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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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NASA NTRS1991

Precise flight-path control using a predictive algorithm

Generalized predictive control describes an algorithm for the control of dynamic systems in which a control input is generated that minimizes a quadratic cost function consisting of a weighted sum of errors between desired and predicted future system output and future predicted control increments. The output predictions are obtained from an internal model of the plant dynamics. A design technique is discussed for applying the single-input/single-output generalized predictive control algorithm to a problem of longitudinal/vertical terrain-following flight of a rotorcraft. By using the generalized predictive control technique to provide inputs to a classically designed stability and control augmentation system, it is demonstrated that a robust flight-path control system can be created that exhibits excellent tracking performance.

Hess, R. A.↗

NASA NTRS2002

NWRA AVOSS Wake Vortex Prediction Algorithm

This report provides a detailed description of the wake vortex prediction algorithm used in the Demonstration Version of NASA's Aircraft Vortex Spacing System (AVOSS). The report includes all equations used in the algorithm, an explanation of how to run the algorithm, and a discussion of how the source code for the algorithm is organized. Several appendices contain important supplementary information, including suggestions for enhancing the algorithm and results from test cases.

Robins, R. E.↗

NASA NTRS2020

Development of a Non-Iterative Balance Load Prediction Algorithm for the NASA Ames Unitary Plan Wind Tunnel

A non-iterative load prediction algorithm for strain-gage balances was developed for the NASA Ames Unitary Plan Wind Tunnels that computes balance loads from the electrical outputs of the balance bridges and a set of state variables. A state variable could be, for example, a balance temperature difference or the bellows pressure of a flow-through balance. The algorithm directly uses regression models of the balance loads for the load prediction that were obtained by applying global regression analysis to balance calibration data. This choice greatly simplifies both implementation and use of the load prediction process for complex balance configurations as no load iteration needs to be performed. The regression model of a balance load is constructed by using terms from a total of nine term groups. Four term groups are derived from a Taylor Series expansion of the relationship between the load, gage outputs, and state variables. The remaining five term groups are defined by using absolute values of the gage outputs and state variables. Terms from these groups should only be included in the regression model if calibration data from a balance with known bi-directional outputs is analyzed. It is illustrated in detail how global regression analysis may be applied to obtain the coefficients of the chosen regression model of a load component assuming that no linear or massive near-linear dependencies between the regression model terms exist. Data from the machine calibration of a six-component force balance is used to illustrate both application and accuracy of the non-iterative load prediction process.

Ulbrich, Norbert M.↗

NASA NTRS1993

Development and evaluation of a predictive algorithm for telerobotic task complexity

There is a wide range of complexity in the various telerobotic servicing tasks performed in subsea, space, and hazardous material handling environments. Experience with telerobotic servicing has evolved into a knowledge base used to design tasks to be 'telerobot friendly.' This knowledge base generally resides in a small group of people. Written documentation and requirements are limited in conveying this knowledge base to serviceable equipment designers and are subject to misinterpretation. A mathematical model of task complexity based on measurable task parameters and telerobot performance characteristics would be a valuable tool to designers and operational planners. Oceaneering Space Systems and TRW have performed an independent research and development project to develop such a tool for telerobotic orbital replacement unit (ORU) exchange. This algorithm was developed to predict an ORU exchange degree of difficulty rating (based on the Cooper-Harper rating used to assess piloted operations). It is based on measurable parameters of the ORU, attachment receptacle and quantifiable telerobotic performance characteristics (e.g., link length, joint ranges, positional accuracy, tool lengths, number of cameras, and locations). The resulting algorithm can be used to predict task complexity as the ORU parameters, receptacle parameters, and telerobotic characteristics are varied.

Gernhardt, M. L.↗

NASA NTRS2012

Adaptive Trajectory Prediction Algorithm for Climbing Flights

Aircraft climb trajectories are difficult to predict, and large errors in these predictions reduce the potential operational benefits of some advanced features for NextGen. The algorithm described in this paper improves climb trajectory prediction accuracy by adjusting trajectory predictions based on observed track data. It utilizes rate-of-climb and airspeed measurements derived from position data to dynamically adjust the aircraft weight modeled for trajectory predictions. In simulations with weight uncertainty, the algorithm is able to adapt to within 3 percent of the actual gross weight within two minutes of the initial adaptation. The root-mean-square of altitude errors for five-minute predictions was reduced by 73 percent. Conflict detection performance also improved, with a 15 percent reduction in missed alerts and a 10 percent reduction in false alerts. In a simulation with climb speed capture intent and weight uncertainty, the algorithm improved climb trajectory prediction accuracy by up to 30 percent and conflict detection performance, reducing missed and false alerts by up to 10 percent.

