Concepts for position and load control for hybrid actuation in primary flight controls

Moving towards the More Electric Aircraft, a hybrid actuator configuration, in which an electromechanical actuator (EMA) and an electrohydraulic servoactuator (EHSA) operate on the same control surface, provides an opportunity to introduce electromechanical actuators into primary flight controls. Besides the operation in active/passive or active/active mode, an “active/no-load” mode is promising. In this mode the EMA is controlled such that it actively follows the movement of the control surface without carrying external air loads, thereby reducing power dissipation compared to active/active mode and failure transients compared to active/passive mode. However, force fighting will occur if both actuators are actively controlled. In this paper, control concepts for a hybrid configuration, extending the original actuator control loops, are presented, enabling active/active as well as active/no-load operation. Nonlinear as well as linear models for an EMA, an EHSA, and a control surface structure are derived from technical data for an airworthy EHSA and combined to a model of the hybrid configuration. These models are used for matching of actuator dynamics and simulation of the developed control laws. For active/active mode, maximum force fighting between the actuators is reduced from about 500% to 7% of the stall load. For active/no-load mode, a force control loop is added to the EMA control, causing the EMA to follow movements of the control surface such that the external loads on the EMA are zero in steady-state. Force fighting is reduced to 30% of the stall load.     

Minimal submodel-set algorithm for maneuvering target tracking

The variable structure multiple model (VSMM) approach to the maneuvering target tracking problem is considered. A new VSMM design, the minimal submodel-set switching (MSMSS) algorithm for tracking a maneuvering target is presented. The MSMSS algorithm adaptively determines the minimal set of models from the total model set and uses this to perform multiple models (MM) estimation. In addition, an iterative MSMSS algorithm with improved maneuver detection and termination properties is developed. Simulations results demonstrate that, compared with a standard interacting MM (IMM), the proposed algorithms require significantly lower computation while maintaining similar tracking performance. Alternatively, for a computational load similar to IMM, the new algorithms display significantly improved performance.     

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.     

Radar Image Processing with Clusters of Computers

Some radar image processing algorithms such as shape-from-shading are particularly compute-intensive and time-consuming. If, in addition, a data set to be processed is large, then it may make sense to perform the processing of images on multiple workstations or parallel processing systems. We have implemented shape-from-shading, stereo matching, resampling, gridding, and visualization of terrain models in such a manner that they execute either on parallel machines or on clusters of workstations. We were motivated by the large image data set from NASA's Magellan mission to planet Venus, but received additional inspiration from the European Union's Center for Earth Observation program (CEO) and Austria's MISSION initiative for distributed processing of remote sensing images on remote workstations, using publicly-accessible algorithms. We developed a multi-processor approach that we denote as CDIP for Concurrent and Distributed Image Processing. The speedup for image processing tasks increases nearly linearly with the number of processors, be they on a parallel machine or arranged in a cluster of distributed workstations. Our approach adds benefits for users of complex image processing algorithms: the efforts for code porting and code maintenance are reduced and the necessity for specialized parallel processing hardware is eliminated.     

Automatic target recognition of time critical moving targets using1D high range resolution (HRR) radar

Synthetic Aperture Radar (SAR) imaging and Automatic Target Recognition (ATR) of moving targets pose a significant challenge due to the inherent difficulty of focusing moving targets. As a result, ATR of moving targets has recently received increased interest. High Range Resolution (HRR) radar mode offers an approach for recognizing moving targets by forming focused HRR profiles with significantly enhanced target-to-(clutter+noise) (T/(C+N)) via Doppler filtering and/or clutter cancellation. A goal of HRR ATR transition is the implementation and evaluation of algorithms exhibiting robustness under extended operating conditions (EOC). The public domain Moving and Stationary Target Acquisition and Recognition (MSTAR) data set was used to study 1D template-based ATR development and performance. Due to the unavailability of a statistically significant moving ground target data set, this approach was taken as an interim step in assessing the separability of ground targets when using range only discriminants. This report summarizes the data and algorithm methodology, simulated performance results, and recommendations     

AE9, AP9 and SPM: New Models for Specifying the Trapped Energetic Particle and Space Plasma Environment

