Robust model predictive control for satellite formation keeping with eccentricity/inclination vector separation

This study presents model predictive formation control based on an eccentricity/inclination vector separation strategy. Alternative collision avoidance can be accomplished by using eccentricity/inclination vectors and adding a simple goal function term for optimization process. Real-time control is also achievable with model predictive controller based on convex formulation. Constraint-tightening approach is address as well improve robustness of the controller, and simulation results are presented to verify performance enhancement for the proposed approach.     

基于室内无线模型的穿墙损耗校正

K-M（Keenan-Motley）模型将单墙固定损耗值相加来计算室内无线信号穿透多墙的总损耗值，存在较大误差。针对该问题选取多种室内场景分别进行连续波（Continuous Wave，CW）测试，对无线信号穿墙损耗的影响因素和变化规律进行分析，提出了一种基于人工神经网络的室内无线模型穿墙损耗校正方法，对预处理后的测试数据进行训练并建立预测模型。经验证该预测模型符合校正判别准则，在实际场景下具有良好的预测准确度。     

Predictive fault-tolerant control of an all-thruster satellite in 6-DOF motion via neural network model updating

The problem of controlling an all-thruster spacecraft in the coupled translational-rotational motion in presence of actuators fault and/or failure is investigated in this paper. The nonlinear model predictive control approach is used because of its ability to predict the future behavior of the system. The fault/failure of the thrusters changes the mapping between the commanded forces to the thrusters and actual force/torque generated by the thruster system. Thus, the basic six degree-of-freedom kinetic equations are separated from this mapping and a set of neural networks are trained off-line to learn the kinetic equations. Then, two neural networks are attached to these trained networks in order to learn the thruster commands to force/torque mappings on-line. Different off-nominal conditions are modeled so that neural networks can detect any failure and fault, including scale factor and misalignment of thrusters. A simple model of the spacecraft relative motion is used in MPC to decrease the computational burden. However, a precise model by the means of orbit propagation including different types of perturbation is utilized to evaluate the usefulness of the proposed approach in actual conditions. The numerical simulation shows that this method can successfully control the all-thruster spacecraft with ON-OFF thrusters in different combinations of thruster fault and/or failure.     

基于新陈代谢GM（1,1）模型的导弹遥测故障预测算法

在人工监视遥测关键参数是否超出阈值范围以判断导弹故障的传统模式基础上,对GM（1,1）预测模型进行了改进,研究了基于新陈代谢的GM（1,1）预测算法对遥测参数实时预测效果.该算法能不断去掉最老的信息,充分利用最新的信息,避免了预测模型老化的现象;由于用于预测的数列维度较少,便于计算,能够保证算法的实时性.用MATLAB编程实现算法,比对实测数据的预测值与实际值,并进行误差分析.结果表明,该算法对遥测参数的预测精度较高,可为及时发现导弹故障提供科学的依据.     

2.5维编织树脂基复合材料平板多尺度动力学模型修正

在2.5维编织树脂基复合材料多尺度固有振动分析的基础上,开展基于实测结构模态数据的复合材料平板动力学模型修正研究,以获取精准反映其动力学特性的模型。首先,建立复合材料平板固有频率对弹性参数的灵敏度分析方法,分析了平板固有振动特性的主要影响因素;其次,建立基于灵敏度分析的复合材料多尺度动力学模型修正方法,形成了基于MSC.NASTRAN平台的模型修正程序。最后,利用实测结构模态测试数据,实现对复合材料平板动力学模型的修正。结果表明,对关键弹性参数进行修正后,不同边界条件和纱线走向的复合材料平板试验件固有频率实测数据与仿真数据的最大误差由10.44%下降至2.39%,相关性得到了大幅改善。此外,所提出的自由模态测试方案结合动力学模型修正方法,为复合材料等效弹性参数的试验辨识提供了新的技术途径。     