Seperation Assurance↗

NASA NTRS1993

Predictive Algorithm For Aiming An Antenna

Method of computing control signals to aim antenna based on predictive control-and-estimation algorithm that takes advantage of control inputs. Conceived for controlling antenna in tracking spacecraft and celestial objects, near-future trajectories of which are known. Also useful in enhancing aiming performances of other antennas and instruments that track objects that move along fairly well known paths.

Gawronski, Wodek K.↗

NASA NTRS2011

A Universal Tare Load Prediction Algorithm for Strain-Gage Balance Calibration Data Analysis

An algorithm is discussed that may be used to estimate tare loads of wind tunnel strain-gage balance calibration data. The algorithm was originally developed by R. Galway of IAR/NRC Canada and has been described in the literature for the iterative analysis technique. Basic ideas of Galway's algorithm, however, are universally applicable and work for both the iterative and the non-iterative analysis technique. A recent modification of Galway's algorithm is presented that improves the convergence behavior of the tare load prediction process if it is used in combination with the non-iterative analysis technique. The modified algorithm allows an analyst to use an alternate method for the calculation of intermediate non-linear tare load estimates whenever Galway's original approach does not lead to a convergence of the tare load iterations. It is also shown in detail how Galway's algorithm may be applied to the non-iterative analysis technique. Hand load data from the calibration of a six-component force balance is used to illustrate the application of the original and modified tare load prediction method. During the analysis of the data both the iterative and the non-iterative analysis technique were applied. Overall, predicted tare loads for combinations of the two tare load prediction methods and the two balance data analysis techniques showed excellent agreement as long as the tare load iterations converged. The modified algorithm, however, appears to have an advantage over the original algorithm when absolute voltage measurements of gage outputs are processed using the non-iterative analysis technique. In these situations only the modified algorithm converged because it uses an exact solution of the intermediate non-linear tare load estimate for the tare load iteration.

balance calibration analysis↗

NASA NTRS2014

Initial Evaluations of LoC Prediction Algorithms Using the NASA Vertical Motion Simulator

Flying near the edge of the safe operating envelope is an inherently unsafe proposition. Edge of the envelope here implies that small changes or disturbances in system state or system dynamics can take the system out of the safe envelope in a short time and could result in loss-of-control events. This study evaluated approaches to predicting loss-of-control safety margins as the aircraft gets closer to the edge of the safe operating envelope. The goal of the approach is to provide the pilot aural, visual, and tactile cues focused on maintaining the pilot's control action within predicted loss-of-control boundaries. Our predictive architecture combines quantitative loss-of-control boundaries, an adaptive prediction method to estimate in real-time Markov model parameters and associated stability margins, and a real-time data-based predictive control margins estimation algorithm. The combined architecture is applied to a nonlinear transport class aircraft. Evaluations of various feedback cues using both test and commercial pilots in the NASA Ames Vertical Motion-base Simulator (VMS) were conducted in the summer of 2013. The paper presents results of this evaluation focused on effectiveness of these approaches and the cues in preventing the pilots from entering a loss-of-control event.

Pilot Cue↗

NASA NTRS2009

Rover Slip Validation and Prediction Algorithm

A physical-based simulation has been developed for the Mars Exploration Rover (MER) mission that applies a slope-induced wheel-slippage to the rover location estimator. Using the digital elevation map from the stereo images, the computational method resolves the quasi-dynamic equations of motion that incorporate the actual wheel-terrain speed to estimate the gross velocity of the vehicle. Based on the empirical slippage measured by the Visual Odometry software of the rover, this algorithm computes two factors for the slip model by minimizing the distance of the predicted and actual vehicle location, and then uses the model to predict the next drives. This technique, which has been deployed to operate the MER rovers in the extended mission periods, can accurately predict the rover position and attitude, mitigating the risk and uncertainties in the path planning on high-slope areas.