The radiation belts and plasma in the Earth’s magnetosphere pose hazards to satellite systems which restrict design and orbit options with a resultant impact on mission performance and cost. For decades the standard space environment specification used for spacecraft design has been provided by the NASA AE8 and AP8 trapped radiation belt models. There are well-known limitations on their performance, however, and the need for a new trapped radiation and plasma model has been recognized by the engineering community for some time. To address this challenge a new set of models, denoted AE9/AP9/SPM, for energetic electrons, energetic protons and space plasma has been developed. The new models offer significant improvements including more detailed spatial resolution and the quantification of uncertainty due to both space weather and instrument errors. Fundamental to the model design, construction and operation are a number of new data sets and a novel statistical approach which captures first order temporal and spatial correlations allowing for the Monte-Carlo estimation of flux thresholds for user-specified percentile levels (e.g., 50th and 95th) over the course of the mission. An overview of the model architecture, data reduction methods, statistics algorithms, user application and initial validation is presented in this paper.     

某型发动机喷管的多学科设计优化

以某型发动机喷管为研究对象，在iSIGHT软件平台上利用单级优化算法对其进行多学科设计优化，目标为权衡喷管推力和质量的综合改善。采用了较为稳健的组合优化策略，依次进行试验设计、全局探索和局部寻优。优化目标函数是通过对多个优化目标的线性加权平均获得的。改进的计算结果表明了MDO在喷管设计中的可行性和实用性。     

K-Nearest Neighbor Particle Filters for Dynamic Hybrid Bayesian Networks

In state estimation of dynamic systems, Kalman filters and HMM filters have been applied to linear-Gaussian models and models with finite state spaces. However, they do not work well in most practical problems with nonlinear and non-Gaussian models. Even when the state space is finite, the dynamic Bayesian networks describing the HMM model could be too complicated to manage. Sequential Monte Carlo methods, also known as particle filters (PFs), have been introduced to deal with these real-world problems. They allow us to treat any type of probability distribution, nonlinearity and nonstationarity although they usually suffer major drawbacks of sample degeneracy and inefficiency in high-dimensional cases. We show how we can exploit the structure of partially dynamic hybrid Bayesian networks (PD-HBN) to reduce ``sample depletion' and increase the efficiency of particle filtering by combining the well-known K-nearest neighbor (KNN) majority voting strategy and the concept of evolution algorithm. Essentially, the novel method resamples the dynamic variables and randomly combines them with the existing samples of static variables to produce new particles. As new observations become available, the algorithm allows the particles to incorporate the latest information so that the fittest particles associated with a proposed objective rule will be kept for resampling. We also conduct a theoretical analysis on the proposed KNN-PF algorithm, and demonstrate the accuracy of the performance prediction with extensive simulations. Performance analysis and numerical results show that this new approach has a superior estimation/classification performance compared to other related algorithms.     

Recursive mode star identification algorithms

Star identification can be accomplished by several different available algorithms that identify the stars observed by a star tracker. However, efficiency and reliability remain key issues and the availability of new active pixel cameras requires new approaches. Two novel algorithms for recursive mode star identification are presented here. The first approach is derived by the spherical polygon search (SP-search) algorithm, it was used to access all the cataloged stars observed by the sensor field-of-view (FOV) and recursively add/remove candidate cataloged stars according to the predicted image motion induced by camera attitude dynamics. Star identification is then accomplished by a star pattern matching technique which identifies the observed stars in the reference catalog. The second method uses star neighborhood information and a catalog neighborhood pointer matrix to access the star catalog. In the recursive star identification process, and under the assumption of "slow" attitude dynamics, only the stars in the neighborhood of previously identified stars are considered for star identification in the succeeding frames. Numerical tests are performed to validate the absolute and relative efficiency of the proposed methods.     

A non-Bayesian segmenting tracker for highly maneuvering targets

The segmenting track identifier (STI) is introduced as a new methodology for tracking highly maneuvering targets. This nonBayesian approach dynamically partitions a target track into a sequence of track segments, making hard estimates of when the target's maneuvering mode transitions occur, and then estimates the parameters of the target model for each segment. STI is compared with two variable structures interacting multiple model (VS-IMM) algorithms through simulations, where it is shown to have a three fold performance advantage in median absolute turn rate estimation errors, as well as better position estimation for very highly maneuvering targets. STI is also shown to outperform a Rauch-Tung-Striebel (RTS) fixed-interval smoother when estimates are retrospectively derived, and STI accurately characterize the temporal pattern of maneuvers.     

