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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

MIMO system identification using frequency response data

A solution to the problem of obtaining a multi-input, multi-output statespace model of a system from its individual input/output frequency responses is presented. The Residue Identification Algorithm (RID) identifies the system poles from a transfer function model of the determinant of the frequency response data matrix. Next, the residue matrices of the modes are computed guaranteeing that each input/output frequency response is fitted in the least squares sense. Finally, a realization of the system is computed. Results of the application of RID to experimental frequency responses of a large space structure ground test facility are presented and compared to those obtained via the Eigensystem Realization Algorithm.

Medina, Enrique A.↗
NASA NTRSDate not supplied

How to Train Your Jacobian: Least-Squares System Identification for Space-Based Coronagraphy

Stellar coronagraphs use closed-loop focal-plane wavefront sensing and control algorithms to create high-contrast dark zones suitable for imaging exoplanets and exozodiacal dust clouds around nearby stars. Model-based algorithms are susceptible to model mismatch, wherein a departure of the coronagraph's true optical characteristics from the assumed model causes reduced control loop performance. Here, we describe a simple technique for empirically tuning the wavefront control Jacobian matrix using applied deformable mirror commands and observed images. This mitigates model mismatch and recovers near-optimal control loop performance.

coronagraphy↗
NASA NTRS1989

Recursive form of the eigensystem realization algorithm for system identification

An algorithm is developed for recursively calculating the minimum realization of a linear system from sampled impulse response data. The Gram-Schmidt orthonormalization technique is used to generate an orthonormal basis for factorization of the data matrix. The system matrix thus identified is in upper Hessenberg form, which has advantages for the identification of modal parameters including damping coefficients, frequencies, mode shapes, and modal participation factors. It also has the property that once an element of the system matrix is computed, it is never altered as the dimension of the model is increased in the recursive process. Numerical examples are presented for comparison of the recursive and nonrecursive forms of the eigensystem realization algorithm.

Longman, Richard W.↗
NASA NTRSDate not supplied

Interval Predictor Models for Robust System Identification

This paper proposes a framework for the identification and uncertainty quantification of plant models according to multivariable data. The only restriction imposed upon such models is for their outputs to depend continuously on their parameters. An Interval Predictor Model (IPM) prescribes the parameters of a computational model as a path-connected set thereby making each predicted output an interval-valued function of its inputs. The formulation proposed seeks the parameter set for which the predicted outputs tightly enclose the data. This set, which is modeled as a semi-algebraic set of low-degree polynomials, enables the characterization of possibly strong parameter dependencies commonly found in practice. This uncertainty characterization makes the resulting plant model amenable to robust control approaches using polynomial optimization. Furthermore, we use non-convex scenario theory to assess the reliability of the resulting IPM. This assessment yields a distribution-free upper bound on the probability that future data will fall outside the predicted intervals.

interval↗
NASA NTRS1983

Digital Instrumentation Analysis and Navigation System (DIANS) for system identification

Hardware, software, and performance features of the Digital Instrumentation and Navigation System (DIANS) designed for NASA research to collect flight data as a strap-down system are detailed. The support software for the system has a cross compiler, a linkage editor, and cross assembler, extended communication capabilities, postflight processing applications, and compilers for PASCAL, FORTRAN, CBASIC and MT. The DIANS microcomputer has a 1 Mbyte RAM module, a fast floating point processor board, a 68000 monoboard computer with 64 RAM, a 128 Kbyte bubble memory card, and a navigation radio. The system also carries a battery for full system operation for over an hour. The support software is also stored on a host mainframe computer, which has a CP/M operating system. Pitch, roll, and heading data are gathered from the on-board system, and communication is possible between the airborne and ground-based computer.

Smyth, R. K.↗
NASA NTRS1996

Parametric Robust Control and System Identification: Unified Approach

During the period of this support, a new control system design and analysis method has been studied. This approach deals with control systems containing uncertainties that are represented in terms of its transfer function parameters. Such a representation of the control system is common and many physical parameter variations fall into this type of uncertainty. Techniques developed here are capable of providing nonconservative analysis of such control systems with parameter variations. We have also developed techniques to deal with control systems when their state space representations are given rather than transfer functions. In this case, the plant parameters will appear as entries of state space matrices. Finally, a system modeling technique to construct such systems from the raw input - output frequency domain data has been developed.

