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Integrated approach encompasses modeling, identification of parameters, estimation of states, and control. Applies to models specified in part by possibly interconnected elliptic partial differential equations for deflections of structures under static loads. Appropriate for systems in which time-dependent effects negligibleand where control forces applied without exciting significiant dynamic behavior.
The simulated microgravity environment aboard a KC-135 aircraft flying along a parabolic trajectory was used to study the ability of an autonomous space robot to grasp a freely translating and rotating object. Since the KC-135 cabin environment and the instrumentation for the Extravehicular Activity Helper/Retriever (EVAHR) do not provide a practical intertial reference frame, estimators based on the extended Kalman filter algorithm were used to model the relative translational dynamics of the KC-135 and the EVAHR. The estimator algorithms require intensive mathematical computation and therefore, i860 real-time. Estimator design, implementation concerns, and issues specific to the KC-135 environment are discussed and the architecture of the KC-135 translational state estimator is depicted.
An effective technique for real-time differential orbit determination with GPS bias fixing is formulated. With this technique, only real-time GPS orbits and clocks are needed (available from the NASA Global Differential GPS System with 10-20 cm accuracy). The onboard, realtime orbital states of user satellites (few meters in accuracy) are used for orbit initialization and integration. An extended Kalman filter is constructed for the estimation of the differential orbit between the two satellites as well as a reference orbit, together with their associating dynamics parameters. Due to close proximity of the two satellites and of similar body shapes, the differential dynamics are highly common and can be tightly constrained which, in turn, strengthens the orbit estimation. Without explicit differencing of GPS data, double-differenced phase biases are formed by a transformation matrix. Integer-valued fixing of these biases are then performed which greatly strengthens the orbit estimation. A 9-day demonstration between GRACE orbits with baselines of approx.200 km indicates that approx.80% of the double-differenced phase biases can successfully be fixed and the differential orbit can be determined to approx.7 mm as compared to the results of onboard K-band ranging.
The variable solar magnetic activity known as the 11-year solar cycle has the longest history of solar observations. These cycles dramatically affect conditions in the heliosphere and the Earth's space environment. Our current understanding of the physical processes that make up global solar dynamics and the dynamo that generates the magnetic fields is sketchy, resulting in unrealistic descriptions in theoretical and numerical models of the solar cycles. The absence of long-term observations of solar interior dynamics and photospheric magnetic fields hinders development of accurate dynamo models and their calibration. In such situations, mathematical data assimilation methods provide an optimal approach for combining the available observational data and their uncertainties with theoretical models in order to estimate the state of the solar dynamo and predict future cycles. In this presentation, we will discuss the implementation and performance of an Ensemble Kalman Filter data assimilation method based on the Parker migratory dynamo model, complemented by the equation of magnetic helicity conservation and longterm sunspot data series. This approach has allowed us to reproduce the general properties of solar cycles and has already demonstrated a good predictive capability for the current cycle, 24. We will discuss further development of this approach, which includes a more sophisticated dynamo model, synoptic magnetogram data, and employs the DART Data Assimilation Research Testbed.
The variable solar magnetic activity known as the 11-year solar cycle has the longest history of solar observations. These cycles dramatically affect conditions in the heliosphere and the Earth's space environment. Our current understanding of the physical processes that make up global solar dynamics and the dynamo that generates the magnetic fields is sketchy, resulting in unrealistic descriptions in theoretical and numerical models of the solar cycles. The absence of long-term observations of solar interior dynamics and photospheric magnetic fields hinders development of accurate dynamo models and their calibration. In such situations, mathematical data assimilation methods provide an optimal approach for combining the available observational data and their uncertainties with theoretical models in order to estimate the state of the solar dynamo and predict future cycles. In this presentation, we will discuss the implementation and performance of an Ensemble Kalman Filter data assimilation method based on the Parker migratory dynamo model, complemented by the equation of magnetic helicity conservation and long-term sunspot data series. This approach has allowed us to reproduce the general properties of solar cycles and has already demonstrated a good predictive capability for the current cycle, 24. We will discuss further development of this approach, which includes a more sophisticated dynamo model, synoptic magnetogram data, and employs the DART Data Assimilation Research Testbed.