Efficient algorithms for finding the K best paths througha trellis

A set of algorithms is presented for finding the best set of K mutually exclusive paths through a trellis of N nodes, with worst-case computation time bounded by N³log n for a fixed-precision computation. The algorithms are based on a transformation of the K-path trellis problem into an equivalent minimum-cost network flow (MCNF) problem. The approach allows the application of efficient MCNF algorithms, which can obtain optimal solutions orders of magnitude faster than the algorithm proposed by J.K. Wolf et al. (1989). The resulting algorithms extend the practicality of the trellis formulation (in terms of required computations) to multiobject tracking problems with much larger numbers of targets and false alarms. A response by Wolf et al. is included     

On the Detectability and Use of Normal Modes for Determining Interior Structure of Mars

The InSight mission to Mars is well underway and will be the first mission to acquire seismic data from a planet other than Earth. In order to maximise the science return of the InSight data, a multifaceted approach will be needed that seeks to investigate the seismic data from a series of different frequency windows, including body waves, surface waves, and normal modes. Here, we present a methodology based on globally-averaged models that employs the long-period information encoded in the seismic data by looking for fundamental-mode spheroidal oscillations. From a preliminary analysis of the expected signal-to-noise ratio, we find that normal modes should be detectable during nighttime in the frequency range 5–15 mHz. For improved picking of (fundamental) normal modes, we show first that those are equally spaced between 5–15 mHz and then show how this spectral spacing, obtained through autocorrelation of the Fourier-transformed time series can be further employed to select normal mode peaks more consistently. Based on this set of normal-mode spectral frequencies, we proceed to show how this data set can be inverted for globally-averaged models of interior structure (to a depth of \(\sim 250~\mbox{km}\)), while simultaneously using the resultant synthetically-approximated normal mode peaks to verify the initial peak selection. This procedure can be applied iteratively to produce a “cleaned-up” set of spectral peaks that are ultimately inverted for a “final” interior-structure model. To investigate the effect of three-dimensional (3D) structure on normal mode spectra, we constructed a 3D model of Mars that includes variations in surface and Moho topography and lateral variations in mantle structure and employed this model to compute full 3D waveforms. The resultant time series are converted to spectra and the inter-station variation hereof is compared to the variation in spectra computed using different 1D models. The comparison shows that 3D effects are less significant than the variation incurred by the difference in radial models, which suggests that our 1D approach represents an adequate approximation of the global average structure of Mars.     

Feedback structure based entropy approach for multiple-model estimation

The variable-structure multiple-model（VSMM）approach,one of the multiple-model（MM）methods,is a popular and effective approach in handling problems with mode uncertainties.The model sequence set adaptation（MSA）is the key to design a better VSMM.However,MSA methods in the literature have big room to improve both theoretically and practically.To this end,we propose a feedback structure based entropy approach that could fnd the model sequence sets with the smallest size under certain conditions.The fltered data are fed back in real time and can be used by the minimum entropy（ME）based VSMM algorithms,i.e.,MEVSMM.Firstly,the full Markov chains are used to achieve optimal solutions.Secondly,the myopic method together with particle flter（PF）and the challenge match algorithm are also used to achieve sub-optimal solutions,a trade-off between practicability and optimality.The numerical results show that the proposed algorithm provides not only refned model sets but also a good robustness margin and very high accuracy.     

EM-ML algorithm for track initialization using possibly noninformative data

Initializing and maintaining a track for a low observable (LO) (low SNR, low target detection probability and high false alarm rate) target can be very challenging because of the low information content of measurements. In addition, in some scenarios, target-originated measurements might not be present in many consecutive scans because of mispointing, target maneuvers, or erroneous preprocessing. That is, one might have a set of noninformative scans that could result in poor track initialization and maintenance. In this paper an algorithm based on the expectation-maximization (EM) algorithm combined with maximum likelihood (ML) estimation is presented for tracking slowly maneuvering targets in heavy clutter and possibly noninformative scans. The adaptive sliding-window EM-ML approach, which operates in batch mode, tries to reject or weight down noninformative scans using the Q-function in the M-step of the EM algorithm. It is shown that target features in the form of, for example, amplitude information (AI), can also be used to improve the estimates. In addition, performance bounds based on the supplemented EM (SEM) technique are also presented. The effectiveness of new algorithm is first demonstrated on a 78-frame long wave infrared (LWIR) data sequence consisting of an Fl Mirage fighter jet in heavy clutter. Previously, this scenario has been used as a benchmark for evaluating the performance of other track initialization algorithms. The new EM-ML estimator confirms the track by frame 20 while the ML-PDA (maximum likelihood estimator combined with probabilistic data association) algorithm, the IMM-MHT (interacting multiple model estimator combined with multiple hypothesis tracking) and the EVIM-PDA estimator previously required 28, 38, and 39 frames, respectively. The benefits of the new algorithm in terms of accuracy, early detection, and computational load are illustrated using simulated scenarios as well.     

