MMAE/MMAC control for bending with multiple uncertain parameters

A moving-bank multiple model estimator/controller (MMAE/MMAC) based on linear system, quadratic cost, and Gaussian noise (LQG) assumptions is used to quell unwanted vibrations in a simulated large flexible space structure. The structure, known as the Space Integrated Controls Experiment (SPICE), exists at Phillips Laboratory, Kirtland Air Force Base, New Mexico. The structure consists of a large platform and a smaller platform connected by a tripod of flexible legs. The purpose of the control system is to maintain a very precise line-of-sight (LOS) vector through the center of the spacecraft. Kalman filtering, used to estimate the position and velocity of the bending modes of the structure, and LQG control techniques are the primary design tools used in the MMAE/MMAC algorithms. Implementing a parallel bank of filters increases robustness when uncertainties exist in the system model, here specifically allowing adaptation to uncertain and changing undamped natural frequencies of the bending modes of the structure. A moving-bank algorithm is utilized to reduce the computational loading. The MMAE/MMAC design provides a well-suited method of estimating variations in the vector of undamped natural frequencies and quelling vibrations in the structure. The MMAE/MMAC was able to track numerous parameter changes and jumps while providing adequate control for the structure.     

Flexible spacestructure control via moving-bank multiple modelalgorithms

The development and performance of moving-bank multiple model adaptive control (MMAC) algorithms for quelling vibrations induced in the SPICE 2 space structure are presented. The structure consists of a large platform and a smaller platform connected by three legs in a tripod fashion. Deviations of the line-of-sight (LOS) vector from the center of the large platform to the center of the smaller platform are used for LQG controller performance evaluation. The parameter estimator implements the maximum entropy with identity covariance (ME/I) algorithm; the moving-bank logic employs parameter position monitoring; the controller uses the modified MMAC method. Whereas parameter variations of two percent caused instabilities in the single filter/controller design, the MMAC algorithm provides an excellent method to estimate a wide range of parameter variations and to quell oscillations in the structure     

模型降阶在折叠机翼主动控制中的应用研究

针对以主动控制为目的的模型降阶中的降阶精度以及控制系统的降阶设计问题,以变体飞机折叠机翼为对象,建立以模态综合法为基础的动力学模型,对该模型分别采用模态价值分析方法和平衡截断降阶方法建立结构的降阶模型;利用可控度、可观度对两种降阶模型的精度进行对比分析,对降阶模型进行设计并施加主动控制律,抑制翼尖的位移响应。结果表明:平衡截断降阶模型具有较高的可控度,模态价值分析降阶模型具有较高的可观度;两种降阶模型均可以快速精确地得到高阶动力学的降阶模型,并且该模型可以有效地应用于主动控制系统的设计。     

Interrelationship of single-filter and multiple-model adaptivealgorithms

An equivalent filter bank structure for multiple model adaptive estimation (MMAE) is developed that uses the residual and state estimates from a single Kalman filter and linear transforms to produce equivalent residuals of a complete Kalman filter bank. The linear transforms, which are a function of the differences between the system models used by the various Kalman filters, are developed for modeling differences in the system input matrix, the output matrix, and the state transition matrix. The computational cost of this new structure is compared with the cost of the standard Kalman filter bank (SKFB) for each of these modeling differences. This structure is quite similar to the generalized likelihood ratio (GLR) structure, where the linear transforms can be used to compute the matched filters used in the GLR approach. This approach produces the best matched filters in the sense that they truly represent the time history of the residuals caused by a physically motivated failure model     

Enhanced motion and sizing of bank in moving-bank MMAE

The focus of this research is to provide methods for generating precise parameter estimates in the face of potentially significant parameter variations such as system component failures. The standard multiple model adaptive estimation (MMAE) algorithm uses a bank of Kalman filters, each based on a different model of the system. Parameter discretization within the MMAE refers to selection of the parameter values assumed by the elemental Kalman filters, and dynamically redeclaring such discretization yields a moving-bank MMAE. A new online parameter discretization method is developed based on the probabilities associated with the generalized chi-squared random variables formed by residual information from the elemental Kalman filters within the MMAE. This new algorithm is validated through computer simulation of an aircraft navigation system subjected to interference/jamming while attempting a successful precision landing of the aircraft.     