The variability of solar magnetic activity known as the 11-‐year solar cycles has the longest history of observations. These solar cycles dramatically affect conditions in the heliosphere and the Earth’s space environment. Our current understanding of the physical processes that make up global solar dynamics and the dynamo that generates the magnetic fields is sketchy, resulting in unrealistic descriptions in theoretical and numerical models of the solar cycles. The absence of long-‐term observations of solar interior dynamics and photospheric magnetic fields hinders development of accurate dynamo models and their calibration. In such situations, mathematical data assimilation methods provide an optimal approach for combining the available observational data and their uncertainties with theoretical models in order to estimate the state of the solar dynamo and predict future cycles. In this presentation, we will discuss the implementation and performance of an Ensemble Kalman Filter data assimilation method based on the Parker migratory dynamo model complemented by the equation of magnetic helicity conservation and long-‐term sunspot data series. This approach has allowed us to reproduce the general properties of the solar cycles and has already demonstrated a good predictive capability for the current cycle, 24. We will discuss further development of this approach, which includes a more sophisticated dynamo model, synoptic magnetogram data, and employs the DART Data Assimilation Research Testbed.
A practical formulation is proposed for parameter estimation using the filter-error method, which is a maximum-likelihood estimator for dynamic systems having both process and measurement noise inputs. The novelty of the proposed formulation is that by accurately estimating the measurement noise covariance matrix using a time series analysis method, the remaining unknowns (which include the unknown parameters in the state-space matrices and the process noise covariance matrix) become decorrelated and can be estimated simultaneously in a straightforward manner. The approach is demonstrated using simulation data and flight test data from a subscale airplane. Results indicate that proposed algorithm can obtain accurate modeling results when both measurement noise and process noise are present in the data.
The Rectilinear Control System can be used to simulate a launch vehicle during liftoff. Several control schemes have been developed that can control different dynamic models of the rectilinear plant. A robust control algorithm was developed that can control a pendulum to maintain an inverted position. A fluid slosh tank will be attached to the pendulum in order to test robustness in the presence of unknown slosh characteristics. The rectilinear plant consists of a DC motor and three carts mounted in series. Each cart s weight can be adjusted with brass masses and the carts can be coupled with springs. The pendulum is mounted on the first cart and an adjustable air damper can be attached to the third cart if desired. Each cart and the pendulum have a quadrature encoder to determine position. Full state feedback was implemented in order to develop the control algorithm along with a state estimator to determine the velocity states of the system. A MATLAB program was used to convert the state space matrices from continuous time to discrete time. This program also used a desired phase margin and damping ratio to determine the feedback gain matrix that would be used in the LabVIEW program. This experiment will allow engineers to gain a better understanding of liquid propellant slosh dynamics, therefore enabling them to develop more robust control algorithms for launch vehicle systems
In-flight estimation of large structure model errors in order to detect inevitable deficiencies in large structure controller/estimator models is discussed. Such an estimation process is particularly applicable in the area of shape control system design required to maintain a prescribed static structural shape and, in addition, suppress dynamic disturbances due to the vehicle vibrational modes. The paper outlines a solution to the problem of static shape estimation where the vehicle shape must be reconstructed from a set of measurements discretely located throughout the structure. The estimation process is based on the principle of least-squares that inherently contains the definition and explicit computation of model error estimates that are optimal in some sense. Consequently, a solution is provided for the problem of estimation of static model errors (e.g., external loads). A generalized formulation applicable to distributed parameters systems is first worked out and then applied to a one-dimensional beam-like structural configuration.
