Comparative Study of Nonlinear Modal Modeling Identification Techniques from Force Appropriation Experiments
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Pacini, Benjamin Robert↗
SEARCH · Engineering Papers
Results for “model identification”
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Solar Sail Models and Test Measurements Correspondence for Validation Requirements Definition
Solar sails are being developed as a mission-enabling technology in support of future NASA science missions. Current efforts have advanced solar sail technology sufficient to justify a flight validation program. A primary objective of this activity is to test and validate solar sail models that are currently under development so that they may be used with confidence in future science mission development (e.g., scalable to larger sails). Both system and model validation requirements must be defined early in the program to guide design cycles and to ensure that relevant and sufficient test data will be obtained to conduct model validation to the level required. A process of model identification, model input/output documentation, model sensitivity analyses, and test measurement correspondence is required so that decisions can be made to satisfy validation requirements within program constraints.
Ewing, Anthony↗
Real-Time Adaptive Drag Minimization Wind Tunnel Investigation of a Flexible Wing with Variable Camber Continuous Trailing Edge Flap System
This paper reports the results of a recently completed real-time adaptive drag minimization wind tunnel investigation of a highly flexible wing wind tunnel model equipped with the Variable Camber Continuous Trailing Flap (VCCTEF) technology at the University of Washington Aeronautical Laboratory (UWAL). The wind tunnel investigation is funded by NASA SBIR Phase II contract with Scientific Systems Company, Inc. (SSCI) and University of Washington (UW) as a subcontractor. The wind tunnel model is a sub-scale Common Research Model (CRM) wing constructed of foam core and fiberglass skin and is aeroelastically scaled to achieve a wing tip deflection of 10% of the wing semi-span which represents a typical wing tip deflection for a modern transport such as Boeing 787. The jig-shape twist of the CRM wing is optimized using a CART3D aero-structural model to achieve the minimum induced drag for the design cruise lift coefficient of 0.5. The wing is equipped with two chord wise cambered segments for each of the six span wise flap sections for a total of 12 individual flap segments that comprise the VCCTEF system. Each of the 12 flap segments is actively controlled by an electric servo-actuator. The real-time adaptive drag optimization strategy includes an on-board aerodynamic model identification, a model excitation, and a real-time drag optimization. The on-board aerodynamic model is constructed parametrically as a function of the angle of attack and flap positions to model the lift and drag coefficients of the wing. The lift coefficient models include a linear model and a second-order model. The drag coefficient models include a quadratic model and a higher-order up to 6th-order model to accurately model the drag coefficient at high angles of attack. The onboard aerodynamic model identification includes a recursive least-squares (RLS) algorithm and a batch least-squares (BLS) algorithm designed to estimate the model parameters. The model excitation method is designed to sample the input set that comprises the angle of attack and the flap positions. Three model excitation methods are developed: random excitation method, sweep method, and iterative angle-of-attack seeking method. The real-time drag optimization includes a generic algorithm developed by SSCI and several optimization methods developed by NASA which include a second-order gradient Newton-Raphson optimization method, an iterative gradient optimization method, a pseudo-inverse optimization method, an analytical optimization method, and an iterative refinement optimization method. The first wind tunnel test entry took place in September 2017. This test revealed major hardware issues and required further redesign of the flap servo mechanisms. The second test entry took place in April 2018. However, the test was not successful due to the issues with the onboard aerodynamic model identification RLS algorithm which incorrectly identified model parameters. This test also provides an experimental comparison study between the VCCTEF and a variable camber discrete trailing edge flap (VCDTEF) without the elastomer transition mechanisms. The experimental result confirms the benefit of the VCCTEF which produces lower drag by 5% than the VCDTEF. The third and final test entry took place in June 2018 after the issues with the RLS algorithm have been identified and corrected. Additional improvements were implemented. These include the BLS algorithm, the iterative angle-of-attack seeking method, the iterative gradient optimization method, and the pseudo-inverse optimization method. The test objectives were successfully demonstrated as the real-time drag optimization identifies several optimal solutions at off-design lift coefficients. The iterative gradient optimization method is found to achieve up to 4.7% drag reduction for the off-design lift coefficient of 0.7. The pseudo-inverse optimization method which does not require the drag coefficient model is found to be quite effective in reducing drag. Up to 9.4% drag reduction for the off-design lift coefficient of 0.7 is achieved with the pseudo-inverse optimization method. The wind tunnel investigation demonstrates the potential of real-time drag optimization technology. Several new capabilities are developed that could enable future adaptive wing technologies for flexible wings equipped with drag control devices such as the VCCTEF.
