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NASA NTRS1994

An overview of recent advances in system identification

This paper presents an overview of the recent advances in system identification for modal testing and control of large flexible structures. Several techniques are discussed including the Observer/Kalman Filter Identification, the Observer/Controller Identification, and the State-Space System Identification in the Frequency Domain. The System/Observer/Controller Toolbox developed at NASA Langley Research Center is used to show the applications of these techniques to real aerospace structures such as the Hubble spacecraft telescope and the active flexible aircraft wing.

Juang, Jer-Nan↗

NASA NTRS1993

Identification of a second-order mechanical system model from a state space realization

Many system identification algorithms produce models in modern state variable form. When a mechanical system is identified one knows that there must be a model in second order form. In structural dynamics one is interested in either a modal model of the system, or in a model including a mass matrix, stiffness matrix and a damping matrix. In this paper, algorithms are developed that convert a modern state space realization into the above two representations of interest in mechanical systems. The algorithm can be used with any identification methods that produce a modern state variable representation such as the Eigensystem Realization Algorithm (ERA), its modified version by data correlation (ERA/DC), and combinations of these with Observer/Kalman Filter Identification (OKID). An algorithm is developed that allows one to identify the damping matrix in a model representation, so that one determines how near to modal the damping is in the system, and can therefore understand the degree to which the modes are coupled by the damping in the system. A second algorithm produced the mass, damping, and stiffness matrices, given the input and output matrices. It is assumed that the number of sensors (or the number of actuators) is greater than or equal to the number of modes in the system, and it is proved that this is a necessary condition to be able to uniquely identify these matrices. Experience with examples suggests that a singular value truncation involved in these algorithms can help one determine the true system order. This truncation has the benefit of knowledge of the correct form for a mechanical system model, which is not present in the modern control identification algorithm.

Tseng, Dong-Huei↗

NASA NTRS1996

Comparison of System Identification Techniques for the Hydraulic Manipulator Test Bed (HMTB)

In this thesis linear, dynamic, multivariable state-space models for three joints of the ground-based Hydraulic Manipulator Test Bed (HMTB) are identified. HMTB, housed at the NASA Langley Research Center, is a ground-based version of the Dexterous Orbital Servicing System (DOSS), a representative space station manipulator. The dynamic models of the HMTB manipulator will first be estimated by applying nonparametric identification methods to determine each joint's response characteristics using various input excitations. These excitations include sum of sinusoids, pseudorandom binary sequences (PRBS), bipolar ramping pulses, and chirp input signals. Next, two different parametric system identification techniques will be applied to identify the best dynamical description of the joints. The manipulator is localized about a representative space station orbital replacement unit (ORU) task allowing the use of linear system identification methods. Comparisons, observations, and results of both parametric system identification techniques are discussed. The thesis concludes by proposing a model reference control system to aid in astronaut ground tests. This approach would allow the identified models to mimic on-orbit dynamic characteristics of the actual flight manipulator thus providing astronauts with realistic on-orbit responses to perform space station tasks in a ground-based environment.

Morris, A. Terry↗

NASA NTRS2003

Method and Apparatus for Reading Two Dimensional Identification Symbols Using Radar Techniques

A method and apparatus are provided for sensing two-dimensional identification marks provided on a substrate or embedded within a substrate below a surface of the substrate. Micropower impulse radar is used to transmit a high risetime, short duration pulse to a focussed radar target area of the substrate having the two dimensional identification marks. The method further includes the steps of listening for radar echoes returned from the identification marks during a short listening period window occurring a predetermined time after transmission of the radar pulse. If radar echoes are detected, an image processing step is carried out. If no radar echoes are detected, the method further includes sequentially transmitting further high risetime, short duration pulses, and listening for radar echoes from each of said further pulses after different elapsed times for each of the further pulses until radar echoes are detected. When radar echoes are detected, data based on the detected echoes is processed to produce an image of the identification marks.