Time series from biological system often display fluctuations in the measured variables. Much effort has been directed at determining whether this variability reflects deterministic chaos, or whether it is merely "noise". Despite this effort, it has been difficult to establish the presence of chaos in time series from biological sytems. The output from a biological system is probably the result of both its internal dynamics, and the input to the system from the surroundings. This implies that the system should be viewed as a mixed system with both stochastic and deterministic components. We present a method that appears to be useful in deciding whether determinism is present in a time series, and if this determinism has chaotic attributes, i.e., a positive characteristic exponent that leads to sensitivity to initial conditions. The method relies on fitting a nonlinear autoregressive model to the time series followed by an estimation of the characteristic exponents of the model over the observed probability distribution of states for the system. The method is tested by computer simulations, and applied to heart rate variability data.
The application of system identification methods to high-bandwidth rotorcraft flight control system design is examined. Flight test and modeling requirements are illustrated using flight test data from a BO-105 hingeless rotor helicopter. The proposed approach involves the identification of nonparametric frequency-response models followed by parametric (transfer funtion and state space) model identification. Results for the BO-105 show the need for including coupled body/rotor flapping and lead-lag dynamics in the identification model structure to allow the accurate prediction of control system bandwidth limitations. Lower-order models are useful for estimating nominal control system performance only when the flight data used for the identification are band-limited to be consistent with the frequency range of applicability of the model. The flight test results presented in this paper are consistent with theoretical studies by previous researchers.
This report is a compilation of PID (Proportional Integral Derivative) results for both longitudinal and lateral directional analysis that was completed during Fall 1994. It had earlier established that the maneuvers available for PID containing independent control surface inputs from OBES were not well suited for extracting the cross-coupling static (i.e., C(sub N beta)) or dynamic (i.e., C(sub Npf)) derivatives. This was due to the fact that these maneuvers were designed with the goal of minimizing any lateral directional motion during longitudinal maneuvers and vice-versa. This allows for greater simplification in the aerodynamic model as far as coupling between longitudinal and lateral directions is concerned. As a result, efforts were made to reanalyze this data and extract static and dynamic derivatives for the F/A-18 HARV (High Angle of Attack Research Vehicle) without the inclusion of the cross-coupling terms such that more accurate estimates of classical model terms could be acquired. Four longitudinal flights containing static PID maneuvers were examined. The classical state equations already available in pEst for alphadot, qdot and thetadot were used. Three lateral directional flights of PID static maneuvers were also examined. The classical state equations already available in pEst for betadot, p dot, rdot and phi dot were used. Enclosed with this document are the full set of longitudinal and lateral directional parameter estimate plots showing coefficient estimates along with Cramer-Rao bounds. In addition, a representative time history match for each type of meneuver tested at each angle of attack is also enclosed.
Quantitative diagnosis involves numerically estimating the values of unobservable parameters that best explain the observed parameter values. We consider quantitative diagnosis for continuous, lumped- parameter, steady-state physical systems because such models are easy to construct and the diagnosis problem is considerably simpler than that for corresponding dynamic models. To further tackle the difficulties of numerically inverting a simulation model to compute a diagnosis, we propose to decompose a physical system model in terms of feedback loops. This decomposition reduces the dimension of the problem and consequently decreases the diagnosis search space. We illustrate this approach on a model of thermal control system studied in earlier research.
The purpose of this effort was to advance the selection, characterization, and modeling of a propulsion electric grid for a Turboelectric Distributed Propulsion (TeDP) system for transport aircraft. The TeDP aircraft would constitute a miniature electric grid with 50 MW or more of total power, two or more generators, redundant transmission lines, and multiple electric motors driving propulsion fans. The study proposed power system architectures, investigated electromechanical and solid state circuit breakers, estimated the impact of the system voltage on system mass, and recommended DC bus voltage range. The study assumed an all cryogenic power system. Detailed assumptions within the study include hybrid circuit breakers, a two cryogen system, and supercritical cyrogens. A dynamic model was developed to investigate control and parameter selection.