Keel, L. H.↗
NASA NTRS1974

On the design of optimal input signals in system identification

The problem of designing optimal inputs in the identification of multi-input multi-output linear systems with unknown time-varying parameters is considered using a Bayesian approach. A sensitivity index gives a measure of performance for the closed-loop system inputs. The computation of the optimal closed-loop mappings is shown to be a nontrivial exercise in stochastic control with no analytic solution, but optimal open-loop and affine laws yield much more tractable problems. For time-invariant systems, the sensitivity index considered is shown to be equivalent to the trace of the (strictly positive definite) information matrix associated with the system. 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 NTRS1984

An on-line equivalent system identification scheme for adaptive control

A prime obstacle to the widespread use of adaptive control is the degradation of performance and possible instability resulting from the presence of unmodeled dynamics. The approach taken is to explicitly include the unstructured model uncertainty in the output error identification algorithm. The order of the compensator is successively increased by including identified modes. During this model building stage, heuristic rules are used to test for convergence prior to designing compensators. Additionally, the recursive identification algorithm as extended to multi-input, multi-output systems. Enhancements were also made to reduce the computational burden of an algorithm for obtaining minimal state space realizations from the inexact, multivariate transfer functions which result from the identification process. A number of potential adaptive control applications for this approach are illustrated using computer simulations. Results indicated that when speed of adaptation and plant stability are not critical, the proposed schemes converge to enhance system performance.

Sliwa, S. M.↗
NASA NTRS1986

Flight test planning and parameter extraction for rotorcraft system identification

The present study is concerned with the mathematical modelling of aircraft dynamics on the basis of an investigation conducted with the aid of the Rotor System Research Aircraft (RSRA). The particular characteristics of RSRA make it possible to investigate aircraft properties which cannot be readily studied elsewhere, for example in the wind tunnel. The considered experiment had mainly the objective to develop an improved understanding of the physics of rotor flapping dynamics and rotor loads in maneuvers. The employed approach is based on a utilization of parameter identification methodology (PID) with application to helicopters. A better understanding of the contribution of the main rotor to the overall aircraft forces and moments is also to be obtained. Attention is given to the mathematical model of a rotorcraft system, an integrated identification method, flight data processing, and the identification of RSRA mathematical models.

Wang, J. C.↗
NASA NTRS1971

IMBLMS phase B4, additional tasks 5.0. Microbial identification system

A laboratory study was undertaken to provide simplified procedures leading to the presumptive identification (I/D) of defined microorganisms on-board an orbiting spacecraft. Identifications were to be initiated by nonprofessional bacteriologists, (crew members) on a contingency basis only. Key objectives/constraints for this investigation were as follows:(1) I/D procedures based on limited, defined diagnostic tests, (2) testing oriented about ten selected microorganisms, (3) provide for definitive I/D key and procedures per selected organism, (4) define possible occurrences of false positives for the resulting I/D key by search of the appropriate literature, and (5) evaluation of the I/D key and procedure through a limited field trial on randomly selected subjects using the I/D key.

Source record↗
NASA NTRS2018

Designing Graceful Degradation into Complex Systems: Identification of Causes of Degradation, Interactions, and Mitigation of Degradation in Air Traffic Control