Harry F Schramm Jr.↗

NASA NTRS2003

System Identification of X-33 Neural Network

Modern flight control research has improved spacecraft survivability as its goal. To this end we need to have a failure detection system on board. In case the spacecraft is performing imperfectly, reconfiguration of control is needed. For that purpose we need to have parameter identification of spacecraft dynamics. Parameter identification of a system is called system identification. We treat the system as a black box which receives some inputs that lead to some outputs. The question is: what kind of parameters for a particular black box can correlate the observed inputs and outputs? Can these parameters help us to predict the outputs for a new given set of inputs? This is the basic problem of system identification. The X33 was supposed to have the onboard capability of evaluating the current performance and if needed to take the corrective measures to adapt to desired performance. The X33 is comprised of both rocket and aircraft vehicle design characteristics and requires, in general, analytical methods for evaluating its flight performance. Its flight consists of four phases: ascent, transition, entry and TAEM (Terminal Area Energy Management). It spends about 200 seconds in ascent phase, reaching an altitude of about 180,000 feet and a speed of about 10 to 15 Mach. During the transition phase which lasts only about 30 seconds, its altitude may increase to about 190,000 feet but its speed is reduced to about 9 Mach. At the beginning of this phase, the Main Engine is Cut Off (MECO) and the control is reconfigured with the help of aerosurfaces (four elevons, two flaps and two rudders) and reaction control system (RCS). The entry phase brings down the altitude of X33 to about 90,000 feet and its speed to about Mach 3. It spends about 250 seconds in this phase. Main engine is still cut off and the vehicle is controlled by complex maneuvers of aerosurfaces. The last phase TAEM lasts for about 450 seconds and the altitude and speed, both are reduced to zero. The present attempt, as a start, focuses only on the entry phase. Since the main engine remains cut off in this phase, there is no thrust acting on the system. This considerably simplifies the equations of motion. We introduce another simplification by assuming the system to be linear after some non-linearities are removed analytically from our consideration. Under these assumptions, the problem could be solved by Classical Statistics by employing the least sum of squares approach. Instead we chose to use the Neural Network method. This method has many advantages. It is modern, more efficient, can be adapted to work even when the assumptions are diluted. In fact, Neural Networks try to model the human brain and are capable of pattern recognition.

Aggarwal, Shiv↗

NASA NTRS2008

Non-Linear System Identification for Aeroelastic Systems with Application to Experimental Data

Representation and identification of a non-linear aeroelastic pitch-plunge system as a model of the NARMAX class is considered. A non-linear difference equation describing this aircraft model is derived theoretically and shown to be of the NARMAX form. Identification methods for NARMAX models are applied to aeroelastic dynamics and its properties demonstrated via continuous-time simulations of experimental conditions. Simulation results show that (i) the outputs of the NARMAX model match closely those generated using continuous-time methods and (ii) NARMAX identification methods applied to aeroelastic dynamics provide accurate discrete-time parameter estimates. Application of NARMAX identification to experimental pitch-plunge dynamics data gives a high percent fit for cross-validated data.

Kukreja, Sunil L.↗

NASA NTRS2008

Nonlinear System Identification for Aeroelastic Systems with Application to Experimental Data

Representation and identification of a nonlinear aeroelastic pitch-plunge system as a model of the Nonlinear AutoRegressive, Moving Average eXogenous (NARMAX) class is considered. A nonlinear difference equation describing this aircraft model is derived theoretically and shown to be of the NARMAX form. Identification methods for NARMAX models are applied to aeroelastic dynamics and its properties demonstrated via continuous-time simulations of experimental conditions. Simulation results show that (1) the outputs of the NARMAX model closely match those generated using continuous-time methods, and (2) NARMAX identification methods applied to aeroelastic dynamics provide accurate discrete-time parameter estimates. Application of NARMAX identification to experimental pitch-plunge dynamics data gives a high percent fit for cross-validated data.

