SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Averaging methods are applied to analyzing and optimizing the transient response associated with the direct adaptive control of an oscillatory second-order minimum-phase system. The analytical design methods developed for a second-order plant can be applied with some approximation to a MIMO flexible structure having a single dominant mode.
A document describes mathematical derivations and applications of autonomous guidance algorithms for maneuvering spacecraft in the vicinities of small astronomical bodies like comets or asteroids. These algorithms compute fuel- or energy-optimal trajectories for typical maneuvers by solving the associated optimal-control problems with relevant control and state constraints. In the derivations, these problems are converted from their original continuous (infinite-dimensional) forms to finite-dimensional forms through (1) discretization of the time axis and (2) spectral discretization of control inputs via a finite number of Chebyshev basis functions. In these doubly discretized problems, the Chebyshev coefficients are the variables. These problems are, variously, either convex programming problems or programming problems that can be convexified. The resulting discrete problems are convex parameter-optimization problems; this is desirable because one can take advantage of very efficient and robust algorithms that have been developed previously and are well established for solving such problems. These algorithms are fast, do not require initial guesses, and always converge to global optima. Following the derivations, the algorithms are demonstrated by applying them to numerical examples of flyby, descent-to-hover, and ascent-from-hover maneuvers.
The small laboratory computer is ideal for experimental control and data acquisition. Postexperimental data processing is often performed on large computers because of the availability of sophisticated programs, but costs and data compatibility are negative factors. Parameter optimization can be accomplished on the small computer, offering ease of programming, data compatibility, and low cost. A previously proposed random-search algorithm ('random creep') was found to be very slow in convergence. A method is proposed (the 'random leap' algorithm) which starts in a global search mode and automatically adjusts step size to speed convergence. A FORTRAN executive program for the random-leap algorithm is presented which calls a user-supplied function subroutine. An example of a function subroutine is given which calculates maximum-likelihood estimates of receiver operating-characteristic parameters from binary response data. Other applications in parameter estimation, generalized least squares, and matrix inversion are discussed.
Two algorithms were developed which utilized multi-sensor analysis techniques to complement the current Space Shuttle Main Engine (SSME) safety monitoring system. The first algorithm analyzed the accumulative error between actual and predicted values of the engine parameter set, while the second algorithm combined these error terms into a response pattern and correlated each pattern with a standard pattern. These algorithms were applied to twelve SSME anomalous test firings and were found to produce improved failure detection times in eight of those twelve compared to the current engine safety monitoring system. Of the eight detected anomalous test firings, the first algorithm detected all eight, while the second algorithm detected seven of the eight. No false alarms were indicated by either algorithm for twelve nominal test firings. An initial parametric study of these algorithms for optimized parameter selection is presented and algorithm robustness to sensor failure is demonstrated.
In this paper we propose a reinforcement learning (RL) algorithm for path planning of Unmanned Aviation Vehicles (UAVs) under varying wind conditions. Solutions to UAV path planning problems are becoming increasingly necessary as autonomous UAVs continue to enter commercial and government spaces. Path-planning is inherently challenging, as UAVs need to account for dynamically changing flying conditions such as weather, obstacle or no-fly zones, degraded vehicle health, and off-nominal battery power consumption. Machine learning methods such as reinforcement learning (RL) have the potential to revolutionize how vehicles navigate in such uncertain environments. In this study, we compute UAV trajectories from a pre-determined starting position to a target cell within a 7X7 grid environment by optimizing parameters for mission assurance and safety limits in addition to the energy consumption and operation time. The UAV navigates the grid by taking actions to move in any of the eight cardinal and inter-cardinal directions, under constant thrust profile. The resultant UAV state is sampled from a probability distribution which accounts for the UAV’s action, local wind velocity, and the presence of obstacles or boundaries. As the unmanned airspace gets more complex due to multiple vehicles and environmental uncertainties, trade-offs between energy consumption, operation time, risk tolerance, and mission assurance need to be made. Our Markov Decision Process (MDP) environment model can capture any combination of these in the optimization objective, making it novel compared to other work in the field.
The development and application of a quasi-Newton gradient search procedure for identifying independent pilot related parameters of the optimal control model for pilot/vehicle systems is reported. A sensitivity analysis procedure which determines whether a given model parameter is required to match a specific experimental result, and which experimentally induced parameter changes are required to account for behavioral and performance differences, is described. Application of the identification scheme to training effects in a manual control task is described.
Continuous parameter optimization techniques applied to synthesis of model of human operators in simple two-axis manual control system
Effect of low temperature environment on storage of fuel cell power supply - computer program for fuel cell system parameter optimization - mathematical model of static moisture removal
Parameter optimization, and operation of coaxial plasma injector
Optimal parameters of double-focusing mass spectrograph for measuring solar wind mass/ charge number composition
Solar electric propulsion Mars high data rate orbiter trajectory design and analysis, including landing site selection and spacecraft parameters optimization
Fifth-order pseudo-Runge-Kutta methods for numerical solution of ordinary differential equations, determining optimal parameters
Calculations of GaAs solar cell output parameters were refined and a computer model was developed for parameter optimization. The results were analyzed to determine the material characteristics required for a high efficiency solar cell. Calculated efficiencies for a P/N cell polarity are higher than an N/P cell. Both cell polarities show efficiency to have a larger dependence on short circuit current than an open circuit voltage under nearly all conditions considered. The tolerances and requirements of a cell fabrication process are more critical for an N/P type than for a P/N type cell. Several solar cell fabrication considerations relative to junction formation using ion implantation are also discussed.
The curriculum of a short course in remote sensing and parameter optimization is presented. The subjects discussed are: (1) basics of remote sensing and the user community, (2) multivariant spectral analysis, (3) advanced mathematics and physics of remote sensing, (4) the atmospheric environment, (5) imaging sensing, and (6)nonimaging sensing. Mathematical models of optimization techniques are developed.
Two general purpose numerical integration schemes were built into the NASA-JSC computer system. The state-of-the-art of numerical integration, the particular integrators built into the JSC computer system, and the use of the new integration packages are described. Background information about numerical integration and the variable-order, variable-stepsize Adams numerical integration technique is discussed. Results concerning the PEACE parameter optimization program are given along with recommendations and conclusions.
A new mean-square-error automatic equalizer for synchronous data transmission is developed. It utilizes Rosen's gradient-projection method to optimize parameters in the discrete frequency domain. The algorithm converges (in the mean) for any channel even in the presence of noise. It is shown that for the channels considered, convergence is faster (in a bounded sense) than for comparable time-domain equalizers.
In the search technique presented, the number of function evaluations is reduced by making use of the derivative of the function. The search method is combined with the variable metric algorithm reported by Davidon (1959) to solve a 17-parameter optimal atmospheric flight of a space shuttle vehicle. In the case of the problem, the proposed method requires one or two function evaluations per iteration less than the standard cubic fit-golden section method.