无人作战飞机空中加油建模与近距机动控制律设计

针对无人作战飞机(UCAV)空中加油时存在加油机尾流干扰导致相对位置难以保持的问题,基于等效气动效应法建立了UCAV动力学模型,并设计了近距机动最优飞行控制律.将加油机尾流对UCAV的影响等效为平均风速度和风梯度,计算出附加于UCAV的诱导力系数和诱导力矩系数,建立了含尾流扰动的受油机模型.以位置跟踪误差的积分为增广状态,基于加权二次型性能指标设计了近距机动最优飞行控制律.仿真结果表明,UCAV模型能真实地体现尾流对受油机的影响,并通过设计的近距机动控制律可有效实现对加油机相对位置的精确跟踪.     

基于SBT和OOTPN的机务准备流程柔性建模研究

提出基于服务蓝图和OOTPN的服务流程的柔性建模方法,建立面向对象petri网的模块化结构,给出支持业务流程动态变化的柔性建模步骤.最后,结合机务准备建模实例,论证方法的有效性.     

通断式多态系统扩展测试性建模方法

现有测试性建模方法有一定的约束条件,包括故障判据的一致性、故障传递关系的一致性、测试的逻辑值判据一致性.针对通断式多态系统具有的状态差异性的特点,提出了一种扩展测试性建模方法.在引入状态集合的基础上,对故障、测试、 D 矩阵和诊断树进行了扩展定义,最后定义了扩展测试性模型.介绍了扩展测试性建模分析的算法,主要有模型建立算法、扩展 D 矩阵生成算法、扩展诊断树生成算法.以某液压操纵系统为例进行了应用,结果验证了方法的可行性和有效性.     

基于拉格朗日方程的倾转旋翼机动力学建模

针对倾转短舱与机体间的气动干扰和惯性耦合特征,基于拉格朗日原理推导出了倾转旋翼机的纵向飞行动力学方程。在此基础上,以XV-15倾转旋翼机为对象,针对几种不同的短舱倾角,计算分析了飞行器的配平特性和过渡模式动态响应特性。仿真结果表明,所建立的动力学模型是合理的。该模型可用于研究倾转旋翼机的直升机飞行模式、飞机飞行模式及过渡飞行模式下的纵向飞行特性。     

干涉型光纤陀螺温度漂移建模与实时补偿

 分析了干涉型光纤陀螺(IFOG)温度漂移的影响因素,通过建立温度与陀螺模型参数之间的线性相关关系,推导出了光纤陀螺温度漂移的分布模型。为了提高陀螺温度漂移模型的适应性,设计了0.25,0.75,1.00 ℃/min三种变温速率的温度试验,并利用小波变换的方法分离数据中的噪声和趋势项。基于温度漂移的分布模型和最小二乘误差（LSE）准则,建立了多参量联合线性模型来估计陀螺温度漂移。采用升温过程和降温过程分段建模的方法将多参量联合线性模型算法进行了简化,并在现场可编程逻辑阵列（FPGA）中分时复用一个全串行有限冲激响应（FIR）滤波器实现了陀螺温度漂移的实时估计。仿真的温度补偿结果及实时的温度补偿结果显示,该方法可以使陀螺温度漂移减小到1/10~2/10。     

SINS制导工具误差补偿研究

 针对现有SINS制导工具误差模型不能满足“天地一致性”的问题,提出了基于实际弹道数据的SINS制导工具误差补偿方法,即首先建立SINS制导工具误差模型,然后采用改进的特征值有偏估计方法(LRE)估计具有复共线性特性的误差模型,最后利用该误差模型对捷联惯性仪表进行误差补偿。半物理仿真试验的结果表明,高度通道的最大测量误差由补偿前的52米降低到补偿后的8米,其余通道的补偿效果类似。这证明了该误差模型的正确性和估计方法的有效性,为将来SINS制导工具误差补偿和提高SINS制导精度提供了充分的依据。     

火星巡视器动力学建模研究与仿真平台实现

火星巡视器是执行复杂的火星表面探测任务的重要载体,为保证巡视器的可靠性,本文对火星巡视器动力学原理进行了研究.首先,分析了巡视器的结构组成,介绍了巡视器动力学建模的相关理论基础.然后根据巡视器有向Span树的轴链系统,基于拉格朗日递归动力学分析方法,得到了多轴系统拉格朗日方程,通过遍历巡视器轴链拓扑完成了巡视器动力学模...     

Dynamics modeling and pressure control of composites tape winding system based on LQSMC

Pressure fluctuations during the composite fiber winding process seriously affect the product's compactness strength, fatigue resistance, stress uniformity, and resin content. The accuracy of pressure control systems is affected by nonlinear disturbances, such as friction, parameter perturbation, and measurement noise. A robust control algorithm based on linear quadratic optimal control and sliding mode control (LQSMC) is proposed to overcome these problems. The method is based on the system state space expression and linear quadratic optimal control. The state space model of the system is improved by using a Kalman filter and control input for state estimation, and a new sliding surface equation is defined. The ameliorated control algorithm exhibits good performance and can effectively suppress sliding mode control (SMC) chattering. Simulation and experimental results show that LQSMC offers high control precision, much stronger anti-interference, and robustness, which can effectively improve the positioning and tracking accuracy of a pressure control system compared with linear quadratic optimal control (LQC). The winding pressure control precision is improved by 45% to 50%. The results show that the porosity of composite fiber tape winding products decreased thanks to our method.     

Modeling satellite battery aging for an operational satellite simulator

During the satellite’s operations, simulation tools perform an important role in ensuring the space mission success. In this sense, the models implemented in the context of an operational satellite simulator that enables analysis of health status and maintenance during operations shall reflect the current satellite behavior with high fidelity. Moreover, it is complicated to obtain all analytical models of a satellite’s disciplines, considering its aging. This paper proposes an Artificial Neural Network (ANN) to reproduce the battery voltage behavior of a large sun-synchronous remote sensing satellite, the CBERS-4, currently in operation. Using the genetic algorithm to find the best network architecture of ANN, the neural model for this application presented an error of less than 1%, demonstrating its feasibility to obtain a high fidelity model for an operational simulator enabling extend analyses. The paper addresses advanced techniques aligned with the space industry’s future, increasing the ability to analyze a large amount of data and improve the space system’s operation.