EXTENSIONS TO PREDICTIVE CONTROL AND PARAMETER GOVERNORS FOR APPLICATIONS TO AUTONOMOUS VEHICLES AND VEHICLE FORMATIONS"
Abstract
The proposed research will advance the Model Predictive Control (MPC) framework and its capabilities for applications to autonomous vehicles, in general, and to spacecraft control and coordinated control of spacecraft formations, in particular. Autonomous vehicles are increasingly in demand in a broadening range of societal applications in robotics, ground and marine transportation, aerial vehicles, and spacecraft missions. Autonomous vehicles are also appealing to general public as worthwhile research and technology development and demonstration platforms. Since the proposed research will advance the capabilities of autonomous vehicles, in general, and autonomous spacecraft, in particular, it is clearly relevant to the society and general public.Spacecraft, in particular, are essential to the public and to the society. The use of spacecraft includes (but is not limited) to commercial and defense communications, navigation, imaging, Earth/Sun observation, weather forecasting, space exploration, advancing science, and extending scientific and public knowledge. Spacecraft missions are complex to develop and operate, and they are also costly. Thus the capability of the spacecraft and spacecraft formations to autonomously make decisions, operate safely, avoid constraint violations, maintain near optimal use of on-board resources and have high reliability and resiliency are all of significant public benefit.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Oct 31, 2018
- Source ID
- FA94531610069
Entities
People
- Ilya V. Kolmanovsky
Organizations
- Air Force Research Laboratory
- Board of Regents of the University of Michigan
- United States Air Force