基于线性重力差模型的拦截弹中制导技术

针对机载动能拦截弹的中制导段气动力作用可忽略且存在无法实时精确计算重力偏差等特点，提出了一种基于线性重力差模型的预测制导方法。通过线性模型逼近中制导过程中拦截弹与目标之间的重力偏差，以此为依据调整拦截弹的推力矢量并将重力偏差值消除。在此基础上采用延迟点火的策略使得拦截弹的拦截器助推级更好地修正预测模型与实际情况的偏差，经过仿真验证可以将整个中制导段的脱靶量限制在一个较小数值，从而为末制导段提供良好的初始条件。     

Probabilistic resident space object detection using archival THEMIS fluxgate magnetometer data

Recent progress in the detection of small space objects, at geosynchronous altitudes, through ground-based optical and radar measurements is demonstrated as a viable method. However, in general, these methods are limited to detection of objects greater than 10?cm. This paper examines the use of magnetometers to detect plausible flyby encounters with charged space objects using a matched filter signal existence binary hypothesis test approach. Relevant data-set processing and reduction of archival fluxgate magnetometer data from the NASA THEMIS mission is discussed in detail. Using the proposed methodology and a false alarm rate of 10%, 285 plausible detections with probability of detection greater than 80% are claimed and several are reviewed in detail.     

Real-time sensor fault detection and isolation for LEO satellite attitude estimation through magnetometer data

Attitude estimation is a critical component of the Attitude Determination and Control System (ADCS) of any satellite. It is used to convert the sensor observation data to an estimated attitude using filtering algorithms. However, in the presence of sensor faults, the ADCS fails to achieve the desired attitude accuracy. In this paper, the Fault Tolerant Extended Kalman Filter (FTEKF) is proposed to handle this imperfection. In accordance, various filtering steps are included in the FTEKF design to enhance both attitude estimation and sensor fault detection. The developed algorithm can detect and isolate any unexpected sensor faults in real time, which provides a reliable attitude estimation. A comparative study with the classical and robust Kalman filters is performed through numerical simulations in order to validate the effectiveness of the adopted filter in case of magnetometer fault data.     

UKF参数估计在航天器气动辅助变轨问题中的应用

为快速精确地求解气动辅助变轨问题，提出一种基于无损卡尔曼滤波(UKF)参数估计的数值求解方法。首先，针对气动辅助变轨问题，利用极大值原理将其转化为对应的两点边值问题;然后，以协态变量的初值作为估计参数，以末端条件为期望观测值，将该两点边值问题转化为参数估计问题，并应用UKF滤波算法求解。该算法基于估计理论，避免了计算传统数值方法所需要的梯度矩阵，同时克服了猜测协态变量初值的困难，降低了求解气动辅助变轨问题的难度。数值仿真表明，该算法结构简单，求解效率高，具有良好的鲁棒性。     

永磁同步电机转速滑模和电流预测组合式控制器运行性能分析

针对永磁同步电机（PMSM）矢量控制系统的速度环采用PI调节器不能很好地适应电机参数变化而导致控制效果不理想的问题，在电流预测控制 (CPC)增强抗干扰能力、提高鲁棒性的基础上，提出了一种滑模控制方法.该滑模与电流预测相结合的控制方法改善了永磁同步电机矢量控制系统的动态性能，实现了负载扰动时调速响应快且无超调控制，提高了系统对内部电阻变化的容忍度.仿真结果表明，采用该方法，系统的响应速度和稳定性均得到改善，转速响应从启动到稳定所用时间减小60%.     

