Neural Network aided PMSM multi-objective design and optimization for more-electric aircraft applications

This study uses the Neural Network(NN) technique to optimize design of surfacemounted Permanent Magnet Synchronous Motors(PMSMs) for More-Electric Aircraft(MEA)applications. The key role of NN is to provide dedicated correction factors for the analytical PMSM mass and loss estimation within the entire design space. Based on that, a globally optimal design can be quickly obtained. Matching the analytical estimation with Finite-Element Analysis(FEA) is the main research target of training the NN. ...     

ROBUST ADAPTIVE BANK-TO-TURN MISSILE AUTOPILOT DESIGN USING FUZZY CMAC NEURAL NETWORKS

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ENGINE SENSOR FAULT DIAGNOSIS USING MAIN AND DECENTRALIZED NEURAL NET WORKS

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Sensor validation using hardware-based on-line learning neuralnetworks

The objective of this document Is to show the capabilities of parallel hardware-based on-line learning neural networks (NNs). This specific application is related to an on-line estimation problem for sensor validation purposes. Neural-network-based microprocessors are starting to be commercially available. However, most of them feature a learning performed with the classic back-propagation algorithm (BPA). To overcome this lack of flexibility a customized motherboard with transputers was implemented for this investigation, The extended BPA (EBPA), a modified and more effective BPA, was used for the on-line learning, These parallel hardware-based neural architectures were used to implement a sensor failure detection, identification, and accommodation scheme in the model of a night control system assumed to be without physical redundancy in the sensory capabilities. The results of this study demonstrate the potential for these neural schemes for implementation in actual flight control systems of modern high performance aircraft, taking advantage of the characteristics of the extended back-propagation along with the parallel computation capabilities of NN customized hardware     

An online data driven actor-critic-disturbance guidance law for missile-target interception with input constraints

In this article, we develop an online robust actor-critic-disturbance guidance law for a missile-target interception system with limited normal acceleration capability. Firstly, the missiletarget engagement is formulated as a zero-sum pursuit-evasion game problem. The key is to seek the saddle point solution of the Hamilton Jacobi Isaacs(HJI) equation, which is generally intractable due to the nonlinearity of the problem. Then, based on the universal approximation capability of Neural Networks(N...     

基于磁场定向控制理论的无刷直流电机控制

磁场定向控制(FOC)理论是永磁同步电机(PMSM)最常用的控制方法之一。该方法需要电机转子瞬时精确位置,因此限制了其在使用霍尔位置传感器的无刷直流电机(BLDCM)上的应用。为了能够在BLDCM上应用FOC理论,又不增加电机成本,在现有霍尔位置传感器的基础上,提出了一种根据霍尔传感器的输出信号,通过计算前一个扇区平均转速的方法估计转子精确位置。使用MATLAB/Simulink软件对该估算方法进行了仿真,并搭建了以TI 的TMS320F28069 DSP为核心的试验系统,通过自动代码生成技术完成算法的编程,可以较好地应用于BLDCM的控制,在一些应用场合可以代替PMSM从而降低使用成本。     

失效卫星姿态接管的并行学习合作博弈控制

针对多颗微小卫星合作接管失效卫星姿态运动的问题,研究了考虑微小卫星控制约束的多星合作博弈策略学习与协同控制方法。首先,建立了微小卫星合作博弈模型,给出了能够处理微小卫星控制约束的多星合作博弈帕累托最优策略显式表达式。其次,针对微小卫星合作博弈策略学习需求,通过过去与当前时刻数据的并行使用,设计了基于并行学习的策略迭代方法,该方法放松了神经网络（NN）权值矢量学习对持续激励条件的要求。给出了为确保神经网络权值矢量估值收敛,所使用的过去时刻数据所需满足的条件,并通过Lyapunov方法分析了神经网络权值矢量估计误差的一致最终有界性。之后,采用并行学习策略迭代方法进行了微小卫星合作博弈帕累托最优策略数值解的逼近。所获得的合作博弈策略具有反馈控制形式,在进行神经网络权值矢量学习后,各微小卫星能够通过合作博弈策略的独立计算实现失效卫星姿态运动接管过程中的闭环协同控制。所设计方法避免了传统姿态控制方法所需进行的力矩分配,消除了微小卫星数量对其控制计算复杂度的影响。最后,通过数值仿真对所设计方法的有效性进行了验证。     

Disturbance estimation and compensation in linear systems

Discrete-time estimation and compensation are discussed as a solution to the problems encountered when disturbances are present and degrade the performance of continuous automatic control systems. The method described, under a mild set of conditions, allows the designer to locate the closed-loop system poles substantially anywhere and provide disturbance rejection as large as desired by increasing the dimensions of the disturbance estimator. This estimation/compensation scheme results in a gain characteristic, below some frequency (ω_c ), with a slope of q×20 dB/decade. The value of q and ω_c can be chosen by the designer, within the physical limitations of the problem, so that the system error resulting from either deterministic or stochastic disturbances is sufficiently reduced. The method has been investigated for use with known linear, time-invariant systems     