Kukreja, Sunil L.↗

NASA NTRS2004

Passive IFF: Autonomous Nonintrusive Rapid Identification of Friendly Assets

A proposed optoelectronic instrument would identify targets rapidly, without need to radiate an interrogating signal, apply identifying marks to the targets, or equip the targets with transponders. The instrument was conceived as an identification, friend or foe (IFF) system in a battlefield setting, where it would be part of a targeting system for weapons, by providing rapid identification for aimed weapons to help in deciding whether and when to trigger them. The instrument could also be adapted to law-enforcement and industrial applications in which it is necessary to rapidly identify objects in view. The instrument would comprise mainly an optical correlator and a neural processor (see figure). The inherent parallel-processing speed and capability of the optical correlator would be exploited to obtain rapid identification of a set of probable targets within a scene of interest and to define regions within the scene for the neural processor to analyze. The neural processor would then concentrate on each region selected by the optical correlator in an effort to identify the target. Depending on whether or not a target was recognized by comparison of its image data with data in an internal database on which the neural processor was trained, the processor would generate an identifying signal (typically, friend or foe ). The time taken for this identification process would be less than the time needed by a human or robotic gunner to acquire a view of, and aim at, a target. An optical correlator that has been under development for several years and that has been demonstrated to be capable of tracking a cruise missile might be considered a prototype of the optical correlator in the proposed IFF instrument. This optical correlator features a 512-by-512-pixel input image frame and operates at an input frame rate of 60 Hz. It includes a spatial light modulator (SLM) for video-to-optical image conversion, a pair of precise lenses to effect Fourier transforms, a filter SLM for digital-to-optical correlation-filter data conversion, and a charge-coupled device (CCD) for detection of correlation peaks. In operation, the input scene grabbed by a video sensor is streamed into the input SLM. Precomputed correlation-filter data files representative of known targets are then downloaded and sequenced into the filter SLM at a rate of 1,000 Hz. When there occurs a match between the input target data and one of the known-target data files, the CCD detects a correlation peak at the location of the target. Distortion- invariant correlation filters from a bank of such filters are then sequenced through the optical correlator for each input frame. The net result is the rapid preliminary recognition of one or a few targets.

Moynihan, Philip↗

NASA NTRS2012

The Impact of Early Design Phase Risk Identification Biases on Space System Project Performance

Risk identification during the early design phases of complex systems is commonly implemented but often fails to result in the identification of events and circumstances that truly challenge project performance. Inefficiencies in cost and schedule estimation are usually held accountable for cost and schedule overruns, but the true root cause is often the realization of programmatic risks. A deeper understanding of frequent risk identification trends and biases pervasive during space system design and development is needed, for it would lead to improved execution of existing identification processes and methods.

Reeves, John D., Jr.↗

NASA NTRS2017

Identification of Fungal Colonies on Ground Control and Flight Veggie Plant Pillows

The Veggie system focuses on growing fresh produce that can be harvested and consumed by astronauts. The microbial colonies in each Veggie experiment are evaluated to determine the safety level of the produce and then differences between flight and ground samples. The identifications of the microbial species can detail risks or benefits to astronaut and plant health. Each Veggie ground or flight experiment includes six plants grown from seeds that are glued into wicks in Teflon pillows filled with clay arcillite and fertilizer. Fungal colonies were isolated from seed wicks, growth media, and lettuce (cv. 'Outredgeous') roots grown in VEG-01B pillows on ISS and in corresponding ground control pillows grown in controlled growth chambers. The colonies were sorted by morphology and identified using MicroSeq(TM) 500 16s rDNA Bacterial Identification System and BIOLOG GEN III MicroPlate(TM). Health risks for each fungal identification were then assessed using literature sources. The goal was to identify all the colonies isolated from flight and ground control VEG-01B plants, roots, and rooting medium and compare the resulting identifications.

