Adaptive Trajectory Reshaping and Control System for RLVs (ATRC)

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8650-04-M-3428
Agency Tracking Number: F041-247-2299
Amount: $99,970.00
Phase: Phase I
Program: SBIR
Awards Year: 2004
Solicitation Year: 2004
Solicitation Topic Code: AF04-247
Solicitation Number: 2004.1
Small Business Information
KNOWLEDGE BASED SYSTEMS, INC.
1408 University Drive East, College Station, TX, 77840
DUNS: 555403328
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Ajay Verma
 Researcher
 (979) 260-5274
 averma@kbsi.com
Business Contact
 Donielle Mayer
Title: Business Operations Manager
Phone: (979) 260-5274
Email: dmayer@kbsi.com
Research Institution
N/A
Abstract
The ultimate goal of the proposed Adaptive Trajectory Reshaping and Control (ATRC) System is to develop technology that allows autonomous RLVs to avoid catastrophic failure when subjected to performance restricting damages and failures. The main focus in Phase-1 is to develop and demonstrate real time solution techniques for reconfigurable control and guidance system, including on-line reshaping of vehicle trajectories under uncertain damage/failure scenario. The proposed ATRC approach overcomes the challenges for determining online trajectories such as, determining a feasible trajectory solution that satisfies not only non-linear differential equations of motions of the vehicle, but also various end-point, in-flight and effectors saturation controls. A major outcome of this effort is the algorithm for a real time on-line trajectory generation application considering that the effort involves the solution of two-point boundary value problem for a non-flat (under-actuated) non-linear differential equation of motion. Our approach solves a set of algebraic equations, yet strictly satisfying the non-linear differential equations of a non-flat system. ATRC also dynamically estimates the uncertain aerodynamic coefficients and projects it along with corresponding probability distribution of expectation downstream. Another highlight of the proposal is optimization of trajectory provides a maximum margin in overcoming the downstream uncertainties in parameters.

* information listed above is at the time of submission.

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