基于重力梯度测量系统的组件结构模态分析

重力梯度测量系统设计过程中,对重力梯度旋转平台中仪表的位置特性有严格要求。按照传统的设计流程,等到结构件设计、安装完毕再进行振动试验能充分反映结构的真实位移,但是会消耗大量的时间及人力成本,效率较低。对此,本文采用数值模态分析方法在实际生产之前对整体结构进行了较为详尽的有限元分析,计算出了前十阶模态频率和振型,并且检验了结构设计中的刚度问题。对样机结构进行修改,从而保证了一阶主频率达到539.74 Hz,从而满足了航空搭载的环境要求。对整体结构的模态分析为重力梯度旋转平台工程样机的研制奠定了基础。     

Multiple-model adaptive estimation using a residual correlationKalman filter bank

We propose a modified multiple model adaptive estimation (MMAE) algorithm that uses the time correlation of the Kalman filter residuals, in place of their scaled magnitude, to assign conditional probabilities for each of the modeled hypotheses. This modified algorithm, denoted the residual correlation Kalman filter bank (RCKFB), uses the magnitude of an estimate of the correlation of the residual with a slightly modified version of the usual MMAE hypothesis testing algorithm to assign the conditional probabilities to the various hypotheses that are modeled in the Kalman filter bank within the MMAE. This concept is used to detect flight control actuator failures, where the existence of a single frequency sinusoid (which is highly time correlated) in the residual of an elemental filter within an MMAE is indicative of that filter having the wrong actuator failure status hypothesis. This technique results in a delay in detecting the flight control actuator failure because several samples of the residual must be collected before the residual correlation can be estimated. However, it allows a significant reduction of the amplitude of the required system inputs for exciting the various system modes to enhance identifiability, to the point where they may possibly be subliminal, so as not to be objectionable to the pilot and passengers     

基于有限元分析方法的某惯性台体结构优化设计

建立惯导系统中惯性组件结构三维模型,利用有限元分析软件对惯性组件结构进行有限元模态计算和分析。根据模态分析结果,对惯性台体结构进行拓扑优化,达到惯性台体减重的目的,优化后的惯性台体结构满足文中提出的设计要求。此结构优化设计方法简单,易于实现,对优化惯导系统结构设计,实现小型化、轻量化目标具有一定的指导意义。     

Detection of interference/jamming and spoofing in a DGPS-aidedinertial system

Previous research at the Air Force Institute of Technology (AFIT) has resulted in the design of a differential Global Positioning System (DGPS) aided INS-based (inertial navigation system) precision landing system (PLS) capable of meeting the FAA precision requirements for instrument landings. The susceptibility of DGPS transmissions to both intentional and nonintentional interference/jamming and spoofing must be addressed before DGPS may be safely used as a major component of such a critical navigational device. This research applies multiple model adaptive estimation (MMAE) techniques to the problem of detecting and identifying interference/jamming and spoofing in the DGPS signal. Such an MMAE is composed of a bank of parallel filters, each hypothesizing a different failure status, along with an evaluation of the current probability of each hypothesis being correct, to form a probability-weighted average state estimate as an output. For interference/jamming degradation represented as increased measurement noise variance, simulation results show that, because of the good failure detection and isolation (FDI) performance using MMAE, the blended navigation performance is essentially that of a single extended Kalman filter (EKF) artificially informed of the actual interference noise variance. However, a standard MMAE is completely unable to detect spoofing failures (modeled as a bias or ramp offset signal directly added to the measurement). This work describes a moving-bank pseudoresidual MMAE (PRMMAE) to detect and identify such spoofing. Using the PRMMAE algorithm, spoofing is very effectively detected and isolated; the resulting navigation performance is equivalent to that of an EKF operating in an environment without spoofing     

Characterization of Kalman filter residuals in the presence ofmismodeling

The mean and covariance of a Kalman filter residual are computed for specific cases in which the Kalman filter model differs from a linear model that accurately represents the true system (the truth model). Multiple model adaptive estimation (MMAE) uses a bank of Kalman filters, each with a different internal model, and a hypothesis testing algorithm that uses the residuals from this bank of Kalman filters to estimate the true system model. At most, only one Kalman filter model will exactly match the truth model and will produce a residual whose mean and standard deviation have already been analyzed. All of the other filters use internal models that mismodel the true system. We compute the effects of a mismodeled input matrix, output matrix, and state transition matrix on these residuals. The computed mean and covariance are compared with simulation results of flight control failures that correspond to mismodeled input matrices and output matrices     