Drag Optimization↗
DEPRECATED AI-Batt-OS (Autonomous Identification of Battery Life Models - Open Source) [SWR 21-17]
DEPRECATED. This repository was archived by the owner on Jun 30, 2026. It is now read-only. Open source implementation of some of the methods utilized by AI-Batt, a battery lifetime modeling and analysis toolkit provided by the National Laboratory of the Rockies (NLR). This software demonstrates the use of bi-level optimization and symbolic regression techniques to semi-autonomously identify algebraic models predicting the capacity fade of lithium-ion batteries during calendar aging. Modeling the degradation of batteries is a complex task, due to the difficulty in separating the time-dependent and time-independent factors impacting cell level degradation, across multiple data series with different numbers of measurements and/or data quality. Bi-level optimization enables model parameters to be optimized to either the entire data set or to individual data series, allowing statistical disambiguation of global behaviors (data series independent) and local behaviors (data series dependent). Symbolic regression is used to automatically search for optimal low-dimesional models predicting the variation of locally optimized parameters versus time-independent experimental variables from millions of possible models, resulting in a more accurate and repeatable model identification process than is possible by a manual search. The provided tools also implement cross-validation and bootstrap resampling schemes, empowering statistical model comparison/selection and quantification of model uncertainties. An example script replicates the results from the manuscript "Challenging Practices of Algebraic Battery Life Models through Statistical Validation and Model Identification via Machine-Learning", submitted to ECS. All code is written in MATLAB. Requires the Statistics and Machine Learning Toolbox. Contact Dr. Paul Gasper at Paul.Gasper@nlr.gov for any questions.
Gasper, Paul↗
Machine-Learning Assisted Identification of Accurate Battery Lifetime Models with Uncertainty
Reduced-order battery lifetime models, which consist of algebraic expressions for various aging modes, are widely utilized for extrapolating degradation trends from accelerated aging tests to real-world aging scenarios. Identifying models with high accuracy and low uncertainty is crucial for ensuring that model extrapolations are believable, however, it is difficult to compose expressions that accurately predict multivariate data trends; a review of cycling degradation models from literature reveals a wide variety of functional relationships. Here, a machine-learning assisted model identification method is utilized to fit degradation in a stand-out LFP-Gr aging data set, with uncertainty quantified by bootstrap resampling. The model identified in this work results in approximately half the mean absolute error of a human expert model. Models are validated by converting to a state-equation form and comparing predictions against cells aging under varying loads. Parameter uncertainty is carried forward into an energy storage system simulation to estimate the impact of aging model uncertainty on system lifetime. The new model identification method used here reduces life-prediction uncertainty by more than a factor of three (86% ± 5% relative capacity at 10 years for human-expert model, 88.5% ± 1.5% for machine-learning assisted model), empowering more confident estimates of energy storage system lifetime.
25 ENERGY STORAGE↗
Finite element model and identification procedure
Viewgraphs on finite element model and identification procedure are presented. Topics covered include: interferometer finite element model; testbed mode shapes; finite element model update; identification procedure; shaker locations; data analysis; modal frequency and damping comparison; computational procedure; fit comparison; residue analysis; typical residues; identification/FEM residual comparison; and pathlength control using isolation mounts.