俯仰跟踪任务中的驾驶员神经网络模型辨识

 在飞机设计中,应用驾驶员数学模型预测飞机飞行品质是避免人机系统出现不良耦合的重要途径之一。为了提高飞行品质的预测精度,采用人工神经网络(NN)方法进行驾驶员模型辨识,着重研究该模型对不同飞机被控对象的适应能力。首先,详细分析了驾驶员完成俯仰跟踪任务的操纵行为特点,提出适用于该驾驶员行为描述的神经网络模型结构形式。然后,根据对不同被控对象进行俯仰跟踪实时仿真实验的结果,对神经网络模型参数进行识别。最后,对模型辨识结果进行了精度评价。研究结果表明,该辨识方法适用于研究具有不同增益、不同短周期振荡频率飞机被控对象的驾驶员操纵行为特性。     

Tracking in clutter with nearest neighbor filters: analysis andperformance

The measurement that is “closest” to the predicted target measurement is known as the “nearest neighbor” (NN) measurement in tracking. A common method currently in wide use for tracking in clutter is the so-called NN filter, which uses only the NN measurement as if it were the true one. The purpose of this work is two fold. First, the following theoretical results are derived: the a priori probabilities of all three data association events (updates with correct measurement, with incorrect measurement, and no update), the probability density functions (pdfs) of the NN measurement conditioned on the association events, and the one-step-ahead prediction of the matrix mean square error (MSE) conditioned on the association events. Secondly, a technique for prediction without recourse to expensive Monte Carlo simulations of the performance of tracking in clutter with the NN filter is presented. It can quantify the dynamic process of tracking divergence as well as the steady-state performance. The technique is a new development along the line of the recently developed general approach to the performance prediction of algorithm with both continuous and discrete uncertainties     

基于神经网络和D-S证据理论的发动机磨损故障融合诊断

运用了4种最常用的滑油分析技术——铁谱分析、光谱分析、颗粒计数分析及理化指标分析,同时结合发动机试车台监测数据,提出运用神经网络和D-S证据理论对发动机试车状态进行融合诊断的方法。首先依据各种分析方法的标准磨损界限值,将原始数据进行了预处理,统一转换成故障征兆的布尔值;其次,建立各子神经网络的拓扑结构,并依据专家经验建立各子系统的输入征兆与故障论域的映射关系,从而得到各子神经网络的训练样本,对各网络进行成功训练后,利用神经网络实现各子网络的诊断并得到中间诊断结果;然后,将每种方法的神经网络诊断结果作为对各种故障模式的基本概率分配值,利用D-S证据理论,实现对神经网络的诊断结果的融合,从而得到最终的融合诊断结果;最后,运用算例表明了本文方法的有效性。      

Maximum likelihood detection of unresolved moving targets

Maximum likelihood estimation and decision theory is applied to the problem of detecting unresolved moving targets with a particular emphasis on space surveillance applications. A general formulation is derived which accounts for fluctuating targets, extended optical point spread functions (PSFs), and long frame integration times such that target images can form streaks within a single image. Probability of false alarm and of detection curves are generated from field data and simulations and compared with theoretical predictions     

一种方向舵-螺旋桨联用的全翼式太阳能无人机横航向控制方法

以全翼式太阳能无人机（UAV）为研究对象,针对无副翼状态下横航向控制问题,提出了采用方向舵偏转和螺旋桨差动进行横航向控制的方法。首先,分析了两个螺旋桨条件下该类无人机横航向的稳定性与操纵性;然后,基于线性自抗扰控制（LADRC）理论,以方向舵偏和螺旋桨差动为控制输出,分别设计了滚转角控制器和偏航角控制器。最后,结合滚转角控制和偏航角控制的优缺点,在L1轨迹跟踪算法的基础上设计了方向舵和螺旋桨联用的直线轨迹跟踪器。仿真结果表明：所设计的控制器具有较好的控制性能、鲁棒性和抗风性。同时参数整定过程相对简单,并采用实际可测的物理量,为进一步工程应用提供参考。     

多目标跟踪的核粒子概率假设密度滤波算法

提出一种新的多目标跟踪算法：核粒子概率假设密度滤波算法(KP-PHDF)。算法的创新点在概率假设密度滤波算法(PHDF)的目标状态提取步骤,以粒子概率假设密度滤波算法为框架,并运用结合了mean-shift算法的核密度估计(KDE)理论进行概率假设密度(PHD)分布的二次估计、提取PHD峰值位置作为目标状态估计值。分析与多目标跟踪(MTT)仿真的结果表明,与现有序列蒙特卡罗概率假设密度滤波算法（SMC-PHDF）相比,在相同仿真条件下新算法的估计精度提高30.5%。     

