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At least 487 records · Page 27

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NASA NTRS2001

Predictive Compensator Optimization for Head Tracking Lag in Virtual Environments

We examined the perceptual impact of plant noise parameterization for Kalman Filter predictive compensation of time delays intrinsic to head tracked virtual environments (VEs). Subjects were tested in their ability to discriminate between the VE system's minimum latency and conditions in which artificially added latency was then predictively compensated back to the system minimum. Two head tracking predictors were parameterized off-line according to cost functions that minimized prediction errors in (1) rotation, and (2) rotation projected into translational displacement with emphasis on higher frequency human operator noise. These predictors were compared with a parameterization obtained from the VE literature for cost function (1). Results from 12 subjects showed that both parameterization type and amount of compensated latency affected discrimination. Analysis of the head motion used in the parameterizations and the subsequent discriminability results suggest that higher frequency predictor artifacts are contributory cues for discriminating the presence of predictive compensation.

Adelstein, Barnard D.↗

NASA NTRS2013

Optimized tuner selection for engine performance estimation

A methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented. This technique specifically addresses the underdetermined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multi-variable iterative search routine which seeks to minimize the theoretical mean-squared estimation error. Theoretical Kalman filter estimation error bias and variance values are derived at steady-state operating conditions, and the tuner selection routine is applied to minimize these values. The new methodology yields an improvement in on-line engine performance estimation accuracy.

Simon, Donald L.↗

NASA NTRS1989

An adaptive data-smoothing routine

An adaptive noise reduction algorithm that can be implemented on a microcomputer is developed. Smoothing polynomials are used where the polynomial coefficients are chosen such that the mean-square-error between the noisy and smoothed data is minimized. This approach is equivalent to the implementation of a low-pass finite impulse response filter. The noise reduction depends on the order of the smoothing polynomial. A whiteness test on the error sequence is incorporated to search for the optimal smoothing. Expansion coefficients may be computed via the fast Fourier transform, and the resulting smoothing process is the equivalent of the implementation of an adaptive ideal low-pass filter. Results are obtained for an analytical signal with added white Gaussian noise. The routine may be applied to any smooth signal with additive random noise.

Taylor, Clayborne D.↗

NASA NTRS1975

Constrained optimal controller for linear systems with state and control dependent disturbance

The problem is posed with the additional constraints that the dynamic controller uses only noise-corrupted outputs, and that its dimension is significantly lower than that of a Kalman filter. The unknown disturbance is viewed as an adversary which tries to maximize a performance criterion: a criterion that the controller gains attempt to minimize. The optimal controller gains are determined by solving a nonlinear matrix two-point boundary value problem.

Basuthakur, S.↗

NASA NTRS1975

Digital adaptive controllers using second order models with transport lag

Design of a discrete optimal regulator requires the a priori knowledge of a mathematical model for the system of interest. Because a second-order model with transport lag is very amenable to control computations and because this type of model has been used previously to represent certain high order single input-single output processes, an adaptive controller was designed based upon adjustment of controls computed for such a model. An extended Kalman filter was utilized for tracking the model parameters which were subsequently used to update a set of optimal control gains. Favorable results were obtained in applying this procedure to the control of several examples including a ninth order nonlinear process.

Joshi, S.↗

NASA NTRS1989

Minimum-variance reduced-order estimation algorithms from Pontrygin's minimum principle

A uniform derivation of minimum-variance reduced-order (MVRO) filter-smoother algorithms from Pontrygin's Minimum Principle is presented. An appropriate performance index for a general class of reduced order estimation problem is formulated herein to yield optimal results over the entire time interval of estimation. These results provide quantitative criteria for measuring the performance of certain classes of heuristically designed, suboptimal reduced-order estimators as well as explicit guidance to the suboptimal filter design process with both continuous and discrete filter-smoother algorithms being considered. By the duality principle, the algorithms of reduced-order estimation can be easily extended to the deterministic problems of optimal control (i.e., the regulator and linear tracking problem).

Ebrahimi, Yaghoob S.↗

NASA NTRS1993

Computationally efficient algorithms for real-time attitude estimation

For many practical spacecraft applications, algorithms for determining spacecraft attitude must combine inputs from diverse sensors and provide redundancy in the event of sensor failure. A Kalman filter is suitable for this task, however, it may impose a computational burden which may be avoided by sub optimal methods. A suboptimal estimator is presented which was implemented successfully on the Delta Star spacecraft which performed a 9 month SDI flight experiment in 1989. This design sought to minimize algorithm complexity to accommodate the limitations of an 8K guidance computer. The algorithm used is interpreted in the framework of Kalman filtering and a derivation is given for the computation.