Satellite lithium-ion battery remaining useful life estimation with an iterative updated RVM fused with the KF algorithm

Lithium-ion batteries have become the third-generation space batteries and are widely utilized in a series of spacecraft. Remaining Useful Life (RUL) estimation is essential to a spacecraft as the battery is a critical part and determines the lifetime and reliability. The Relevance Vector Machine (RVM) is a data-driven algorithm used to estimate a battery’s RUL due to its sparse feature and uncertainty management capability. Especially, some of the regressive cases indicate that the RVM can obtain a better short-term prediction performance rather than long-term prediction. As a nonlinear kernel learning algorithm, the coefficient matrix and relevance vectors are fixed once the RVM training is conducted. Moreover, the RVM can be simply influenced by the noise with the training data. Thus, this work proposes an iterative updated approach to improve the long-term prediction performance for a battery’s RUL prediction. Firstly, when a new estimator is output by the RVM, the Kalman filter is applied to optimize this estimator with a physical degradation model. Then, this optimized estimator is added into the training set as an on-line sample, the RVM model is re-trained, and the coefficient matrix and relevance vectors can be dynamically adjusted to make next iterative prediction. Experimental results with a commercial battery test data set and a satellite battery data set both indicate that the proposed method can achieve a better performance for RUL estimation.     

A multi-rate sensor fusion approach using information filters for estimating aero-engine performance degradation

Gas-path performance estimation plays an important role in aero-engine health management, and Kalman Filter(KF) is a well-known technique to estimate performance degradation. In previous studies, it is assumed that different kinds of sensors are with the same sampling rate, and they are used for state estimation by the KF simultaneously. However, it is hard to achieve state estimation using various kinds of sensor measurements at the same sampling rate due to a complex network and physical characteristic differences between sensors, especially in an advanced multisensor architecture. For this purpose, a multi-rate sensor fusion using the information filtering approach is proposed based on the square-root cubature rule, which is called Multi-rate Squareroot Cubature Information Filter(MSCIF) to track engine performance degradation. Soft measurement synchronization of the MSCIF is designed to provide a sensor fusion condition for multiple sampling rates of measurement, and a fault sensor is isolated by maximum likelihood validation before state estimation. The contribution of this paper is to supply a novel multi-rate informationfilter approach for sensor fault tolerant health estimation of an aero-engine in a multi-sensor system. Tests are conducted for aero-engine performance degradation estimation with multiple sampling rates of sensor measurement on both digital simulation and semi-physical experiment.Experimental results illustrate the superiority of the proposed algorithm in terms of degradation estimation accuracy and robustness to sensor failure in a multi-sensor system.     

五桥臂双永磁同步电机系统优化模型预测控制

五桥臂逆变器传统模型预测控制一个控制周期内由单矢量作用,降低了2台电机的运行性能。针对该问题,提出了优化模型预测控制策略。通过对2台电机有效电压矢量和零电压矢量的占空比进行整体优化分配,减小电机的转矩波动,提高2台电机的控制精度,进而在满足2台三相电机控制独立性的情况下,提升了电机系统的控制性能。仿真结果验证了所提优化模型预测控制的正确性和有效性。     

滤波器群时延对二元翼段阵风减缓的影响

针对二元翼段阵风载荷减缓主动控制系统,研究了数字滤波器群时延对阵风减缓效率的影响。首先建立了气动弹性模型,并将时滞系统转化为等价的无时滞系统。随后设计了阵风发生器、数字滤波器及时滞线性二次型(Linear quadratic,LQ)控制器等组成控制系统。最后对系统进行数值仿真并开展风洞实验加以验证。研究发现,当滤波器群时延量较小时可以改善控制系统性能,提高阵风载荷减缓效率,但随着滤波器群时延量增大,阵风载荷减缓效率降低甚至出现控制器失效的情况。     

涡扇发动机部件特性的滤波自动修正更新方法

针对航空发动机部件制造装配以及性能蜕化引起的平均模型与个体发动机之间的性能不匹配问题,提出一种基于非线性滤波算法的发动机部件特性自动修正方法。根据发动机部件级平均模型输出与个体量测数据的残差,利用数据处理策略结合无迹卡尔曼滤波算法的不可测部件特性变化估计,自动更新发动机部件特性,建立发动机个体物理模型。通过小涵道比涡扇发动机仿真验证,结果表明该方法可自动修正发动机部件特性,相比较平均模型,通过该方法修正的发动机个体模型中各截面温度、压力计算偏差均在0.5%以内,有效提高涡扇发动机个体物理模型稳态、动态精度。