Yen, Jeng↗

NASA NTRS2004

Simulating Virtual Terminal Area Weather Data Bases for Use in the Wake Vortex Avoidance System (Wake VAS) Prediction Algorithm

During the research project, sounding datasets were generated for the region surrounding 9 major airports, including Dallas, TX, Boston, MA, New York, NY, Chicago, IL, St. Louis, MO, Atlanta, GA, Miami, FL, San Francico, CA, and Los Angeles, CA. The numerical simulation of winter and summer environments during which no instrument flight rule impact was occurring at these 9 terminals was performed using the most contemporary version of the Terminal Area PBL Prediction System (TAPPS) model nested from 36 km to 6 km to 1 km horizontal resolution and very detailed vertical resolution in the planetary boundary layer. The soundings from the 1 km model were archived at 30 minute time intervals for a 24 hour period and the vertical dependent variables as well as derived quantities, i.e., 3-dimensional wind components, temperatures, pressures, mixing ratios, turbulence kinetic energy and eddy dissipation rates were then interpolated to 5 m vertical resolution up to 1000 m elevation above ground level. After partial validation against field experiment datasets for Dallas as well as larger scale and much coarser resolution observations at the other 8 airports, these sounding datasets were sent to NASA for use in the Virtual Air Space and Modeling program. The application of these datasets being to determine representative airport weather environments to diagnose the response of simulated wake vortices to realistic atmospheric environments. These virtual datasets are based on large scale observed atmospheric initial conditions that are dynamically interpolated in space and time. The 1 km nested-grid simulated datasets providing a very coarse and highly smoothed representation of airport environment meteorological conditions. Details concerning the airport surface forcing are virtually absent from these simulated datasets although the observed background atmospheric processes have been compared to the simulated fields and the fields were found to accurately replicate the flows surrounding the airport where coarse verification data were available as well as where airport scale datasets were available.

Kaplan, Michael L.↗

NASA NTRS1993

Development, refinement, and testing of a short term solar flare prediction algorithm

Progress toward performance of the tasks and accomplishing the goals set forth in the two year Research Grant included primarily analysis of digital data sets and determination of methodology associated with the analysis of the very large, unique, and complex collection of digital solar magnetic field data. The treatment of each magnetogram as a unique set of data requiring special treatment was found to be necessary. It is determined that a person familiar with the data, the analysis system, and logical, coherent outcome of the analysis must conduct each analysis, and interact with the analysis program(s) significantly sometimes many iterations for successful calibration and analysis of the data set. With this interaction, the data sets yield valuable, coherent analyses. During this period, it was also decided that only data sets taken inside heliographic longitudes (Central Meridian Distance) East and West 30 degrees (within 30 degrees of the Central Meridian of the Sun). If the total data set is then found to be numerically inadequate for the final analysis, 30 - 45 degrees Central Meridian Distance data will then be analyzed. The Optical Data storage system (MSFC observatory) was found appropriate for use both in intermediate storage of the data (preliminary to analysis), and for storage of the analyzed data sets for later parametric extraction.

Smith, Jesse B., Jr.↗

NASA NTRS1993

Development, refinement, and testing of a short term solar flare prediction algorithm

During the period included in this report, the expenditure of time and effort, and progress toward performance of the tasks and accomplishing the goals set forth in the two year research grant proposal, consisted primarily of calibration and analysis of selected data sets. The heliographic limits of 30 degrees from central meridian were continued. As previously reported, all analyses are interactive and are performed by the Principal Investigator. It should also be noted that the analysis time involved by the Principal Investigator during this reporting period was limited, partially due to illness and partially resulting from other uncontrollable factors. The calibration technique (as developed by MSFC solar scientists), incorporates sets of constants which vary according to the wave length of the observation data set. One input constant is then varied interactively to correct for observing conditions, etc., to result in a maximum magnetic field strength (in the calibrated data), based on a separate analysis. There is some insecurity in the methodology and the selection of variables to yield the most self-consistent results for variable maximum field strengths and for variable observing/atmospheric conditions. Several data sets were analyzed using differing constant sets, and separate analyses to differing maximum field strength - toward standardizing methodology and technique for the most self-consistent results for the large number of cases. It may be necessary to recalibrate some of the analyses, but the sc analyses are retained on the optical disks and can still be used with recalibration where necessary. Only the extracted parameters will be changed.

Smith, Jesse B., Jr.↗

NASA NTRS2006

Optimized Algorithms for Prediction within Robotic Tele-Operative Interfaces

Robonaut, the humanoid robot developed at the Dexterous Robotics Laboratory at NASA Johnson Space Center serves as a testbed for human-robot collaboration research and development efforts. One of the primary efforts investigates how adjustable autonomy can provide for a safe and more effective completion of manipulation-based tasks. A predictive algorithm developed in previous work was deployed as part of a software interface that can be used for long-distance tele-operation. In this paper we provide the details of this algorithm, how to improve upon the methods via optimization, and also present viable alternatives to the original algorithmic approach. We show that all of the algorithms presented can be optimized to meet the specifications of the metrics shown as being useful for measuring the performance of the predictive methods. Judicious feature selection also plays a significant role in the conclusions drawn.

