Enhanced Constrained Predictive Control for Applications to Autonomous Vehicles and Missions
Abstract
Abstract The proposed research will advance Model Predictive Control (MPC) framework and capabilities for applications which involve autonomous vehicles, in general, and for spacecraft control applications, specifically. The major thrust areas are (i) computational enhancements to Constrained Nonlinear Model Predictive Control (CNMPC) algorithms to improve their feasibility for autonomous vehicle applications; (ii) applications of MPC and constrained control to combined translational and rotational spacecraft motion; (iii) developing MPC solutions for maneuvering autonomous networked spacecraft formations with debris avoidance. The project deliverables are project reports summarizing the main developments and research findings, and Matlab software. At least one conference publication will be developed.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jul 18, 2016
- Source ID
- FA94531510330
Entities
People
- Ilya V. Kolmanovsky
Organizations
- Air Force Research Laboratory
- Board of Regents of the University of Michigan
- United States Air Force