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At least 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

Integrated system identification and modal state estimation for control of flexible space structures

A novel approach of integrated system identification and modal state estimation is proposed for control of linear dynamical systems including flexible space structures. There are four steps involved in this approach. First, the relation between a stochastic state space model of a dynamical system and the coefficients of its autoregressive model with exogenous input is derived. Second, an adaptive least-squares transversal predictor is used to estimate the coefficients of the model. Third, a state space model and a steady state Kalman filter gain of the dynamical system are then identified from the coefficients of the model by using the eigensystem realization algorithm. Fourth, a modal state estimator is constructed using the modal parameters of the identified model. On-line implementation of this algorithm can continually improve the modal parameters and the filter gain. It can also gradually update the system model when the system characteristics are slowly changing. A numerical example is used to illustrate the feasibility of the new approach.

Chen, Chung-Wen↗
NASA NTRS1990

Accuracy of the ERBS definitive attitude determination system in the presence of propagation noise

Definitive attitude solutions are supposed to be the most accurate possible. For the Earth Radiation Budget Satellite (ERBS), this has been accomplished by using gyro rates to transform many nonsimultaneous observations to a common time point and then averaging to reduce the effects of observation noise. Rate quality is critical to realizing improved accuracy with this method. Gyro deterioration, which shows up as large observation residuals and discontinuities between contiguous batch solutions, now discourages using the batch approach for ERBS. To address this problem, a simple Kalman filter is tried in place of the batch estimator. The filter works well as long as the attitude is completely observable. During periods without Sun coverage, however, the extrapolated yaw may diverge and then change abruptly when the Sun returns to the sensor field of view. Causes of this behavior are discussed, and some solutions are tried that address the observability aspect of the problem.

Chu, D.↗
NASA NTRS1990

Ridge Regression Signal Processing

The introduction of the Global Positioning System (GPS) into the National Airspace System (NAS) necessitates the development of Receiver Autonomous Integrity Monitoring (RAIM) techniques. In order to guarantee a certain level of integrity, a thorough understanding of modern estimation techniques applied to navigational problems is required. The extended Kalman filter (EKF) is derived and analyzed under poor geometry conditions. It was found that the performance of the EKF is difficult to predict, since the EKF is designed for a Gaussian environment. A novel approach is implemented which incorporates ridge regression to explain the behavior of an EKF in the presence of dynamics under poor geometry conditions. The basic principles of ridge regression theory are presented, followed by the derivation of a linearized recursive ridge estimator. Computer simulations are performed to confirm the underlying theory and to provide a comparative analysis of the EKF and the recursive ridge estimator.

Kuhl, Mark R.↗
NASA NTRS1990

System Identification for Nonlinear Control Using Neural Networks

An approach to incorporating artificial neural networks in nonlinear, adaptive control systems is described. The controller contains three principal elements: a nonlinear inverse dynamic control law whose coefficients depend on a comprehensive model of the plant, a neural network that models system dynamics, and a state estimator whose outputs drive the control law and train the neural network. Attention is focused on the system identification task, which combines an extended Kalman filter with generalized spline function approximation. Continual learning is possible during normal operation, without taking the system off line for specialized training. Nonlinear inverse dynamic control requires smooth derivatives as well as function estimates, imposing stringent goals on the approximating technique.

Stengel, Robert F.↗
NASA NTRS1991

Quantitative knowledge acquisition for expert systems

A common problem in the design of expert systems is the definition of rules from data obtained in system operation or simulation. While it is relatively easy to collect data and to log the comments of human operators engaged in experiments, generalizing such information to a set of rules has not previously been a direct task. A statistical method is presented for generating rule bases from numerical data, motivated by an example based on aircraft navigation with multiple sensors. The specific objective is to design an expert system that selects a satisfactory suite of measurements from a dissimilar, redundant set, given an arbitrary navigation geometry and possible sensor failures. The systematic development is described of a Navigation Sensor Management (NSM) Expert System from Kalman Filter convariance data. The method invokes two statistical techniques: Analysis of Variance (ANOVA) and the ID3 Algorithm. The ANOVA technique indicates whether variations of problem parameters give statistically different covariance results, and the ID3 algorithms identifies the relationships between the problem parameters using probabilistic knowledge extracted from a simulation example set. Both are detailed.