Pringle, Steven R.↗

NASA NTRS1972

A symmetrical laser Doppler velocity meter and its application to turbulence characterization

A symmetrical method of optical heterodyning of the Doppler shifted scattered laser radiation developed for velocity measurements with a minimal instrumental spectral broadening and a high signal-to-noise ratio. The method employs two laser beams incident on the moving scatterer and does not use any reference beam for heterodyning. The Doppler signal frequency is independent of the scattering angle and the signal possesses no receiving aperture broadening. Optical alignment is simple. Typical values of the instrumental spectral broadening were approximately 0.8 percent of the center frequency of the Doppler signal, and the signal-to-noise ratio was approximately 25 dB, obtained from an air flow system using submicron dioctylphthalate scattering aerosol. Experimental and theoretical studies were made on the characteristics of the Doppler signal and the effect of system parameters in turbulent flow measurement. The optimization process involved in the beam optics and in the use of a spatial filter is described. For localized flow measurement in any direction of the three-dimensional orthogonal coordinates, the system, using uncorrected optical components, had a sensing volume which can be described by a sensitive length of 600 microns and a diameter of 100 microns.

Mazumder, M. K.↗

NASA NTRS1972

On the adaptive control of linear systems using the open-loop-feedback-optimal approach.

This paper considers the suboptimal stochastic control of linear discrete-time dynamical systems with unknown or stochastically varying parameters. The suboptimal scheme is based upon the use of the open-loop-feedback-optimal (O.L.F.O.) method. The state and parameter estimates are generated by an extended Kalman filter algorithm. Numerical results for first order systems are presented.

Ku, R.↗

NASA NTRS1973

On the adaptive control of linear systems using the open-loop-feedback-optimal approach.

This paper considers the suboptimal stochastic control of linear discrete-time dynamical systems with uncertain or stochastically varying parameters. The suboptimal scheme is based upon the use of the open-loop-feedback-optimal (OLFO) method. The state and parameter estimates are generated by an extended Kalman filter algorithm. Numerical results for first-order systems are presented.

Ku, R.↗

NASA NTRS1974

On the design of optimal input signals in system identification

The problem of designing optimal inputs in the identification of linear systems with unknown random parameters is considered using a Bayesian approach. The information matrix, which is positive definite for the class of systems analyzed, gives a measure of performance for the system inputs. The computation of the optimal closed-loop input mappings is shown to be a nontrivial exercise in adaptive control. Deterministic optimal inputs are shown to be easily computable. Numerical examples are given. A Kalman filter is used to estimate the parameters. A necessary condition for the Kalman filter not to diverge when applying linear feedback is also given.

Lopez-Toledo, A. A.↗

NASA NTRS1974

Estimation and detection of signals in multiplicative noise

We define a class of detection-estimation problems on matrix Lie groups in which the observation noise is multiplicative in nature. By examining the differential versions of the hypotheses, which are bilinear, we are able to derive the relevant likelihood ratio formula and the associated optimal estimation equations for the signal given the observations and the assumption that the signal is present. These estimation equations are of interest in their own right, in that they represent a finite-dimensional optimal solution to a nonlinear estimation problem and consist of a Kalman-Bucy filter along with the on-line computation of the solution of the associated Riccati equation, which is driven by the observations. The usefulness of these results is illustrated via an example concerning the detection of an actuator failure in a rigid-body rotational control system.

Willsky, A. S.↗

NASA NTRS1980

Parameter identification of linear discrete stochastic systems with time delays

An identification algorithm that uses the maximum likelihood technique to identify the unknown time delays, plant parameters, and noise covariances of linear discrete stochastic systems is presented. Cases of additive white noise and colored measurement noises are considered. The likelihood function is evaluated using either a minimum-variance (Kalman) filter or a minimal-order observer. The Kalman filter is used in the identification algorithm to provide minimum-variance estimates. The minimal-order observer is a lower-dimensional and computationally simpler filter, and is advantageous especially for systems with long delays. It provides a less optimal solution to the minimum-mean-square state estimation problem. The colored-noise observer algorithm has the disadvantage of having to compute an extra error covariance matrix of lower order.