Multiple-model estimation with variable structure. V. Likely-modelset algorithm

For pt. IV see ibid., vol. 35, no. 1, p. 242-254 (1999). A multiple-model (MM) estimator with a variable structure, called likely-model set (LMS) algorithm, is presented, which is generally applicable to most hybrid estimation problems and is easily implementable. It uses a set of models that are not unlikely to match the system mode in effect at any given time. Different versions of the algorithm are discussed. The model set is made adaptive in the simplest version by deleting all unlikely models and activating all models to which a principal model may jump so as to anticipate the possible system mode transitions. The generality, simplicity, and ease in the design and implementation of the LMS estimator are illustrated via an example of tracking a maneuvering target and an example of fault detection and identification. Comparison of its cost-effectiveness with other fixed- and variable-structure MM estimators is given     

一种用于解决粒子滤波粒子退化现象的重要性重采样算法的研究

粒子滤波通过蒙特卡罗模拟来实现递推贝叶斯估计,在非线性非高斯系统中体现出良好的特性;但粒子滤波存在粒子退化现象的缺陷,针对这一问题,提出一种新的重采样算法,即分区重采样算法,其主要思想是根据多项式重采样与分层重采样算法的特点,把随机数区间划分成若干个区,每个区内的随机数任意排列,而区与区之间按升序排列。与目前常用的其他重采样算法相比,该方法提高了粒子滤波的平均性能,仿真实验验证了该算法的有效性和实用性。     

两种涡扇发动机部件特性自适应模型对比

发展了航空发动机自适应模型．并对以两种优化算法为基础的自适应模型进行了对比分析。两种模型以通用特性为基础运用优化方法,以发动机主要性能参数和过程参数偏差函数最小为优化目标,以部件特性耦合因子为被优参数,可以预测出不同飞行条件下的发动机风扇、压气机、燃烧室、高压涡轮、低压涡轮等部件特性。运用单纯形和遗传算法为基础的自适应模型对某型涡扇发动机性能的计算结果表明：相对于单纯形算法模型．遗传算法模型对发动机主要性能参数和过程参数的计算偏差降低了20％～30％;对发动机各截面总温、总压计算偏差降低了15％～20％;遗传算法模型相对于单纯形模型具有更为宽广的自适应模拟范围。对某型已知部件特性的涡扇发动机模拟结果显示．遗传算法模型部件特性模拟结果与已知部件特性差别甚微。     

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.     

考虑工序外协的TOC产品组合优化研究

 针对 “粗粒度”产品外包或自制模式进行能力拓展时造成的产品规划保守和接单损失问题,采用“细粒度”工序外协模式对瓶颈、非瓶颈有区别地进行产能提升,使企业设备平均利用率最高、整体有效产出最优、市场需求满意度最好。从自制商和外协商两种角度定义了有效产出,并推导了工序外协和产品外包两种不同制造模式下有效产出的关系。基于传统和新型两种约束理论（TOC）运作逻辑,根据独立优化和集成优化两种优化策略,构建了考虑工序外协的两种TOC产品组合优化数学模型。分析了工序外协和自制/外包模型间的关系,并进行了数学证明。若单位产品的外包成本等于所有工序外协的累加成本,则同一制造模式下集成优化策略不差于独立优化策略,同一优化策略下工序外协制造模式不差于产品外包制造模式。最后通过单瓶颈、多瓶颈算例分析验证了模型以及定理的有效性。     

通过铣削力或铣削扭矩监测铣刀破损的研究

为了通过铣削力或铣削扭矩波形的不均匀程度来识别铣刀是否发生破损,提出了两类识别方法。一类是计算齿周期峰值变化率;一类是计算相对最大切削厚度的偏心率。两类算法均简单、可靠,可用于各种铣削加工过程的铣刀破损监测。