System resilience is critical to safety in air traffic control. An important element of maintaining resilience is the ability of systems to degrade gracefully. Of the available graceful degradation research, a majority of studies have focused primarily on technological causes of degradation only, limiting an ecologically valid understanding of the causes of degradation in air traffic control, and the preventative and mitigative strategies that enable graceful degradation. The current study aimed to address this research gap by investigating causes of degradation in air traffic control across the broad categories of technology, the environment, and the human operator, and the potential interactions between these causes. 12 retired controllers participated in semi-structured interviews focused on previous experience of causes of degradation and mitigation strategies. Findings provide an understanding of causation of degradation in air traffic control, and the prevention and mitigation strategies that moderate the relationship between cause and system effect. Findings confirmed that causes appear to interact to create compound, multiple effects on overall system performance. Findings also revealed prevention and mitigation strategies utilized to moderate the effect of the cause on the system. In order to gain an ecologically valid understanding of the causes of degradation, and effective prevention or mitigation strategies, causes from multiple categories, and the interactions between them, must be identified. Findings have implications for designers of future air traffic control systems to ensure the ability of the system to gracefully degrade, as well as risk assessment and system validation processes.

human performance↗
NASA NTRS1981

The use of frequency methods in rotorcraft system identification

A new approach to model structure determination is examined. Flight data from the Rotor Systems Research Aircraft (RSRA) are transformed into the frequency domain and truncated to provide band limiting. The stepwise regression technique is then used to identify a quasistatic state-space model from the transformed data. The data processing requirements for both time domain and frequency domain identification are discussed and the results of the two techniques are compared.

Duval, R. W.↗
NASA NTRS1980

Adaptive/learning control of large space structures - System identification techniques

Techniques developed for the control of aircraft under changing operating conditions are used to develop a learning control system structure for a multi-configuration, flexible space vehicle. A configuration identification subsystem that is to be used with a learning algorithm and a memory and control process subsystem is developed. Adaptive gain adjustments can be achieved by this learning approach without prestoring of large blocks of parameter data and without dither signal inputs which will be suppressed during operations for which they are not compatible. The Space Shuttle Solar Electric Propulsion (SEP) experiment is used as a sample problem for the testing of adaptive/learning control system algorithms.

Thau, F. E.↗
NASA NTRS1988

Active control and system identification of rotordynamic structure

Four current research projects are summarized: (1) active control of rotor system dynamics; (2) attenuation of rotor vibration using controlled pressure hydrostatic bearings; (3) a new seal test facility for measuring isotropic and anisotropic linear rotordynamic characteristics; and (4) the use of rotordynamic instability thresholds to accurately measure bearing rotordynamic characteristics.

Adams, M. L.↗
NASA NTRS1992

Frequency domain state-space system identification

An algorithm for identifying state-space models from frequency response data of linear systems is presented. A matrix-fraction description of the transfer function is employed to curve-fit the frequency response data, using the least-squares method. The parameters of the matrix-fraction representation are then used to construct the Markov parameters of the system. Finally, state-space models are obtained through the Eigensystem Realization Algorithm using Markov parameters. The main advantage of this approach is that the curve-fitting and the Markov parameter construction are linear problems which avoid the difficulties of nonlinear optimization of other approaches. Another advantage is that it avoids windowing distortions associated with other frequency domain methods.

Chen, Chung-Wen↗
NASA NTRS1982

A simple, low cost application of a flight test parameter identification system

The flight test system combines state-of-the-art microprocessor technology and high accuracy instrumentation with parameter identification technology which minimize data and flight time requirements. The system was designed to avoid permanent modifications of the test airplane and allow quick installation. It is capable of longitudinal and lateral-directional stability and control derivative estimation. This paper presents details of this system, calibration and flight test procedures, and the results of the Cessna 172 flight test program. The system has proven easy to install, simple to operate, and capable of accurate estimation of stability and control parameters in the Cessna 172 flight tests.

Clarke, R.↗
NASA NTRS2003

Continuous-Time Bilinear System Identification

The objective of this paper is to describe a new method for identification of a continuous-time multi-input and multi-output bilinear system. The approach is to make judicious use of the linear-model properties of the bilinear system when subjected to a constant input. Two steps are required in the identification process. The first step is to use a set of pulse responses resulting from a constant input of one sample period to identify the state matrix, the output matrix, and the direct transmission matrix. The second step is to use another set of pulse responses with the same constant input over multiple sample periods to identify the input matrix and the coefficient matrices associated with the coupling terms between the state and the inputs. Numerical examples are given to illustrate the concept and the computational algorithm for the identification method.

Juang, Jer-Nan↗