Belkin, Brenda L.↗
NASA NTRS1990

A preliminary evaluation of an F100 engine parameter estimation process using flight data

The parameter estimation algorithm developed for the F100 engine is described. The algorithm is a two-step process. The first step consists of a Kalman filter estimation of five deterioration parameters, which model the off-nominal behavior of the engine during flight. The second step is based on a simplified steady-state model of the compact engine model (CEM). In this step, the control vector in the CEM is augmented by the deterioration parameters estimated in the first step. The results of an evaluation made using flight data from the F-15 aircraft are presented, indicating that the algorithm can provide reasonable estimates of engine variables for an advanced propulsion control law development.

Maine, Trindel A.↗
NASA NTRS1991

Spatial operator approach to flexible multibody system dynamics and control

The inverse and forward dynamics problems for flexible multibody systems were solved using the techniques of spatially recursive Kalman filtering and smoothing. These algorithms are easily developed using a set of identities associated with mass matrix factorization and inversion. These identities are easily derived using the spatial operator algebra developed by the author. Current work is aimed at computational experiments with the described algorithms and at modelling for control design of limber manipulator systems. It is also aimed at handling and manipulation of flexible objects.

Rodriguez, G.↗
NASA NTRS1991

Knowledge acquisition for expert systems using statistical methods

A common problem in the design of expert systems is the definition of rules from data obtained in system operation or simulation. A statistical method for generating rule bases from numerical data, motivated by an example based on aircraft navigation with multiple sensors is presented. The specific objective is to design an expert system that selects a satisfactory suite of measurements from a dissimilar, redundant set, given an arbitrary navigation geometry and possible sensor failures. The systematic development of a Navigation Sensor Management (NSM) Expert System from Kalman Filter covariance data is described. The development method invokes two statistical techniques: Analysis-of-Variance (ANOVA) and the ID3 algorithm. The ANOVA technique indicates whether variations of problem parameters give statistically different covariance results, and the ID3 algorithm identifies the relationships between the problem parameters using probabilistic knowledge extracted from a simulation example set.

Belkin, Brenda L.↗
NASA NTRS1987

Enhanced global positioning system delta-range processing

A circuit is shown which utilizes signals transmitted from global positioning system satellites to create change in phase signals. The change in phase signals are measured over a predetermined time interval, one· second, and stored. The stored signals are applied to a Kalman filter and back to the storage unit so that each one second time interval includes the information from all previous time intervals. This accumulated information is referred to as accumulated Delta-Range information which permits a more rapid determination of position by the circuit.

Alison Brown↗
NASA NTRS1990

Random field estimation approach to robot dynamics

The difference equations of Kalman filtering and smoothing recursively factor and invert the covariance of the output of a linear state-space system driven by a white-noise process. Here it is shown that similar recursive techniques factor and invert the inertia matrix of a multibody robot system. The random field models are based on the assumption that all of the inertial (D'Alembert) forces in the system are represented by a spatially distributed white-noise model. They are easier to describe than the models based on classical mechanics, which typically require extensive derivation and manipulation of equations of motion for complex mechanical systems. With the spatially random models, more primitive locally specified computations result in a global collective system behavior equivalent to that obtained with deterministic models. The primary goal of applying random field estimation is to provide a concise analytical foundation for solving robot control and motion planning problems.

Rodriguez, Guillermo↗
NASA NTRS1990

Estimation of synoptic fields of middle atmosphere parameters from Nimbus-7 LIMS profile data

The most widely used version of the Nimbus-7 middle atmosphere dataset is the set of high quality, daily, and zonal Fourier coefficients that resolve information out to six wavenumbers at 12 UTC. A Kalman filter algorithm was applied to the original profile data in order to generate those fields for the data archive or LAMAT product. The characteristics and implementation of the algorithm are described in some detail, along with examples of the output for each of the LIMS parameters.