航空惯性稳定平台的框架刚度及模态分析

在航空惯性稳定平台的框架结构设计中,采用ANSYS有限元软件对其结构进行了刚度和模态分析,得到了大载荷作用下的应力、应变分布及模态特性,并根据分析结果对其结构进行了优化设计,以满足稳定平台的控制精度要求。分析结果表明,优化后的框架结构刚度得到了大幅的提高,同时其动态特性也得到了有效的改善。从而验证了在稳定平台设计中进行工程力学分析的必要性和合理性,为航空惯性稳定平台的设计提供理论依据     

Multiple model adaptive estimation with filter spawning

Multiple model adaptive estimation (MMAE) with filter spawning is used to detect and estimate partial actuator failures on the VISTA F-16. The truth model is a full six-degree-of-freedom simulation provided by Calspan and General Dynamics. The design models are chosen as 13-state linearized models, including first order actuator models. Actuator failures are incorporated into the truth model and design model assuming a "failure to free stream." Filter spawning is used to include additional filters with partial actuator failure hypotheses into the MMAE bank. The spawned filters are based on varying degrees of partial failures (in terms of effectiveness) associated with the complete-actuaton-failure hypothesis with the highest conditional probability of correctness at the current time. Thus, a blended estimate of the failure effectiveness is found using the filters' estimates based upon a no-failure hypothesis, a complete actuator failure hypothesis, and the spawned filters' partial-failure hypotheses. This yields substantial precision in effectiveness estimation, compared with what is possible without spawning additional filters, making partial failure adaptation a viable methodology.     

Identification of reduced-order model for an aeroelastic system from flutter test data

Recently, flutter active control using linear parameter varying (LPV) framework has attracted a lot of attention. LPV control synthesis usually generates controllers that are at least of the same order as the aeroelastic models. Therefore, the reduced-order model is required by syn-thesis for avoidance of large computation cost and high-order controller. This paper proposes a new procedure for generation of accurate reduced-order linear time-invariant (LTI) models by using sys-tem identification from flutter testing data. The proposed approach is in two steps. The well-known poly-reference least squares complex frequency (p-LSCF) algorithm is firstly employed for modal parameter identification from frequency response measurement. After parameter identification, the dominant physical modes are determined by clear stabilization diagrams and clustering tech-nique. In the second step, with prior knowledge of physical poles, the improved frequency-domain maximum likelihood (ML) estimator is presented for building accurate reduced-order model. Before ML estimation, an improved subspace identification considering the poles constraint is also proposed for initializing the iterative procedure. Finally, the performance of the proposed procedure is validated by real flight flutter test data.     

Reconfigurable flight control via multiple model adaptive controlmethods

An aircraft flight control system with reconfigurable capabilities is considered. A multiple model adaptive controller (MMAC) is shown to provide effective reconfigurability when subjected to single and double failures of sensors and/or actuators. A command generator tracker/proportional-plus-integral/Kalman filter (CGT/PI/KF) form of controller was chosen for each of the elemental controllers within the MMAC algorithm and each was designed via LQG synthesis to provide desirable vehicle behavior for a particular failure status of sensors and actuators. The MMAC performance is enhanced by an alternate computation of the MMAC hypothesis probabilities, use of maximum a posteriori probability (MAP) versus Bayesian form of the MAC (or a modified combination of both), and reduction of identification ambiguities through scalar residual monitoring for the case of sensor failures     

Multipath-adaptive GPS/INS receiver

Multipath interference is one of the contributing sources of errors in precise global positioning system (GPS) position determination. This paper identifies key parameters of a multipath signal, focusing on estimating them accurately in order to mitigate multipath effects. Multiple model adaptive estimation (MMAE) techniques are applied to an inertial navigation system (INS)-coupled GPS receiver, based on a federated (distributed) Kalman filter design, to estimate the desired multipath parameters. The system configuration is one in which a GPS receiver and an INS are integrated together at the level of the in-phase and quadrature phase (I and Q) signals, rather than at the level of pseudo-range signals or navigation solutions. The system model of the MMAE is presented and the elemental Kalman filter design is examined. Different parameter search spaces are examined for accurate multipath parameter identification. The resulting GPS/INS receiver designs are validated through computer simulation of a user receiving signals from GPS satellites with multipath signal interference present The designed adaptive receiver provides pseudo-range estimates that are corrected for the effects of multipath interference, resulting in an integrated system that performs well with or without multipath interference present.     

