马格努斯效应对旋转弹自动驾驶仪影响的研究

为了分析马格努斯力矩对单通道旋转弹自动驾驶仪的影响,在准弹体系下建立了旋转弹加速度反馈自动驾驶仪模型.观察了不同马格努斯力矩对自动驾驶仪的影响,并与速率陀螺反馈自动驾驶仪进行了对比.仿真结果表明了该建模分析方法的正确性,并证明了加速度反馈相对速率反馈能够有效降低马格努斯力矩对旋转导弹的干扰作用.     

基于贝叶斯决策的图像检索相关反馈方法研究

为了有效解决基于内容图像检索中“语义鸿沟”问题,利用贝叶斯决策理论估计图像检索中相关图像集与不相关图像集合的分类面,通过移动查询点的策略,指导新一轮的查询,将查询点移向好的范例点而远离坏的范例点。试验证明该方法是一种有效的基于统计分析的反馈方法。     

基于NMPC的战斗机末端机动规避策略

利用非线性模型预测控制（nonlinear model predictive control,NMPC）的思想建立了战机末端规避导弹的机动策略求解方法。根据导弹与战机的空战态势,建立了导弹与战机的相对运动微分方程;将导弹的导引律引入到导弹运动模型中,与飞机模型一起构建了系统预测模型,并对飞机和导弹的运动约束进行了分析。通过对导弹结构限制和战术特性的分析,给出了飞机机动规避导弹的性能指标,进而建立了机动规避导弹的最优控制模型。利用高斯伪谱法对模型进行求解,采用滚动优化策略实现了对机动规避策略的闭环求解。针对导弹气动参数和导航比未知以及相对测量量具有噪声的问题,利用极大似然法对导弹的气动参数和导航比进行估计,实现了对系统预测模型的反馈校正。仿真结果表明,此方法能够实现对导弹的机动规避。     

机器人操作空间加速度信号应用研究

机器人力控制存在着动力学不稳定性问题，即系统响应速度和稳定性之间的矛盾。在操作空间一级引人加速度正反馈，既能使系统的响应频带变宽，又能提高系统阻尼。这种方法能有效地提高机器人对高刚度环境的适应性，使力控制的动力学不稳定性问题得到明显地缓和；另外本文研究了用加速度积分信号代替位置差分速度信号来消除差分噪声，提高力控制精度方法的应用前景。     

基于加速度反馈的机械手位置控制

本文首先分析了影响机器人位置控制性能的几个因素。然后针对这些不利因素分析了加速度正反馈和负反馈对它们的不同作用,提出了一种用综合加速度反馈来提高机器人位置控制性能的控制方法。该方法能较大程度地改善机器人阻尼不足的现象,是实现机器人高速高精度控制的一条有效途径。机械手上的实验结果验证了本方法的有效性。     

负反馈放大器分析方法探讨

集成电路出现后,负反馈放大器原先的一些计算公式已不适用。为解决这个问题,提出以新放大器为基础的负反馈放火器输入电阻、输出电阻、放大倍数的计算公式。     

一个人机瞄系统的非线性分析设计

讨论了一个人机瞄系统的非线性分析设计。首先建立了人机瞄系统的非线性模型,然后采用反馈线性化方法,利用状态反馈,完全抵消了非线性因素对系统输出的影响,实现了系统输入－输出的严格线性关系和解耦控制,再利用线性系统方法,进行了系统的极点配置,使系统具有希望的跟踪性能,最后提出有待于深入研究的内容。     

基于反馈解耦法的旋转火箭弹自适应控制系统设计

火箭弹弹体的旋转将引起俯仰和偏航通道的强耦合,而且在飞行过程中,导弹的动力学参数在不断变化。这些因素加大了旋转火箭弹的控制系统设计难度。采用反馈方法实现了俯仰、偏航两通道的动态解耦,并利用模型参考自适应控制方法完成了两通道的控制系统设计。研究发现,所提方法很好地克服了耦合因素对控制系统的影响,提高了控制系统对动力学参数变化和外界干扰的鲁棒性,满足旋转火箭弹飞行控制系统设计要求。     

基于反馈线性化的EMS型磁浮列车非线性悬浮控制器设计

悬浮系统控制技术是EMS型磁浮列车的关键技术之一。针对悬浮系统的非线性特征,设计了一种非线性悬浮控制器。在合理假设的基础上,建立了EMS型磁浮列车悬浮系统的非线性数学模型;通过反馈线性化将该非线性模型精确线性化,得到等价的线性模型,采用状态反馈的方法设计了非线性控制器。仿真结果表明,该非线性控制器的控制性能明显优于传统的局部近似线性化方法设计的控制器,对间隙干扰和负载干扰具有鲁棒性。     

Signal‐to‐interference‐plus‐noise ratio estimation for wireless communication systems: Methods and analysis

The Signal‐to‐Interference‐plus‐Noise Ratio (SINR) is an important metric of wireless communication link quality. SINR estimates have several important applications. These include optimizing the transmit power level for a target quality of service, assisting with handoff decisions and dynamically adapting the data rate for wireless Internet applications. Accurate SINR estimation provides for both a more efficient system and a higher user‐perceived quality of service. In this paper, we develop new SINR estimators and compare their mean squared error (MSE) performance. We show that our new estimators dominate estimators that have previously appeared in the literature with respect to MSE. The sequence of transmitted bits in wireless communication systems consists of both pilot bits (which are known both to the transmitter and receiver) and user bits (which are known only by the transmitter). The SINR estimators we consider alternatively depend exclusively on pilot bits, exclusively on user bits, or simultaneously use both pilot and user bits. In addition, we consider estimators that utilize smoothing and feedback mechanisms. Smoothed estimators are motivated by the fact that the interference component of the SINR changes relatively slowly with time, typically with the addition or departure of a user to the system. Feedback estimators are motivated by the fact that receivers typically decode bits correctly with a very high probability, and therefore user bits can be thought of as quasipilot bits. For each estimator discussed, we derive an exact or approximate formula for its MSE. Satterthwaite approximations, noncentral F distributions (singly and doubly) and distribution theory of quadratic forms are the key statistical tools used in developing the MSE formulas. In the case of approximate MSE formulas, we validate their accuracy using simulation techniques. The approximate MSE formulas, of interest in their own right for comparing the quality of the estimators, are also used for optimally combining estimators. In particular, we derive optimal weights for linearly combining an estimator based on pilot bits with an estimator based on user bits. The optimal weights depend on the MSE of the two estimators being combined, and thus the accurate approximate MSE formulas can conveniently be used. The optimal weights also depend on the unknown SINR, and therefore need to be estimated in order to construct a useable combined estimator. The impact on the MSE of the combined estimator due to estimating the weights is examined. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004