AOS自适应帧生成算法的性能分析与应用研究

对AOS协议中的自适应帧生成算法进行研究,通过严格地理论分析,给出了该算法的有效信息传送速率计算公式。进一步,将数据帧生成模块与虚拟信道复用模块作为一个整体进行考虑,针对现有的AOS多路复用技术的不足,提出了自适应帧生成算法与虚拟信道调度算法相结合的多路复用新方法。研究结果表明,通过在多路复用中应用自适应帧生成算法,并设置合理的帧生成时间门限值,可以比目前主要使用的、采用高效率帧生成算法与虚拟信道调度算法相结合的多路复用方法获得更好的包时延性能。     

Adaptive fuzzy terminal sliding mode control for the free-floating space manipulator with free-swinging joint failure

Space manipulator with free-swinging joint failure simultaneously contains kinematic and dynamic coupling relationships, so it belongs to a new underactuated system. To allow the manipulator to carry on tasks, an effective robust underactuated control method for the space manipulator with free-swinging joint failure is studied in this paper. Considering the effect of model uncertainty and joint torque disturbance, a robust underactuated control system based on the Terminal Sliding Mode Controller (TSMC) is designed, but two drawbacks are discussed: (A) Robustness depraves with eliminating chattering. (B) Control parameters are difficult to be determined under unknown uncertainty and disturbance. To improve the TSMC, the adaptive fuzzy controller is introduced to estimate the real effect of unknown uncertainty and disturbance according to deviations of sliding mode and its reaching law. The estimated result is directly compensated into active joints torque. In simulation, the space manipulator with free-swinging joint executes tasks based on the TSMC and the Adaptive Fuzzy Terminal Sliding Mode Controller (AFTSMC) respectively. Same tasks can be finished with smaller joints torque and stronger robustness based on the AFTSMC. Therefore, AFTSMC can serve as an effective robust control method for the space manipulator with free-swinging joint failure under unknown model uncertainty and torque disturbance.     

自适应滤波在磁暴期间地磁导航中的应用

对磁暴期间巡航导弹巡航段的地磁导航问题进行了讨论。针对地磁导航在磁暴期间常规卡尔曼滤波容易发散的问题,提出了一种基于Sage\|Husa自适应卡尔曼滤波的地磁导航算法。算法根据新息的变化自适应调整测量噪声矩阵,消除了磁暴影响,提高了滤波稳定性。仿真表明在选择合适的遗忘因子基础上,即使在磁暴最剧烈的时间段,采用自适应卡尔曼滤波仍能保证一定的精度,定位精度在200m以内,满足巡航导弹中程制导的精度要求。     

基于归一化神经网络的航天器自适应姿态跟踪控制

针对以变速控制力矩陀螺（VSCMGs）为姿态控制执行机构的航天器在同时考虑惯性参数和执行机构不确定性情况下的姿态跟踪控制问题,提出了一种基于归一化神经网络的自适应姿态跟踪控制方法。设计一个非线性反馈控制器作为航天器姿态控制的基本控制器,利用归一化神经网络设计补偿控制器,用以在线估计和消除包含系统不确定参数的未知不确定函数的影响,避免了标准自适应控制方法需要进行大量不确定参数估计的缺陷。采用神经网络输入归一化技术,简化了闭环系统复杂的稳定性分析过程。理论分析证明了闭环系统的稳定性和姿态跟踪误差的收敛性。仿真结果表明,所提出的控制方法能满足航天器在惯性参数和执行机构不确定性及外干扰存在情况下的高精度姿态跟踪控制要求。
     

六自由度气浮台及其姿态平台自动平衡系统设计

针对卫星交会对接中相对姿态和位置的控制问题,设计一种6自由度（DOF）气浮台,并推导出描述两个气浮台模拟卫星交会对接运动的耦合6自由度(DOF)动力学模型。为解决气浮台模拟空间失重环境中的重力力矩干扰问题,设计一种基于应用新颖的快速非奇异终端滑模面的自适应有限时间控制器的自动平衡系统,估计出质心位置,通过控制三个平衡质量块补偿姿态平台质心与旋转中心之间的位置偏差。李雅普诺夫理论推导和仿真结果表明,该方法可以快速完成对姿态平台质心位置的准确估计,实现质心与旋转中心的高度重合。     

Adaptive path planning for unmanned aerial vehicles based on bi-level programming and variable planning time interval

This paper presents an adaptive path planner for unmanned aerial vehicles (UAVs) to adapt a real-time path search procedure to variations and fluctuations of UAVs’ relevant performances, with respect to sensory capability, maneuverability, and flight velocity limit. On the basis of a novel adaptability-involved problem statement, bi-level programming (BLP) and variable planning step techniques are introduced to model the necessary path planning components and then an adaptive path planner is developed for the purpose of adaptation and optimization. Additionally, both probabilistic-risk-based obstacle avoidance and performance limits are described as path search constraints to guarantee path safety and navigability. A discrete-search-based path planning solution, embedded with four optimization strategies, is especially designed for the planner to efficiently generate optimal flight paths in complex operational spaces, within which different surface-to-air missiles (SAMs) are deployed. Simulation results in challenging and stochastic scenarios firstly demonstrate the effectiveness and efficiency of the proposed planner, and then verify its great adaptability and relative stability when planning optimal paths for a UAV with changing or fluctuating performances.     

基于鲁棒自适应反步法的航天器姿态跟踪控制

针对存在未知惯量矩阵和外干扰的刚体航天器姿态跟踪,提出了一种鲁棒自适应控制方法。综合自适应反步法和非线性L2增益干扰抑制方法,用自适应反步法构造系统的Lyapunov函数,获得了具L2增益的鲁棒自适应控制器,以保证姿态跟踪误差系统为一致最终有界稳定,确保估计的航天器惯量参数的有界性,并使从外干扰输入到评价输出的L2增益不大于给定值。仿真结果验证了方法的有效性和可行性。     

再入机动飞行器自适应轨迹线性化控制

针对一类多输入多输出模型不确定系统,提出了一种基于广义模糊神经网络的自适应轨迹线性化控制方法(ATLC).针对再入机动飞行器(MRV)进行了控制器设计和分析.MRV气动参数存在较大的不确定,这会导致轨迹线性化控制器(TLC)鲁棒性能下降.利用广义模糊神经网络(G-FNN)在线补偿系统的非线性建模不确定,改善了控制器性能.基于Lyapunov稳定性理论,证明了ATLC闭环控制系统的稳定性.仿真结果表明自适应轨迹线性化控制系统在飞行器气动参数大范围摄动时仍具有鲁棒性和稳定性,验证了所提出的控制策略的有效性.     

卫星编队飞行输出反馈姿态协同跟踪控制

针对卫星编队飞行姿态协同跟踪的控制过程中角速度信息不可测及外界常值干扰的问题,提出输出反馈协同控制器的设计方法。首先,利用绝对及相对姿态误差信息设计含有积分项的滤波器,并在控制器中引入滤波器的输出信息;同时,考虑到卫星的模型不确定性,设计自适应估计器以对卫星的转动惯量进行在线估计。此外,论证了当卫星编队的通信拓扑结构满足无向树形结构的条件时,仅对任意一颗卫星期望姿态可知即可使整个系统协同收敛于期望值。设计的控制器无需卫星角速度信息即可使角速度信息协同达到期望值,并且引入积分项使得闭环控制系统对常值干扰有良好抑制效果,同时给出降低系统的信息通信压力的分析。最后将提出的算法应用于无需角速度信息反馈的卫星编队飞行的协同控制,仿真结果表明该方法的可行性与有效性,具有实际的应用前景。