不确定性对航空发动机分布式控制系统性能的影响,针对具有参数摄动、不确定时延、执行机构动态故障、外部噪声干扰四种不确定性的航空发动机分布式控制系统,提出了一种基于鲁棒H∞理论的容错控制方法.首先对系统不确定性进行数学描述,将不确定时延视为服从齐次Markov链分布的随机变量,将执行机构故障等效为存在均值和方差约束的随机变量,并在此基础上建立整个闭环系统的增广模型;其次证明了该增广模型保持均方渐进稳定且具备H∞性能的充分条件;最后利用线性矩阵不等式(LMI)理论给出闭环系统鲁棒H∞容错控制器的设计方法.仿真结果表明该方法能够保证控制系统均方渐进稳定,并对以上四种不确定因素具有鲁棒性,同时对于飞行包线其他各点具有较好的动态响应.     

分布式自动化测量系统的研制

近年来,计算机辅助测试系统(CAT)及单片微机的发展极为迅速,但当众多测试点分布在相距较远的不同房间、不同地区且被测参数的数据处理复杂时,欲同时进行多点的实时测量,上述两种系统的应用就受到限制。本文作者研制的分布式自动化测量系统以一台 IBM—PC 计算机为控制站,可对各测试站进行程控并能进行复杂的数据处理。控制站和测试站间可相互通信、信息共存、资源共享,亦可独立工作。本文从软件、硬件两个方面说明了该系统的设计原理与要点。