How, Jonathan P.↗
Modeling, system identification, and control of ASTREX
The modeling, system identification and controller design aspects of the ASTREX precision space structure are presented in this work. Modeling of ASTREX is performed using NASTRAN, TREETOPS and I-DEAS. The models generated range from simple linear time-invariant models to nonlinear models used for large angle simulations. Identification in both the time and frequency domains are presented. The experimental set up and the results from the identification experiments are included. Finally, controller design for ASTREX is presented. Simulation results using this optimal controller demonstrate the controller performance. Finally the future directions and plans for the facility are addressed.
Abhyankar, Nandu S.↗
Identification of human response models in manual control systems.
Human response model identification in manual control system applied to study of stability and performance of man-machine system
FREQUENCY RANGE↗
A flight-test methodology for identification of an aerodynamic model for a V/STOL aircraft
Described is a flight test methodology for developing a data base to be used to identify an aerodynamic model of a vertical and short takeoff and landing (V/STOL) fighter aircraft. The aircraft serves as a test bed at Ames for ongoing research in advanced V/STOL control and display concepts. The flight envelope to be modeled includes hover, transition to conventional flight, and back to hover, STOL operation, and normaL cruise. Although the aerodynamic model is highly nonlinear, it has been formulated to be linear in the parameters to be identified. Motivation for the flight test methodology advocated in this paper is based on the choice of a linear least-squares method for model identification. The paper covers elements of the methodology from maneuver design to the completed data base. Major emphasis is placed on the use of state estimation with tracking data to ensure consistency among maneuver variables prior to their entry into the data base. The design and processing of a typical maneuver is illustrated.
Bach, Ralph E., Jr.↗
A flight-test methodology for identification of an aerodynamic model for a V/STOL aircraft
This paper describes a flight-test methodology for developing a data base to be used to identify an aerodynamic model of a V/STOL fighter aircraft. The aircraft serves as a test bed at NASA Ames for ongoing research in advanced V/STOL control and display concepts. The flight envelope to be modeled includes hover, transition to conventional flight and back to hover, STOL operation, and normal cruise. Although the aerodynamic model is highly nonlinear, it has been formulated to be linear in the parameters to be identified. Motivation for the flight-test methodology advocated in this paper is based on the choice of a linear least-squares method for model identification. The paper covers elements of the methodology from maneuver design to the completed data base. Major emphasis is placed on the use of state estimation with tracking data to ensure consistency among maneuver variables prior to their entry into the data base. The design and processing of a typical maneuver are illustrated.
Bach, R. E., Jr.↗
Alternatives for jet engine control
Tensor model order reduction, recursive tensor model identification, input design for tensor model identification, software development for nonlinear feedback control laws based upon tensors, and development of the CATNAP software package for tensor modeling, identification and simulation were studied. The last of these are discussed.
Sain, M. K.↗
Aero-Propulsive Damping Characterization for eVTOL Aircraft Using Free Motion Wind-Tunnel Testing
This paper describes an electric vertical takeoff and landing (eVTOL) aircraft system identification method applied using three degree-of-freedom (3DOF) free motion wind-tunnel testing. The approach, similar to flight-test system identification, allows for efficient mathematical model development of the aero-propulsive moments applied on an eVTOL vehicle, including aerodynamic damping effects. The approach is demonstrated using a subscale tiltrotor eVTOL aircraft mounted on a new 3DOF wind-tunnel apparatus. To execute the test, a model-based 3DOF control system is designed to track attitude commands and transition the aircraft based on the freestream dynamic pressure. While the flight controller is active, orthogonal phase-optimized multisine inputs are injected into the attitude command and control effector command signals to enable collection of informative data for model identification. Aero-propulsive models are then identified at several reference conditions in the transition flight envelope using the equation-error method in the frequency domain. The identified models are shown to have a good fit to the modeling data and good prediction capability of data not used for model identification. The method yields aerodynamic damping estimates using less wind-tunnel test time compared to traditional forced oscillation experiments and supplements static wind-tunnel testing to produce a comprehensive transition aero-propulsive model suitable for use in flight dynamics simulations.
