Incremental Sampling Algorithms for Robust Propulsion Control, Phase I

Metadata Updated: May 2, 2019

Aurora Flight Sciences proposes to develop a system for robust engine control based on incremental sampling, specifically Rapidly-Expanding Random Tree (RRT) algorithms. In this concept, the task of accelerating or decelerating the engine is treated as a path planning exercise. The control system actively searches for actuator inputs that allow the engine to traverse power settings without entering undesired regions of operation. The search is based on the sequential construction of control actions that satisfy feasibility constraints given the system dynamics. These algorithms have been proven to converge to the optimal solution through repeated iteration. RRTs allow for an efficient search of the solution space, reducing the computational expense of determining the best sequence of inputs with which to control the engine. This allows an efficient, online method for an engine to adapt and recalibrate to unexpected operational conditions.

Access & Use Information

Public: This dataset is intended for public access and use. License: U.S. Government Work

Downloads & Resources


Metadata Created Date August 1, 2018
Metadata Updated Date May 2, 2019

Metadata Source

Harvested from NASA Data.json

Additional Metadata

Resource Type Dataset
Metadata Created Date August 1, 2018
Metadata Updated Date May 2, 2019
Publisher Space Technology Mission Directorate
Unique Identifier TECHPORT_9115
Maintainer Email
Public Access Level public
Bureau Code 026:00
Metadata Context
Metadata Catalog ID
Schema Version
Catalog Describedby
Datagov Dedupe Retained 20190501230127
Harvest Object Id bc89f792-b9d0-4b87-9a6e-689bff1578e1
Harvest Source Id 39e4ad2a-47ca-4507-8258-852babd0fd99
Harvest Source Title NASA Data.json
Data First Published 2011-09-01
Homepage URL
Data Last Modified 2018-07-20
Program Code 026:027
Source Datajson Identifier True
Source Hash 0423f73b42a87a908ed072c7f3597540ed986a49
Source Schema Version 1.1

Didn't find what you're looking for? Suggest a dataset here.