Vehicle health monitoring using stochastic constraint suspension

Abstract

Autonomous vehicle health monitoring (VHM) has been identified as a high priority technology for future space exploration in NASA's 2012 technology roadmap. Traditional VHM approaches are often designed for a specific application and are unable to detect and isolate a wide variety of faults. Proposed methods are often too computationally complex for NASA's manned flight software verification and validation (V&V) process. An innovative VHM algorithm is presented that addresses these weaknesses by integrating the constraint suspension technique with parity space and hypothesis testing. The approach relies on on-board sensor measurements, knowledge of control commands, and a modular mathematical system model to provide a VHM solution. Improvement over original constraint suspension is demonstrated using conceptual and numerical examples. Feasibility of the VHM method on a spacecraft is explored using a numerical simulation of a generic vehicle.