RAVEN↗
Parameter identification and modeling of longitudinal aerodynamics
Using a comprehensive flight test database and a parameter identification software program produced at NASA Ames Research Center, a math model of the longitudinal aerodynamics of the Harrier aircraft was formulated. The identification program employed the equation error method using multiple linear regression to estimate the nonlinear parameters. The formulated math model structure adhered closely to aerodynamic and stability/control theory, particularly with regard to compressibility and dynamic manoeuvring. Validation was accomplished by using a three degree-of-freedom nonlinear flight simulator with pilot inputs from flight test data. The simulation models agreed quite well with the measured states. It is important to note that the flight test data used for the validation of the model was not used in the model identification.
Aksteter, J. W.↗
A Perspective on Traditional and Data Driven Electrochemical Modeling and Analysis
To understand the behavior of electrochemical systems, we need to reduce the dimensionality of the measured current-voltage-time (I-V-t) data by fitting models, thus enabling us to analyze and compare the governing physics. Traditionally, the process for this is an 'expert first' approach: defining the model and its explicit assumptions based on inductive reasoning or empirical observation, fitting small portions of the I-V-t data where assumptions are most valid or carefully designing experiments to enforce key assumptions, and then interpreting the model parameters. However, modern data-driven methods enable a new paradigm: a 'data first' approach, where the latent behaviors governing the system's measured response are identified directly using machine-learning models that optimize both model structure and parameters from the I-V-t data, guaranteeing that the learned model explains as much of the observed system response as possible. After model identification, the model can then be interrogated by an expert to connect observed behaviors with underlying physics. This talk will review several different types of electrochemical analysis (electrochemical impedance, differential voltage-capacity, electrochemical kinetics) and compare the traditional and data-driven methods for analyzing the data.
42 ENGINEERING↗
Modeling and Identification of a Large Gap Magnetic Suspension System
This paper presents the results of modeling and system identification efforts on the NASA Large-Angle Magnetic Suspension Test Fixture (LAMSTF). The LAMSTF consists of a cylindrical permanent magnet which is levitated above a planar array of five electromagnets mounted in a circular configuration. The analytical model is first developed and open-loop characteristics are described. The system is shown to be highly unstable and requires feedback control in order to apply system identification. Limitations on modeling accuracy due to the effect of eddy-currents on the system are discussed. An algorithm is derived to identify a state-space model for the system from input/output data acquired during closed-loop operation. The algorithm is tested on both the baseline system and a perturbed system which has an increased presence of eddy currents. It is found that for the baseline system the analytic model adequately captures the dynamics, although the identified model improves the simulation accuracy. For the system perturbed by additional unmodeled eddy-currents the analytic model is no longer adequate and a higher-order model, determined through system identification, is required to accurately predict the system's time response.
Cox, David E.↗
Some modern control techniques for human operator modeling and identification
Some recent results in quantitative modeling of the human operator are reviewed as well as a method for processing his input-output data. Modern control techniques are applied to the pilot operating in a dynamic compensatory tracking task. Trained pilots are known to behave in an optimal manner under fully stressed conditions. This has led to the development of an optimal control model of the human operator which takes his limitations into account. A rationale for determining the appropriate cost functions is determined. Experimental results indicate that the model can closely predict pilot performance in one- or two-axis tracking tasks. The model predicts pilot remnant (noise), error scores, and frequency response in both single-axis and multi-axis tracking tasks.
Shirley, R. S.↗
An eigensystem realization algorithm for modal parameter identification and model reduction
A method called the eigensystem realization algorithm is developed for modal parameter identification and model reduction of dynamic systems from test data. A new approach is introduced in conjunction with the singular-value decomposition technique to derive the basic formulation of minimum order realization which is an extended version of the Ho-Kalman algorithm. The basic formulation is then transformed into modal space for modal parameter identification. Two accuracy indicators are developed to quantitatively identify the system and noise modes. For illustration of the algorithm, an example is shown using experimental data from the Galileo spacecraft.
Juang, J.-N.↗