Scotten, Jessica E.↗

NASA NTRSDate not supplied

A Dynamic PCA and Machine Learning Tool for Automated Identification of Solar Wind Disturbances Impacting Earth’s Magnetosphere

Earth’s magnetosphere is continuously impacted by solar wind and interplanetary magnetic field (IMF) disturbances, such as shocks, discontinuities, magnetic clouds and more. Understanding how such disturbances propagate from the Sun and what is their impact on the different magnetospheric domains is key to understanding and forecasting energy transfer from the solar wind to Earth. The large number of overlapping solar wind and magnetospheric missions carrying magnetometers and the recent advances in communications and data storage technologies have enabled an unprecedented quantity of high-fidelity magnetic field data captured by in-situ spacecraft to be available at the click of a button. However, this massive quantity of available data can prove unwieldy for researchers, limiting the identification of interesting phenomena and disturbances to a relatively small percentage of the total dataset. Several techniques have been previously developed for automated identification of specific types of magnetic anomalies, but these methods are typically mission-specific and can be difficult to generalize. We present initial results for a generic method of automated anomaly detection in magnetic field measurements based on dimensionality reduction and unsupervised clustering via machine learning. The benefit of our technique is its high degree of generalizability and flexibility which make it a most useful data survey tool for a wide range of magnetic field datasets. This method can also be applied simultaneously to other observed time-series properties like plasma density, pressure, and velocity for more accurate event identification. Additionally, the application of this method to data captured by multiple spacecraft enables the simultaneous identification of disturbances and the determination of their propagation characteristics. Initial evaluation of this technique has been performed using data from Magnetospheric MultiScale (MMS) and THEMIS-ARTEMIS missions, providing a testbed scenario for the future Heliophysics Environmental and Radiation Measurement Experiment Suite (HERMES) platform instruments that will measure solar wind and IMF properties from lunar orbit onboard the Gateway station.

Miguel Martinez-Ledesma↗

NASA NTRSDate not supplied

A Dynamic PCA and Machine Learning Tool for Automated Identification of Solar Wind Disturbances Impacting Earth’s Magnetosphere

Miguel Martinez-Ledesma↗

NASA NTRS2024

Harnessing Collaborative Learning Automata to Guide Multi-objective Optimization based Inverse Analysis for Structural Damage Identification

Structural damage identification based on physical models is often transformed into an optimization problem that minimizes the difference between measurement information of structure being monitored and the model prediction in the parametric space. However, the objective function in this context often exhibits multimodality, involving high-dimensional variables due to the reliance on finite element models for damage identification. These features pose challenges to optimization algorithms, where entrapment in local solutions can lead to false positives and false negatives in damage identification. In this research, we propose a reinforcement learning based multi-swarm optimizer to tackle such challenges in pursuit of a small yet diverse solution set that can capture the true damage scenario as one of the solutions. The proposed method leverages the flexibility of the particle swarm optimizer and incorporates novel strategies of metaheuristics to realize targeted improvement. To enable the particle swarm to adaptively select the appropriate search strategy based on the current environment, we adopt the learning automata technique, which sidesteps the need for reward strategy selection that is usually ad hoc at each step of the search. The integration harnesses the automatic learning and self-adaptation capabilities of learning automata, enabling the particles to navigate based on environmental signals. This leads to accumulated probabilities tied to advantageous movements, fostering an adaptive exploration of particles in the search space. The proposed approach is first validated through implementing into benchmark test cases with comparisons. It is then applied to structural damage identification with piezoelectric admittance experimental signals. `The results highlight the capability of the algorithm to identify a small solution set with high accuracy to match the actual damage scenario.