适用于参数可调结构的非定常气动力降阶建模方法

基于计算流体力学(CFD)的非定常气动力降阶模型(ROM)可以极大提高气动弹性分析效率,然而现有的ROM只能针对固定参数结构,即只适合于固定模态振型,这使得现有ROM在气动弹性优化设计和不确定性分析等结构变参问题中应用受限。针对该问题,在文献[20]基础上提出了一种新的适用于任意模态振型的非定常气动力建模方法。首先将待设计/分析的结构进行参数化抽样和模态分析,之后通过主成分分析(PCA)得到若干基振型,再将这些基振型线性叠加即可拟合抽样空间内任何参数下结构的前若干阶振型。当结构参数改动时,仅仅是叠加系数发生变化。算例表明,仅用很少的基振型就能达到理想的拟合精度。经典的气动力降阶方法可用于基振型坐标下的气动力降阶,进一步变换可得到适用于不同结构的ROM,这意味着,结构参数可以在抽样空间内任意调节改动,而ROM却是通用的。该方法能广泛用于气动弹性优化设计和不确定性分析工作,可提高颤振分析精度和效率。     

永磁同步力矩电机的多领域联合设计与分析

基于Ansys、MATLAB/Simulink等仿真软件,对永磁同步力矩电机的工作特性进行多领域分析。以抑制转矩脉动和振动为目标对电机进行电磁仿真和模态分析;仿真电机空载最高转速的转子离心应力和转子结构形变;校核技术指标要求的最高转速工作点的运行可靠性;基于Ansys Simplorer平台对电机控制系统的转矩特性进行联合仿真;分析电机加工工艺对电机性能的影响。从而从整体系统角度出发,寻求最优的电机设计方案。     

惯性平台轴端结构仿真分析与改进设计

轴端结构是惯性平台系统的主要承载部件,并且实现平台环架结构的转动自由度。因此,轴端构件需要具备足够结构强度和刚度保证惯性平台系统在空间飞行、地面运输等振动冲击工况下可靠工作。本文以某型号惯性平台轴端结构件为研究对象, 探讨了轴端结构件的有限元分析方法。利用HyperWorks 11.0分析软件, 进行了结构静力和模态分析,并对原始的模型结构进行了改进设计。改进后的轴端机盖在相同工况下,结构的强度和刚度上均得到有效提高。     

An MMAE failure detection system for the F-16

A multiple model adaptive estimation (MMAE) algorithm is implemented with the fully nonlinear six-degree-of-motion, Simulation Rapid-Prototyping facility (SRF) VISTA F-16 software simulation tool. The algorithm is composed of a bank of Kalman filters modeled to match particular hypotheses of the real world. Each presumes a single failure in one of the flight-critical actuators, or sensors, and one presumes no failure. For dual failures, a hierarchical structure is used to keep the number of on-line filters to a minimum. The algorithm is demonstrated to be capable of identifying flight-critical aircraft actuator and sensor failures at a low dynamic pressure (20,000 ft, 0.4 Mach). Research includes single and dual complete failures. Tuning methods for accommodating model mismatch, including addition of discrete dynamics pseudonoise and measurement pseudonoise, are discussed and demonstrated. Scalar residuals within each filter are also examined and characterized for possible use as an additional failure declaration voter. An investigation of algorithm performance off the nominal design conditions is accomplished as a first step towards full flight envelope coverage     

基于降阶模型的气动弹性主动控制律设计

流体/结构耦合数值模拟是目前解决复杂气动弹性问题精度最高的方法。但由于计算效率比较低,模型阶数过高,不能直接用于气动弹性系统的主动控制律设计。为了对主动控制系统设计提供高效高精度状态空间模型,研究了气动弹性系统的时域正则正交分解(POD)/降阶模型(ROM)方法,并引入平衡截断(BT)技术进一步降低时域POD/ROM的阶数,从而有效克服了时域POD/ROM阶数过高的缺点。在此基础上建立了基于POD-BT/ROM的气动伺服弹性降阶方程。以AGARD445.6机翼为例,说明了时域POD/ROM建模的各个细节,并将其用于气动弹性主动控制律的设计。计算结果表明,POD/ROM具有接近计算流体力学(CFD)/计算结构动力学(CSD)耦合计算的精度,同时又大大提高了计算效率约1～2个量级,是一种高精度高效率的气动弹性主动控制系统设计工具。