乙烯简化化学动力学模型在HIFiRE燃烧室计算中的应用

为评估化学反应动力学模型对超燃冲压发动机计算结果的影响,首先利用基于"准稳态"假设方法建立了复杂化学反应动力学模型简化软件包(SPARCK),从详细模型出发得到了一个包含20组分16步总包反应的乙烯简化动力学模型.然后采用该简化模型和Princeton大学的简化模型对乙烯点火延迟时间进行了计算,对比显示两个简化模型计算结果基本一致,与实验测量结果均吻合较好,都能准确反映乙烯点火特性.但对HIFiRE燃烧室直连式实验的数值模拟结果显示两个模型计算得到的燃烧流场静温分布差别较大,内流道推力性能相差近12%.相比于Princeton大学的简化模型,SPARCK软件得到的简化模型计算的壁面压力分布与实验结果吻合更好,能够准确描述燃烧室流场现象.     

基于交互式多模型的飞机编队相对导航算法研究

在飞机编队飞行时,成员间的相对位置信息是实现系统协同作战的重要保证,为了提高机群编队飞行的相对导航定位精度,在无地面基准的机群编队飞行JTIDS/GPS/TACAN/IFDL组合的相对导航系统中,采用交互式多模型扩展卡尔曼滤波(IMM-EKF)算法,设计实现了多传感器相对导航系统,克服了飞机动态模型参数变化导致使用单一动态模型滤波精度下降的问题。仿真分析结果表明,交互式多模型算法可以提高相对导航系统的定位精度和可靠性,特别在GPS可见卫星很少的情况下,依然能够具有良好的定位性能。     

弱模型依赖通用智能姿态控制技术

超高速跨域飞行、敏捷机动等是新一代飞行器发展方向,而长时高速飞行产生的气动外形变化带来的气动参数大范围改变等问题,都对控制系统设计提出了更高的要求。为提高飞行器对模型不确定性的适应能力及控制方法对不同外形、复合执行机构的通用性,深入研究了弱模型依赖的通用智能姿态控制技术,分层次地开展了基于深度学习(DL)的自适应姿态控制、基于深度确定性策略梯度算法(DDPG)的通用姿态控制、弱模型依赖的多维复合控制等技术研究,显著提高了控制系统的鲁棒性和通用性,对人工智能技术在飞行器姿态控制中的应用具有一定的指导意义。     

飞机增稳系统的耗散控制问题研究

讨论了飞机增稳系统的耗散控制问题．首先将增稳系统的运动方程简化为定常线性系统;其次,研究系统的耗散性分析;再次,设计状态反馈控制器使得系统的闭环系统耗散;最后,通过数值仿真验证了方法的可行性。     

基于鲁棒扩展卡尔曼滤波的双目视觉测量相对导航技术研究

针对非合作目标相对导航问题,为提高相对导航的精度和可靠性,采用双目视觉测量方法实现追踪器与目标器之间相对状态的测量,并基于鲁棒扩展卡尔曼滤波方法,提出了一种鲁棒相对导航滤波方法。仿真结果表明,该方法对相对导航系统模型中的不确定性具有良好的鲁棒性,且滤波精度较高。     

视觉辅助的无人机自主空中加油建模与仿真

为准确获得插头锥套式空中加油过程中无人机与锥套的相对位姿信息,提出了机器视觉辅助的插头锥套式无人机自主空中加油仿真方案,开发了基于Open CV,Simulink及Vega Prime视景仿真技术的闭环仿真系统.研究了机器视觉识别跟踪锥套的图像处理算法,利用Kalman滤波算法估计无人机与锥套的相对位姿.仿真结果表明,机器视觉算法可以准确识别跟踪锥套,滤波器状态估计的收敛速度较快,无人机姿态角变化范围较小,保证了插头锥套式无人机自主空中加油的平稳安全对接.     

Dual smoothing ionospheric gradient monitoring algorithm for dual-frequency BDS GBAS

In this study, a Dual Smoothing Ionospheric Gradient Monitor Algorithm (DSIGMA) was developed for Code-Carrier Divergence (CCD) faults of dual-frequency Ground-Based Augmentation Systems (GBAS) based on the BeiDou Navigation Satellite System (BDS). Divergence-Free (DF) combinations of the signals were used to form test statistics for a dual-frequency DSIGMA. First, the single-frequency DSIGMA was reviewed, which supports the GBAS approach service type D (GAST-D) for protection against the effect of large ionospheric gradients. The single-frequency DSIGMA was used to create a novel input scheme for the dual-frequency DSIGMA by introducing DF combinations. The steady states of the test statistics were also analysed. The monitors were characterized using BDS measurement data, whereby standard deviations of 0.0432 and 0.0639 m for the proposed two test statistics were used to calculate the monitor threshold. An extensive simulation was designed to assess the monitor performance by comparing the Probability of Missed Detection (PMD) according to the differential error with the range domain PMD limits under different fault modes. The results showed that the proposed algorithm has a higher integrity performance than the single-frequency monitor. The minimum detectable divergence with the same missed probability is less than 50% that of GAST-D.     

一种用于同步静止卫星监测的微型VLBI网

针对地球静止轨道（GEO）卫星全天时全天候高精度的监测需求，考虑传统甚长基线干涉（VLBI）测站高成本、高投入和GEO卫星专用观测时段有限等制约条件，研发了简易型VLBI观测系统，并组建了包括上海、都匀和乌鲁木齐三站的微型VLBI网（micro VLBI network，MVN），开展了并置站测试以及对GEO卫星亚太6C的连续监测，并评估了当前MVN的观测能力。结果表明MVN扣除系统差后的单站接收精度为2ns，各基线观测时延拟后残差约几纳秒，GEO目标实测位置精度为百米级（内外符精度分别约100m和400m）。不同于传统VLBI和其他GEO监测手段，MVN还具备全天时、全天候、低造价、易布设及易推广等特点，充分表明了其在GEO卫星监测领域的应用价值。     

Adjustable box-wing model for solar radiation pressure impacting GPS satellites

One of the major uncertainty sources affecting Global Positioning System (GPS) satellite orbits is the direct solar radiation pressure. In this paper a new model for the solar radiation pressure on GPS satellites is presented that is based on a box-wing satellite model, and assumes nominal attitude. The box-wing model is based on the physical interaction between solar radiation and satellite surfaces, and can be adjusted to fit the GPS tracking data.     

Sensor-fault tolerant attitude determination using two-stage estimator

Satellite attitude determination accuracy significantly drops when sensor-fault occurs. Hence, a proper mitigation strategy to detect sensor-fault and accurately estimate corresponding fault magnitudes is mandatory for robust and accurate attitude determination. In this paper, a novel sensor-fault tolerant precise attitude estimator is proposed consisting of two stages. In the first stage, sensor-fault is detected, and the associated sensor parameter change is roughly estimated using an interacting multiple-model (IMM) approach. Subsequently, the second stage is triggered. The sensor parameter change is precisely estimated with a new sensor-parameter-augmented filter. This is defined as a selectively augmented extended Kalman filter (SAEKF) in this paper. The conventional augmented extended Kalman filter (AEKF) is computationally more expensive than the proposed SAEKF. The SAEKF augments only the sensor parameters affected by sensor-faults, not the full sensor parameters, into the state vector. This leads to a significant computational time-saving. A transition method from the first stage to the second stage is also investigated. Numerical simulation results demonstrate that the proposed two-stage approach has smaller attitude determination errors than the existing algorithms, ranged from 21.7% to 88.8%, in cases with gyro scale factor error or misalignment.