Integrated system identification and state estimation for control of flexible space structures
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Chen, Chung-Wen↗
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System identification and structural control on the JPL Phase B testbed
The primary objective of NASA's CSI program at JPL is to develop and demonstrate the CSI technology required to achieve high precision structural stability on large complex optical class spacecraft. The focus mission for this work is an orbiting interferometer telescope. Toward the realization of such a mission, a series of evolutionary testbed structures are being constructed. The JPL's CSI Phase B testbed is the second structure constructed in this series which is designed to study the pathlength control problem of the optical train of a stellar interferometer telescope mounted on a large flexible structure. A detailed description of this testbed can be found. This paper describes our efforts in the first phase of active structural control experiments of Phase B testbed using the active control approach where a single piezoelectric active member is used as an actuation device and the measurements include both colocated and noncolocated sensors. Our goal for this experiment is to demonstrate the feasibility of active structural control using both colocated and noncolocated measurements by means of successive control design and loop closing. More specifically, the colocated control loop was designed and closed first to provide good damping improvement over the frequency range of interest. The noncolocated controller was then designed with respect to a partially controlled structure to further improve the performance. Based on our approach, experimental closed-loop results have demonstrated significant performance improvement with excellent stability margins.
Chu, Cheng-Chih↗
Frequency-weighted system identification and linear quadratic controller design
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Horta, Lucas G.↗
Frequency domain system identification methods - Matrix fraction description approach
This paper presents the use of matrix fraction descriptions for least-squares curve fitting of the frequency spectra to compute two matrix polynomials. The matrix polynomials are intermediate step to obtain a linearized representation of the experimental transfer function. Two approaches are presented: first, the matrix polynomials are identified using an estimated transfer function; second, the matrix polynomials are identified directly from the cross/auto spectra of the input and output signals. A set of Markov parameters are computed from the polynomials and subsequently realization theory is used to recover a minimum order state space model. Unevenly spaced frequency response functions may be used. Results from a simple numerical example and an experiment are discussed to highlight some of the important aspect of the algorithm.
Horta, Luca G.↗
On-orbit system identification overview
Parameter identification and modeling are key elements of a design and operational flight strategy for control of flexible space structures. The emphasis of the identification program is on on-orbit applications to spacecraft control. High performance robust controllers will result from advanced design synthesis techniques and on-orbit identification/system tuning. Near term goals for the program include development of an integrated on-line processing capability for flexible body parameter identification, and validation with physical structure experiments.
Mettler, Edward↗
System identification using modal residues and roots
This presentation will address an approach for using modal residues and roots, typically derived from vibration testing, to solve for the lumped parameters of a finite element model. The uniqueness of the approach is that the mass, stiffness, or damping matrices of a structure do not need to be known or estimated a-priori. Instead just the connectivity of the structure needs to be estimated. In addition, the presentation will demonstrate how models with orders larger than the number of modes obtained by the test program can be solved. The presentation will start by covering the basis of the approach, then illustrate a trivial application of how it works, and finally illustrate its use on a cantilevered beam using only a fraction of the mode to solve the mass, stiffness, and damping matrices.
Wendler, Bruce H.↗
Parametric robust control and system identification: Unified approach
Despite significant advancement in the area of robust parametric control, the problem of synthesizing such a controller is still a wide open problem. Thus, we attempt to give a solution to this important problem. Our approach captures the parametric uncertainty as an H(sub infinity) unstructured uncertainty so that H(sub infinity) synthesis techniques are applicable. Although the techniques cannot cope with the exact parametric uncertainty, they give a reasonable guideline to model the unstructured uncertainty that contains the parametric uncertainty. An additional loop shaping technique is also introduced to relax its conservatism.
Keel, Leehyun↗
Flight test validation of a frequency-based system identification method on an F-15 aircraft
A frequency-based performance identification approach was evaluated using flight data from the NASA F-15 Highly Integrated Digital Electronic Control aircraft. The approach used frequency separation to identify the effectiveness of multiple controls simultaneously as an alternative to independent control identification methods. Fourier transformations converted measured control and response data into frequency domain representations. Performance gradients were formed using multiterm frequency matching of control and response frequency domain models. An objective function was generated using these performance gradients. This function was formally optimized to produce a coordinated control trim set. This algorithm was applied to longitudinal acceleration and evaluated using two control effectors: nozzle throat area and inlet first ramp. Three criteria were investigated to validate the approach: simultaneous gradient identification, gradient frequency dependency, and repeatability. This report describes the flight test results. These data demonstrate that the approach can accurately identify performance gradients during simultaneous control excitation independent of excitation frequency.
Schkolnik, Gerard S.↗
Identification System
Under a NASA Space Act Agreement with Marshall Space Flight Center, Symbology Research Center commercialized a new method of identifying products with invisible and virtually indestructible markings. This digital data matrix technology was developed at Marshall Space Flight Center to identify the millions of parts that comprise space shuttles. The laser-etched markings are seen as the next generation of product "bar codes."
Source record↗
Linear and nonlinear system identification of autonomic heart-rate modulation
No abstract available
NASA Discipline Regulatory Physiology↗
System identification of perilymphatic fistula in an animal model
An acute animal model has been developed in the chinchilla for the study of perilymphatic fistulas. Micropunctures were made in three sites to simulate bony, round window, and oval window fistulas. The eye movements in response to pressure applied to the external auditory canal were recorded after micropuncture induction and in preoperative controls. The main pressure stimulus was a pseudorandom binary sequence (PRBS) that rapidly changed between plus and minus 200 mm of water. The PRBS stimulus, with its wide frequency bandwidth, produced responses clearly above the preoperative baseline in 78 percent of the runs. The response was better between 0.5 and 3.3 Hz than it was below 0.5 Hz. The direction of horizontal eye movement was toward the side of the fistula with positive pressure applied in 92 percent of the runs. Vertical eye movements were also observed. The ratio of vertical eye displacement to horizontal eye displacement depended upon the site of the micropuncture induction. Thus, such a ratio measurement may be clinically useful in the noninvasive localization of perilymphatic fistulas in humans.