In-Flight Alignment and Calibration of Inertial Measurement Units?Part I: General Formulation

This is the first part of a two-part paper which summarizes work pursued by the author in 1966 [1]. The paper describes the application of minimum-variance estimation techniques for in-flight alignment and calibration of an inertial measurement unit (IMU) relative to another IMU and/or some other reference. The first part formulates the problem, and the second part [2] reports numerical results and analyses. The approach taken is to cast the problem into the framework of Kalman-Bucy estimation theory, where velocity and position differences between the two IMU's are used as observations and the IMU parameters of interest become part of the state vector. Instrument quantization and computer roundoff errors are considered as measurement noise, and environmental induced random accelerations are considered as state noise. Typical applications of the technique presented might include the alignment and calibration of IMU's on aircraft carriers, the initialization of rockets or rocket airplanes which are launched from the wing of a mother ship, the alignment and calibration of IMU's which are only used in the latter phases of rocket flight, and for the initialization/updating of SST guidance systems.     

Status of CR-like lower bounds for nonlinear filtering

The motivation and mechanics of utilizing Cramer-Rao (CR) -type lower bounds to gauge the performance of filters being evaluated in nonlinear estimation applications such as in sonar, sonobuoy, and radar target tracking are reviewed. The status of several similar alternative CR-type lower bounds that have been considered or used for this purpose and certain limitations and/or caveats associated with their use are offered. These results should be of interest to sonar, sonobuoy, and radar practitioners and Kalman-filtering or nonlinear-filtering theorists     

Geometrical entropy approach for variable structure multiple-model estimation

The variable structure multiple-model(VSMM) estimation approach, one of the multiple-model(MM) estimation approaches, is popular in handling state estimation problems with mode uncertainties.In the VSMM algorithms, the model sequence set adaptation(MSA) plays a key role.The MSA methods are challenged in both theory and practice for the target modes and the real observation error distributions are usually uncertain in practice.In this paper, a geometrical entropy(GE) measure is proposed so that the MSA is achieved on the minimum geometrical entropy(MGE) principle.Consequently, the minimum geometrical entropy multiple-model(MGEMM) framework is proposed, and two suboptimal algorithms, the particle filter k-means minimum geometrical entropy multiple-model algorithm(PF-KMGEMM) as well as the particle filter adaptive minimum geometrical entropy multiple-model algorithm(PF-AMGEMM), are established for practical applications.The proposed algorithms are tested in three groups of maneuvering target tracking scenarios with mode and observation error distribution uncertainties.Numerical simulations have demonstrated that compared to several existing algorithms, the MGE-based algorithms can achieve more robust and accurate estimation results when the real observation error is inconsistent with a priori.     

Bounds on Time-Delay Estimation for Monochromatic Signals

Time-delay estimation (TDE) of monochromatic signals,observed by spatially separated sensors, is widely used for sourcelocalization in radar/sonar applications. We use lower bounds tostudy the accuracy of TDE as a function of signal to noise ratio(SNR), frequency, and sensor separation. We show that the Cramer-Rao bound, which is frequently used as a standard ofreference, yields optimistic predictions in many cases.     

A comprehensive survey on the methods of angle of attack measurement and estimation in UAVs

Angle of Attack (AOA) is a crucial parameter which directly affects the aerodynamic forces of an aircraft. The measurement of AOA is required to ensure a safe flight within its designed flight envelop. This paper intends to summarise a comprehensive survey on the measurement techniques and estimation methods for AOA, specifically in Unmanned Aerial Vehicle (UAV) applications. In the case of UAVs, weight constraint plays a major role as far as sensor suites are concerned. This results in selecting a suitable estimation method to extract AOA using the available data from the autopilot. The most feasible and widely employed AOA measurement technique is by using the Multi-Hole Probes (MHPs). The MHP measures the AOA regarding the pressure variations between the ports. Due to the importance of MHP in AOA measurement, the calibration methods for the MHP are also included in this paper. This paper discusses the AOA measurement using virtual AOA sensors, their importance and the operation.     

航天器有限时间自适应姿态跟踪容错控制

非刚体航天器存在时变的惯量、执行器完全失效或衰退故障以及外界干扰的情况,提出一种有限时间自适应姿态跟踪容错控制方法。首先,基于有限时间理论和自适应方法,设计惯量不确定性自适应估计项和外界干扰参数自适应估计项进行系统补偿,克服惯量不确定性和抑制外界干扰;然后,基于容错控制和双幂次方法,设计一种自适应有限时间姿态跟踪容错控制算法,并且利用Lyapunov稳定性理论证明所提算法能够保证航天器姿态跟踪系统实际有限时间稳定;最后,对仿真结果进行验证。结果表明:所提有限时间姿态跟踪容错控制方法是有效的。