基于控制分配的恒压腔压力精准控制

针对采用双阀调节的恒压腔系统压力在空气流量大范围变化时的精确控制问题,提出了一种基于控制分配的恒压腔压力精准控制方法。首先,建立了虚拟放气流量的双阀控制分配算法,包括：建立满足虚拟放气流量要求且调节阀能耗最小的优化问题;通过线性矩阵不等式(Linear Matrix Inequality, LMI)求解该优化问题得到双阀实际流通面积值;考虑调节阀动态并计算调节阀控制信号指令值。其次,建立以虚拟放气流量为恒压腔控制输入的闭环负反馈回路,基于此,设计满足伺服性能和抗干扰性能要求的PI控制器,引入上述双阀控制分配算法,进而构建完整的基于控制分配的恒压腔压力控制系统。仿真结果表明,采用该方法的控制系统性能明显优于传统单阀PI控制系统性能,恒压腔压力动态相对误差小于0.07%;干扰流量最大变化率为77kg/s2时,压力最大偏差低于500Pa;此外,调节阀动态时间常数和流量系数的拉偏仿真结果进一步验证了该控制器的鲁棒性。     

基于自适应差分进化算法的变循环发动机模型求解方法研究

为了降低传统迭代算法在求解变循环发动机非线性模型时对初值的依赖性,将模型的求解问题转换为求最小值的优化问题,引入差分进化算法进行模型的求解,并提出一种自适应差分进化算法(ADE)。ADE借助轮盘赌选择法,利用种群的进化经验可以自适应的选择最适合当前种群的差分策略与算法控制参数。针对变循环发动机四个典型工作点的模型求解问题,研究了标准差分进化算法(SDE)的控制参数对其性能的影响,获取了SDE在求解四个典型工作点时的最优控制参数组合,对比分析了ADE与SDE的性能差异,最后研究了种群规模对ADE性能的影响。结果表明：SDE在求解发动机模型时具有较好的鲁棒性,在求解不同工作点时算法的最优控制参数并不完全相同;相比于使用最优控制参数的SDE,ADE可以在不影响算法鲁棒性的情况下提升效率50%以上;减少ADE的种群规模会在提升算法效率的同时破坏鲁棒性。     

面向非结构混合网格高精度阻力预测的梯度求解方法

针对非结构混合网格的特点,通过改进传统的Green-Gauss梯度求解方法,提出了一种可提高非结构混合网格黏性计算精度的节点型Green-Gauss梯度求解方法。利用改进后的方法,完成了DLR-F4翼身组合体算例的计算和对比分析。改进后的梯度求解方法残差收敛更好,下降量级更多,阻力系数和试验吻合更好,激波区域压力分布和分离区域流场细节的模拟更精确,说明改进后的梯度求解方法有效提高了程序的鲁棒性和阻力预测精度,验证了方法的有效性。采用改进后的方法对第5届AIAA阻力预测研讨会的通用研究模型（CRM）进行了详细的模拟分析,结果表明：改进的梯度求解方法更加适用于非结构混合网格的黏性计算,计算精准度达到国际同类CFD软件水平,进一步验证了改进方法的可靠性。     

阵列多波束雷达通道失配对旁瓣对消性能的影响

分析了多波束雷达通道失配对旁瓣对消的影响,并结合某雷达的试验现象,运用仿真方法分析了该雷达在假目标干扰条件下通道失配对旁瓣对消性能的影响,为该体制雷达的对消效果评估提供了依据。     

Dynamic parametric modeling-based model updating strategy of aeroengine casings

For accurate Finite Element (FE) modeling for the structural dynamics of aeroengine casings, Parametric Modeling-based Model Updating Strategy (PM-MUS) is proposed based on efficient FE parametric modeling and model updating techniques regarding uncorrelated/correlated mode shapes. Casings structure is parametrically modeled by simplifying initial structural FE model and equivalently simulating mechanical characteristics. Uncorrelated modes between FE model and experiment are reasonably handled by adopting an objective function to recognize correct correlated modes pairs. The parametrized FE model is updated to effectively describe structural dynamic characteristics in respect of testing data. The model updating technology is firstly validated by the detailed FE model updating of one fixed–fixed beam structure in light of correlated/uncorrelated mode shapes and measured mode data. The PM-MUS is applied to the FE parametrized model updating of an aeroengine stator system (casings) which is constructed by the proposed parametric modeling approach. As revealed in this study, (A) the updated models by the proposed updating strategy and dynamic test data is accurate, and (B) the uncorrelated modes like close modes can be effectively handled and precisely identify the FE model mode associated the corresponding experimental mode, and (C) parametric modeling can enhance the dynamic modeling updating of complex structure in the accuracy of mode matching. The efforts of this study provide an efficient dynamic model updating strategy (PM-MUS) for aeroengine casings by parametric modeling and experimental test data regarding uncorrelated modes.