Non-NASA Center↗
Normal and abnormal tissue identification system and method for medical images such as digital mammograms
A system and method for analyzing a medical image to determine whether an abnormality is present, for example, in digital mammograms, includes the application of a wavelet expansion to a raw image to obtain subspace images of varying resolution. At least one subspace image is selected that has a resolution commensurate with a desired predetermined detection resolution range. A functional form of a probability distribution function is determined for each selected subspace image, and an optimal statistical normal image region test is determined for each selected subspace image. A threshold level for the probability distribution function is established from the optimal statistical normal image region test for each selected subspace image. A region size comprising at least one sector is defined, and an output image is created that includes a combination of all regions for each selected subspace image. Each region has a first value when the region intensity level is above the threshold and a second value when the region intensity level is below the threshold. This permits the localization of a potential abnormality within the image.
Heine, John J.↗
Transport Aircraft System Identification from Wind Tunnel Data
Recent studies have been undertaken to investigate and develop aerodynamic models that predict aircraft response in nonlinear unsteady flight regimes for transport configurations. The models retain conventional static and rotary dynamic terms but replace conventional acceleration terms with more general indicial functions. In the Integrated Resilient Aircraft Controls project of the NASA Aviation Safety Program one aspect of the research is to apply these current developments to transport configurations to facilitate development of advanced controls technology. This paper describes initial application of a more general modeling methodology to the NASA Langley Generic Transport Model, a sub-scale flight test vehicle.
Murphy, Patrick C.↗
Bruel and Kjaer 4944 Microphone Grid Frequency Response Function System Identification
Br el & Kjaer (B&K) 4944B pressure field microphone was judiciously selected to measure acoustic environments, 400Hz 50kHz, in close proximity of the nozzle during multiple firings of solid propellant rocket motors. It is well known that protective grids can affect the frequency response of microphones. B&K recommends operation of the B&K 4944B without a protective grid when recording measurements above 10 to 15 kHz.
Bennett, Reginald↗
Transport Aircraft System Identification Using Roll and Yaw Oscillatory Wind Tunnel Data
Continued studies have been undertaken to investigate and develop aerodynamic models that predict aircraft response in nonlinear unsteady flight regimes for transport configurations. The models retain conventional static and dynamic terms but replace conventional acceleration terms with indicial functions. In the Subsonic Fixed Wing Project of the NASA Fundamental Aeronautics Program and the Integrated Resilient Aircraft Controls project of the NASA Aviation Safety Program one aspect of the research is to apply these current developments to transport configurations to facilitate development of advanced simulation and control design technology. This paper continues development and application of a more general modeling methodology to the NASA Langley Generic Transport Model, a sub-scale flight test vehicle. In the present study models for the lateral-directional aerodynamics are developed.
Murphy, Patrick C.↗
System Identification and Uncertainty Quantification Using Orthogonal Excitations and the Semi-span Super Sonic Transport (S4T) Model
Orthogonal harmonic multisine excitations were utilized in a wind tunnel test and in simulation of the SemiSpan Supersonic Transport model to assess aeroservoelastic characteristics. Fundamental issues associated with analyzing sinusoidal signals were examined, including spectral leakage, excitation truncation, and uncertainties on frequency response functions and mean-square coherence. Simulation allowed for evaluation of these issues relative to a truth model, while wind tunnel data introduced real-world implementation issues.
Heeg, Jennifer↗
Sensor-Only System Identification for Structural Health Monitoring of Advanced Aircraft
Environmental conditions, cyclic loading, and aging contribute to structural wear and degradation, and thus potentially catastrophic events. The challenge of health monitoring technology is to determine incipient changes accurately and efficiently. This project addresses this challenge by developing health monitoring techniques that depend only on sensor measurements. Since actively controlled excitation is not needed, sensor-to-sensor identification (S2SID) provides an in-flight diagnostic tool that exploits ambient excitation to provide advance warning of significant changes. S2SID can subsequently be followed up by ground testing to localize and quantify structural changes. The conceptual foundation of S2SID is the notion of a pseudo-transfer function, where one sensor is viewed as the pseudo-input and another is viewed as the pseudo-output, is approach is less restrictive than transmissibility identification and operational modal analysis since no assumption is made about the locations of the sensors relative to the excitation.
Kukreja, Sunil L.↗
Neural Network Target Identification System for False Alarm Reduction
A multi-stage automated target recognition (ATR) system has been designed to perform computer vision tasks with adequate proficiency in mimicking human vision. The system is able to detect, identify, and track targets of interest. Potential regions of interest (ROIs) are first identified by the detection stage using an Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter combined with a wavelet transform. False positives are then eliminated by the verification stage using feature extraction methods in conjunction with neural networks. Feature extraction transforms the ROIs using filtering and binning algorithms to create feature vectors. A feed forward back propagation neural network (NN) is then trained to classify each feature vector and remove false positives. This paper discusses the test of the system performance and parameter optimizations process which adapts the system to various targets and datasets. The test results show that the system was successful in substantially reducing the false positive rate when tested on a sonar image dataset.
ATR↗