Wong, E. C.↗

NASA NTRS1985

Testing of a failure accommodation system on a highly flexible grid

This paper presents a scheme for on-line failure detection in systems with appreciable structural dynamics. The design is suboptimal because of extensive computational requirements of optimal schemes. To accomplish failure detection, the innovations sequence of a finite order Kalman filter is examined. Because of the heavy dependence of the system on the zero-mean character of the innovations sequence of the filter much attention has been given to the design and evaluation of the Kalman filters used. The filter designs are based on modal models of the structural dynamics. Two modal models were considered, one based on an analytic finite element model and the other based on empirically derived frequency and damping. Experiments using a grid structure are presented which illustrate operation and performance of the filter designs based on these models. The general character of the results presented is that appreciable errors exist in the filter design based on the finite element model. Substantial improvement results if the design model is modified to include empirically derived frequency and damping.

Montgomery, R. C.↗

NASA NTRS1987

Dynamic sensing of 6-axis external force and moment applied to a robot end

A dynamic force sensing method is proposed for a robot manipulator. A 6-axis wrist force sensor which is commonly used for robot force sensing does not take the direct measurement of the force and moment applied to an end effector externally from the environment of the manipulator, but takes the resultant of the gravity, the inertial force and moment on the end effector, and the external force and moment. The separation of gravitational and inertial force and moment from the measurement of the force sensor is crucial to achieving dynamically accurate force sensing and fast manipulator motion under force feedback control. A mathematical model is derived for the force sensing process and the separation of the force and moment as a nonlinear optimal state estimation problem formulated. Then an approximate solution is given using extended Kalman filtering techniques.

Yokota, Masaru Uchiyama↗

NASA NTRS1991

The behavior of forecast error covariances for a Kalman filter in two dimensions

The behavior of forecast error covariances in a fairly realistic setting is demonstrated via a Kalman filter algorithm. It is used to assimilate simulated data from the existing radiosonde network, from the demonstration network of 31 Doppler wind profilers in the central U.S., and from hypothetical radiometers located at five of the profiler sites. Some theoretical justification of the hypothesis advanced by Phillips (1982), and the hypothesis is used to formulate the model error covariance matrix required by the Kalman filter. The results show that assimilating the profiler wind data leads to a large reduction of forecast/analysis error in heights as well as in winds, over the profiler region and also downstream, when compared with the results of assimilating the radiosonde data alone. The forecast error covariance matrices that the Kalman filter calculates to obtain this error reduction differ considerably from those prescribed by the optimal interpolation schemes that are employed for data assimilation at operational centers.

Cohn, Stephen E.↗

NASA NTRS1991

Optimization of an optically implemented on-board FDMA demultiplexer

Performance of a 30 GHz frequency division multiple access (FDMA) uplink to a processing satellite is modelled for the case where the onboard demultiplexer is implemented optically. Included in the performance model are the effects of adjacent channel interference, intersymbol interference, and spurious signals associated with the optical implementation. Demultiplexer parameters are optimized to provide the minimum bit error probability at a given bandwidth efficiency when filtered QPSK modulation is employed.

Fargnoli, J.↗

NASA NTRS1993

On the estimation algorithm for adaptive performance optimization of turbofan engines

The performance seeking control (PSC) algorithm is designed to continuously optimize the performance of propulsion systems. The PSC algorithm uses a nominal propulsion system model and estimates, in flight, the engine deviation parameters (EDPs) characterizing the engine deviations with respect to nominal conditions. In practice, because of measurement biases and/or model uncertainties, the estimated EDPs may not reflect the engine's actual off-nominal condition. This factor has a direct impact on the PSC scheme exacerbated by the open-loop character of the algorithm. In this paper, the effects produced by unknown measurement biases over the estimation algorithm are evaluated. This evaluation allows for identification of the most critical measurements for application of the PSC algorithm to an F100 engine. An equivalence relation between the biases and EDPs stems from the analysis; therefore, it is undecided whether the estimated EDPs represent the actual engine deviation or whether they simply reflect the measurement biases. A new algorithm, based on the engine's (steady-state) optimization model, is proposed and tested with flight data. When compared with previous Kalman filter schemes, based on local engine dynamic models, the new algorithm is easier to design and tune and it reduces the computational burden of the onboard computer.

Espana, Martin D.↗