Martin, Rodney A.↗

NASA NTRS2007

A Robustly Stabilizing Model Predictive Control Algorithm

A model predictive control (MPC) algorithm that differs from prior MPC algorithms has been developed for controlling an uncertain nonlinear system. This algorithm guarantees the resolvability of an associated finite-horizon optimal-control problem in a receding-horizon implementation.

Ackmece, A. Behcet↗

NASA NTRS2020

Verification of Prognostic Algorithms to Predict Remaining Flying Time for Electric Unmanned Vehicles

This paper addresses the problem of building trust in the online prediction of a eUAV’s remaining available flying time powered by lithium-ion polymer batteries. A series of ground tests are described that make use of an electric unmanned aerial vehicle (eUAV) to verify the performance of remaining flying time predictions. The algorithm verification procedure described is implemented on a fully functional vehicle that is restrained to a platform for repeated run-to-functional-failure (charge depletion) experiments. The vehicle under test is commanded to follow a predefined propeller RPM profile in order to create battery demand profiles similar to those expected during flight. The eUAV is repeatedly operated until the charge stored in powertrain batteries falls below a specified limit threshold. The time at which the limit threshold on battery charge is crossed is then used to measure the accuracy of the remaining flying time prediction. In our earlier work battery aging was not included. In this work we take into account aging of the batteries where the parameters were updated to make predictions. Accuracy requirements are considered for an alarm that warns operators when remaining flying time is estimated to fall below the specified limit threshold.

Edward F. Hogge↗

NASA NTRS2005

Small Body GN&C Research Report: A Robust Model Predictive Control Algorithm with Guaranteed Resolvability

A robustly stabilizing MPC (model predictive control) algorithm for uncertain nonlinear systems is developed that guarantees the resolvability of the associated finite-horizon optimal control problem in a receding-horizon implementation. The control consists of two components; (i) feedforward, and (ii) feedback part. Feed-forward control is obtained by online solution of a finite-horizon optimal control problem for the nominal system dynamics. The feedback control policy is designed off-line based on a bound on the uncertainty in the system model. The entire controller is shown to be robustly stabilizing with a region of attraction composed of initial states for which the finite-horizon optimal control problem is feasible. The controller design for this algorithm is demonstrated on a class of systems with uncertain nonlinear terms that have norm-bounded derivatives, and derivatives in polytopes. An illustrative numerical example is also provided.

model predictive control algorithm (MPC)↗

NASA NTRS2007

LMI-Based Generation of Feedback Laws for a Robust Model Predictive Control Algorithm

This technical note provides a mathematical proof of Corollary 1 from the paper 'A Nonlinear Model Predictive Control Algorithm with Proven Robustness and Resolvability' that appeared in the 2006 Proceedings of the American Control Conference. The proof was omitted for brevity in the publication. The paper was based on algorithms developed for the FY2005 R&TD (Research and Technology Development) project for Small-body Guidance, Navigation, and Control [2].The framework established by the Corollary is for a robustly stabilizing MPC (model predictive control) algorithm for uncertain nonlinear systems that guarantees the resolvability of the associated nite-horizon optimal control problem in a receding-horizon implementation. Additional details of the framework are available in the publication.

algorithms↗

NASA NTRS1997

Simulation of the Predictive Control Algorithm for Container Crane Operation using Matlab Fuzzy Logic Tool Box

This research has investigated the use of fuzzy logic, via the Matlab Fuzzy Logic Tool Box, to design optimized controller systems. The engineering system for which the controller was designed and simulate was the container crane. The fuzzy logic algorithm that was investigated was the 'predictive control' algorithm. The plant dynamics of the container crane is representative of many important systems including robotic arm movements. The container crane that was investigated had a trolley motor and hoist motor. Total distance to be traveled by the trolley was 15 meters. The obstruction height was 5 meters. Crane height was 17.8 meters. Trolley mass was 7500 kilograms. Load mass was 6450 kilograms. Maximum trolley and rope velocities were 1.25 meters per sec. and 0.3 meters per sec., respectively. The fuzzy logic approach allowed the inclusion, in the controller model, of performance indices that are more effectively defined in linguistic terms. These include 'safety' and 'cargo swaying'. Two fuzzy inference systems were implemented using the Matlab simulation package, namely the Mamdani system (which relates fuzzy input variables to fuzzy output variables), and the Sugeno system (which relates fuzzy input variables to crisp output variable). It is found that the Sugeno FIS is better suited to including aspects of those plant dynamics whose mathematical relationships can be determined.

Richardson, Albert O.↗