We used one-way laser ranging data from International Laser Ranging Service (ILRS) ground stations to NASA's Lunar Reconnaissance Orbiter (LRO) for a demonstration of orbit determination. In the one-way setup, the state of LRO and the parameters of the spacecraft and all involved ground station clocks must be estimated simultaneously. This setup introduces many correlated parameters that are resolved by using a priori constraints. More over the observation data coverage and errors accumulating from the dynamical and the clock modeling limit the maximum arc length. The objective of this paper is to investigate the effect of the arc length, the dynamical and modeling accuracy and the observation data coverage on the accuracy of the results. We analyzed multiple arcs using lengths of 2 and 7 days during a one-week period in Science Mission phase 02 (SM02,November2010) and compared the trajectories, the post-fit measurement residuals and the estimated clock parameters. We further incorporated simultaneous passes from multiple stations within the observation data to investigate the expected improvement in positioning. The estimated trajectories were compared to the nominal LRO trajectory and the clock parameters (offset, rate and aging) to the results found in the literature. Arcs estimated with one-way ranging data had differences of 5-30 m compared to the nominal LRO trajectory. While the estimated LRO clock rates agreed closely with the a priori constraints, the aging parameters absorbed clock modeling errors with increasing clock arc length. Because of high correlations between the different ground station clocks and due to limited clock modeling accuracy, their differences only agreed at the order of magnitude with the literature. We found that the incorporation of simultaneous passes requires improved modeling in particular to enable the expected improvement in positioning. We found that gaps in the observation data coverage over 12h (approximately equals 6 successive LRO orbits) prevented the successful estimation of arcs with lengths shorter or longer than 2 or 7 days with our given modeling.
This paper presents an integrated reliability and physics-based risk modeling approach for assessing human spaceflight systems. The approach is demonstrated using an example, end-to-end risk assessment of a generic-crewed space transportation system during a reference mission to the International Space Station. The behavior of the system is modeled using analysis techniques from multiple disciplines in order to properly capture the dynamic time- and state- dependent consequences of failures encountered in different mission phases. We discuss how to combine traditional reliability analyses with Monte Carlo simulation methods and physics-based engineering models to produce loss-of- mission and loss-of-crew risk estimates supporting risk-based decision-making and requirement verification. This approach facilitates risk-informed design by providing more realistic representation of system failures and interactions; identifying key risk-driving sensitivities, dependencies, and assumptions; and tracking multiple figures of merit within a single, responsive assessment framework that can readily incorporate evolving design information throughout system development.
Soil moisture dynamics in the presence of dense vegetation canopies are determinants of ecosystem function and biogeochemical cycles, but the capability of existing spaceborne sensors to support reliable and useful estimates is not known. New results from a recently initiated field experiment in the northeast United States show that the NASA SMAP (Soil Moisture Active Passive) satellite is capable of retrieving soil moisture under temperate forest canopies. We present an analysis demonstrating that a parameterized emission model with the SMAP morning overpass brightness temperature resulted in a RMSD (root mean square difference) range of 0.047-0.057 m(exp 3)/m(exp 3) and a Pearson correlation range of 0.75-0.85 depending on the experiment location and the SMAP polarization. The inversion approach included a minimal amount of ancillary data. This result demonstrates unequivocally that spaceborne L-band radiometry is sensitive to soil moisture under temperate forest canopies, which has been uncertain because of lack of representative reference data.
The purpose of the GEODYN Orbit Determination and Parameter Estimation, the SOLVE and ERODYN Programs is to recover geodetic and geophysical parameters from satellite and other data in a state-of-the-art manner. Continued solutions for gravity field, pole positions, Earth rotation, GM, and baselines were made as part of the Crustal Dynamics Project. Some tidal parameters were recovered as well. The eight digit station identification number was incorporated in the software and new techniques for constraining monthly station parameters to each other are being developed. This is allowing the analysts even more flexibility in the shaping of solutions from monthly sets of normal equations and right-hand sides.