Yang Zhang↗

NASA NTRS2010

Identification and Classification of Common Risks in Space Science Missions

Due to the highly constrained schedules and budgets that NASA missions must contend with, the identification and management of cost, schedule and risks in the earliest stages of the lifecycle is critical. At the Jet Propulsion Laboratory (JPL) it is the concurrent engineering teams that first address these items in a systematic manner. Foremost of these concurrent engineering teams is Team X. Started in 1995, Team X has carried out over 1000 studies, dramatically reducing the time and cost involved, and has been the model for other concurrent engineering teams both within NASA and throughout the larger aerospace community. The ability to do integrated risk identification and assessment was first introduced into Team X in 2001. Since that time the mission risks identified in each study have been kept in a database. In this paper we will describe how the Team X risk process is evolving highlighting the strengths and weaknesses of the different approaches. The paper will especially focus on the identification and classification of common risks that have arisen during Team X studies of space based science missions.

Risk Identification↗

NASA NTRS2016

Identification and Reconfigurable Control of Impaired Multi-Rotor Drones

The paper presents an algorithm for control and safe landing of impaired multi-rotor drones when one or more motors fail simultaneously or in any sequence. It includes three main components: an identification block, a reconfigurable control block, and a decisions making block. The identification block monitors each motor load characteristics and the current drawn, based on which the failures are detected. The control block generates the required total thrust and three axis torques for the altitude, horizontal position and/or orientation control of the drone based on the time scale separation and nonlinear dynamic inversion. The horizontal displacement is controlled by modulating the roll and pitch angles. The decision making algorithm maps the total thrust and three torques into the individual motor thrusts based on the information provided by the identification block. The drone continues the mission execution as long as the number of functioning motors provide controllability of it. Otherwise, the controller is switched to the safe mode, which gives up the yaw control, commands a safe landing spot and descent rate while maintaining the horizontal attitude.

Unmanned aerial system↗

NASA NTRSDate not supplied

System Identification for eVTOL Aircraft Using Simulated Flight Data

This paper describes a system identification method for electric vertical takeoff and landing (eVTOL) aircraft. The approach merges fixed-wing and rotary-wing modeling techniques with new strategies to develop a modeling method for eVTOL vehicles using flight test data. The eVTOL aircraft system identification approach is demonstrated through application to the NASA LA-8 tandem tilt-wing, distributed electric propulsion aircraft using a high-fidelity flight dynamics simulation. Orthogonal phase-optimized multisine inputs are applied to each control surface and propulsor at numerous flight conditions throughout the flight envelope to collect informative flight data. An aero-propulsive model is identified at each flight condition using the equation-error method in the frequency domain. The local model parameters are then blended to create a global model across the nominal flight envelope. Parameter estimation results are shown to provide a good fit to modeling data and have good prediction capability. The methodology is developed with a discussion of unique eVTOL vehicle aerodynamic characteristics and practical strategies intended to inform future flight-based system identification efforts for eVTOL aircraft.

system identification↗

NASA NTRS1972

Optimal inputs for system identification

Identification criteria are presented for linear dynamic systems with and without process noise. With process noise, the state equations are replaced by the Kalman filter equations. If the identification performance index is expanded in a Taylor's series with respect to the parameters to be identified, then maximizing the weighting factor of the quadratic term with respect to the inputs will insure that an identification algorithm will converge more rapidly and to a more accurate result than with nonoptimal inputs. The expectation of this weighting factor is the Fisher information matrix, and its inverse is a lower bound for the covariance of the parameters. Direct and indirect methods of calculating the information matrix are presented for systems with and without process noise.

Reid, D. B.↗

NASA NTRS1972

Methods and application of system identification in shock and vibration.

A logical picture is presented of current useful system identification techniques in the shock and vibration field. A technology tree diagram is developed for the purpose of organizing and categorizing the widely varying approaches according to the fundamental nature of each. Specific examples of accomplished activity for each identification category are noted and discussed. To provide greater insight into the most current trends in the system identification field, a somewhat detailed description is presented of the essential features of a recently developed technique that is based on making the maximum use of all statistically known information about a system.

Collins, J. D.↗