Remsberg, Ellis E.↗
NASA NTRS1991

Optimizing aircraft performance with adaptive, integrated flight/propulsion control

The Performance-Seeking Control (PSC) integrated flight/propulsion adaptive control algorithm presented was developed in order to optimize total aircraft performance during steady-state engine operation. The PSC multimode algorithm minimizes fuel consumption at cruise conditions, while maximizing excess thrust during aircraft accelerations, climbs, and dashes, and simultaneously extending engine service life through reduction of fan-driving turbine inlet temperature upon engagement of the extended-life mode. The engine models incorporated by the PSC are continually upgraded, using a Kalman filter to detect anomalous operations. The PSC algorithm will be flight-demonstrated by an F-15 at NASA-Dryden.

Smith, R. H.↗
NASA NTRS1989

Linear estimation and control studies of vibration suppression of SCOLE flexible mast

A Kalman filter and a controller are presented for vibration suppression of the Spacecraft Control Laboratory Experiment flexible mast mounted in the cantilevered configuration. Mode shapes and frequencies of the structure obtained from a finite element analysis are used to compute the controller and filter gains. The paper presents results and discussion from simulation and experimental studies. Comparison of experimental results with those obtained by simulation show close agreement.

Ghosh, D.↗
NASA NTRS1990

Calibration and operation of a large space-based optical interferometer

The on-orbit calibration of the optics, structure, and control systems of the CSI Focus Mission Interferometer (FMI) is described. The calibration involves the estimation and propagation of both positional and rotational parameters and the propagation of both positional and rotational parameters at the nanometer/nanoradian level. It is shown that, given a nanometer class metrology system to monitor positional changes of critical optical elements, this calibration procedure should enable the FMI to perform 50 picoradian astrometry. The same Kalman filter that implements the initializing calibration of the interferometer baselines and internal pathlengths will also participate in the astrometric measurements of stellar positions.

Laskin, R. A.↗
NASA NTRS1990

Performance evaluation of a Doppler radar system for wind shear detection

Nonlinear stochastic differential equations are used to model wind shear, and extended Kalman filters are used to generate state estimates from measurements received from a Doppler radar onboard an aircraft. Likelihood-ratio tests are then used to detect the presence of wind shear. The performance of the system is evaluated by deriving theoretical expressions for the false alarm and miss error probabiilties. The approach uses a Fokker-Planck equation. The overall methodology is general and should be of interest in other applications.

Khalaf, Camille S.↗
NASA NTRS1991

Three-dimensional simulations of wintertime ozone variability in the lower stratosphere

The evolution of ozone has been calculated for the winters of 1979 and 1989 using winds derived from the stratospheric data assimilation system STRATAN. The ozone fields calculated using this technique are found to compare well with satellite-measured fields for simulations of 2-3 months. This paper presents comparisons of model fields with both satellite and sonde measurements to verify that stratospheric transport processes are properly represented by this modeling technique. Attention is focused on the Northern Hemisphere middle and high latitudes at the 10-hPa level and below, where transport processes are most important to the ozone distribution. First-order quantities and derived budgets from both the model and satellite data are presented. By sampling the model with a limb-viewing satellite and then Kalman filtering the 'observations' of the model, it is shown that transient subplanetary-scale features that are essential to the ozone budget are missed by the satellite system.

Rood, Richard B.↗
NASA NTRS1990

Application of a novel multi-stage signal parameter estimator to high dynamic GPS receivers

The performance of a novel multistage estimator when applied to the estimation of the position, velocity, and acceleration of high dynamic Global Positioning System (GPS) receivers is discussed. For the present application, a two-stage specialization of the more general estimation scheme is considered, wherein the first-stage algorithm is selected to be a modified least-squares algorithm operating upon the differential signal model and referred to as differential least-squares (DLS) and the second stage is simply an extended Kalman filter (EKF). In terms of the threshold on received carrier power-to-noise power spectral density ratio (CNR), when compared to the single-stage EKF algorithm, the DLS-EKF algorithm is about 1.5-2.0 dB better in terms of threshold and outperforms the crossproduct AFC (automatic frequency control) loop by 2-5 dB. For the case when data modulation is present, the proposed scheme provides an improvement of about 6 dB in terms of CNR compared to an earlier approximate MLE (maximum likelihood estimation) scheme. There are also very significant improvements in terms of other performance measures.

Kumar, R.↗