基于ReliefF-LMBP算法的涡轴发动机气路故障模式识别—两机专刊

针对涡轴发动机气路故障模式识别精度不高的问题,提出了一种基于ReliefF-LMBP故障特征提取的发动机故障模式识别方法。应用ReliefF算法对发动机传感器参数赋予权值,对传感器参数特征权重值进行迭代更新和排序,聚集好的特征样本,离散异类样本。根据筛选出的特征子集,利用LMBP神经网络算法进行发动机故障模式识别。以涡轴发动机为对象进行气路故障诊断验证,结果表明所提方法能提取特征传感器参数并实现有效的故障模式识别。     

基于神经网络和证据理论的火箭发动机故障诊断

针对运载火箭动力系统故障的复杂特性和故障模式的不确定性,提出了基于神经网络和证据理论的火箭发动机故障诊断。首先以火箭视加速度和角速度作为网络输入,故障类型矩阵作为网络输出,通过BP神经网络和RBF(径向基函数)神经网络进行故障诊断;之后通过D-S证据理论融合神经网络结果;最后通过滚动时域估计方法对火箭飞行状态特征量估计。仿真结果表明诊断准确率达到99%以上,表明提出的方法对于火箭发动机故障诊断具有较高的准确性和实用性。     

Neural network-based model predictive control with fuzzy-SQP optimization for direct thrust control of turbofan engine

A nonlinear model predictive control method based on fuzzy-Sequential Quadratic Programming(SQP) for direct thrust control is proposed in this paper for the sake of improving the accuracy of thrust control. The designed control system includes four parts, namely a predictive model, rolling optimization, online correction, and feedback correction. Considering the strong nonlinearity of engine, a predictive model is established by Back Propagation(BP) neural network for the entire flight envelope,...     

Robust attitude coordinated control for spacecraft formation with communication delays

In this paper,attitude coordinated tracking control algorithms for multiple spacecraft formation are investigated with consideration of parametric uncertainties,external disturbances,communication delays and actuator saturation.Initially,a sliding mode delay-dependent attitude coordinated controller is proposed under bounded external disturbances.However,neither inertia uncertainty nor actuator constraint has been taken into account.Then,a robust saturated delay dependent attitude coordinated control law is further derived,where uncertainties and external disturbances are handled by Chebyshev neural networks (CNN).In addition,command filter technique is introduced to facilitate the backstepping design procedure,through which actuator saturation problem is solved.Thus the spacecraft in the formation are able to track the reference attitude trajectory even in the presence of time-varying communication delays.Rigorous analysis is presented by using Lyapunov-Krasovskii approach to demonstrate the stability of the closed-loop system under both control algorithms.Finally,the numerical examples are carried out to illustrate the efficiency of the theoretical results.     

复合固体推进剂燃烧性能模拟计算的神经网络方法

在总结已有燃烧模型的基础上，重点考虑压强、氧化剂的重均粒径、氧化剂的质量浓度三种主要影响因素，提出了一种基于误差反传（ＢＰ）神经网络的复合固体推进剂燃烧性能模拟计算方法，计算结果和实验值吻合较好，这为推进剂配方的计算机辅助设计提供了一种新方法。     

基于神经网络的某导弹姿态控制系统故障诊断

介绍了多层前向神经网络及其算法 ,讨论了神经网络在故障诊断方面的应用 ,并对某导弹姿态控制系统的典型故障进行了神经网络建模 ,经测试取得了良好的效果。     

基于神经网络的参数-费用模型研究

讨论了采用用神经网络理论来建立参数－费用模型的方法，给出了几种地空导弹武器系统的特征参数与研制费用，对神经网络法与线性回归法所得的结果进行了比较。比较结果表明，神经网络法比线性回归法精确。     

Forecasting magnetopause crossing locations by using Neural Networks

Given the highly complex and nonlinear nature of Near Earth Space processes, mathematical modeling of these processes is usually difficult or impossible. In such cases, modeling methods involving Artificial Intelligence may be employed. We demonstrate that data driven models, such as the Neural Network based approach, shows promise in its ability to forecast or predict the behavior of these processes. In this paper, modeling studies for forecasting magnetopause crossing locations are summarized and a Neural Network algorithm is presented to describe the nonlinear time-dependent response of the subsolar region of the magnetopause to varying solar wind conditions. In our approach the past history of the solar wind has, for the first time to the best knowledge of the authors, been included in forecasting the subsolar region of the magnetopause. It is proposed that the data driven approach is a valid approach to understanding and modeling the physical phenomena of Near Earth Space. The only basic requirement for the data driven approach is the availability of representative data for the phenomena. The objective of this paper is to demonstrate that by using WIND and GEOTAIL satellite data a Neural Network based model can be adapted to the modeling of the Earth’s magnetopause.     

Automatic landing system using neural networks and radio-technical subsystems

The paper focuses on the design of a new automatic landing system(ALS) in longitudinal plane; the new ALS controls the aircraft trajectory and longitudinal velocity. Aircraft control is achieved by means of a proportional-integral(PI) controller and the instrumental landing system– the first phase of landing(the glide slope) and a proportional-integral-derivative(PID) controller together with a radio-altimeter – the second phase of landing(the flare); both controllers modify the reference model associated with aircraft pitch angle. The control of the pitch angle and longitudinal velocity is performed by a neural network adaptive control system, based on the dynamic inversion concept, having the following as components: a linear dynamic compensator, a linear observer, reference models, and a Pseudo control hedging(PCH) block. The theoretical results are software implemented and validated by complex numerical simulations; compared with other ALSs having the same radio-technical subsystems but with conventional or fuzzy controllers for the control of aircraft pitch angle and longitudinal velocity, the architecture designed in this paper is characterized by much smaller overshoots and stationary errors.     

Prediction of ionospheric total electron content using adaptive neural network with in-situ learning algorithm

The Ionospheric Total Electron Content is responsible for the group delay of the signals from the Navigation satellites. This delay causes ranging error, which in turn degrades the accuracy of position estimated by the receivers. For critical applications, single frequency receivers resort to Satellite Based Augmentation Systems in order to have improved accuracy and integrity. The performance of these systems in terms of accuracy can be improved if predictions of the delays are available simultaneously with real measurements. This paper attempts to predict the Total Electron Content using adaptive recurrent Neural Network at three different locations of India. These locations are selected at the magnetic equator, at the equatorial anomaly crest and outside the anomaly range, respectively. In-situ Learning Algorithm has been used for tracking the non-stationary nature of the variation. Prediction is done for different prediction intervals. It is observed that, for each case, the mean and root mean square values of prediction errors remain small enough for all practical applications. Analysis of Variance is also done on the results.