A Mission Based Fault Reconfiguration Framework for Spacecraft Applications

Abstract

We present a Markov Decision Process (MDP) framework for computing post-fault reconfiguration policies that are optimal with respect to a discounted cost. Our cost function penalizes states that are unsuitable to achieve the remaining objectives of the given mission. The cost function also penalizes states where the necessary goal achievement actions cannot be executed. We incorporate probabilities of missed detections and false alarms for a given fault condition into our cost to encourage the selection of policies that minimize the likelihood of incorrect reconfiguration. To illustrate the implementation of our proposed framework, we present an example inspired by the Far Ultraviolet Spectroscopic Explorer (FUSE) spacecraft with a mission to collect scientific data from 5 targets. Using this example, we also demonstrate that there is a design tradeoff between safe operation and mission completion. Simulation results are presented to illustrate and manage this tradeoff through the selection of optimization parameters.

Mission-Based Fault Reconfiguration for Spacecraft Applications

“…The science rewards for the targets are selected such that the reward for one target (number 3) is much higher than rewards for the other four targets. Details of the simulation case study have been published previously [5,6]. Results revealed a tradeoff between mission completion versus spacecraft safety based on tuning the reward function parameters from Eq.…”

“…In the simplified FUSE case study [5], the mission and reconfiguration problem specification required a state- states. This state-space size is formidable, particularly given that this model represents a "highly simplified" FUSE mission.…”

“…Exploitation of hierarchical domain structure will facilitate reconfiguration policy development for distinct subsystems. Table 1 Qualitative summary of optimal policy characteristics [5] Scenario…”

“…G is positive otherwise, with the highest value for the worst possible configuration with respect to safety. Because G is a defined over a set of discrete-valued state parameters, G is computed by table lookup rather than by an analytical function [5,6]. As shown in Eq.…”

Multi-Axis Inputs for Identification of a Reconfigurable Fixed-Wing UAV

“…The primary aim of the reconfiguration is to eliminate effects of the system failures; however, it may also be used in order to gain tactical advantage during air combat or for investigations performed in in-flight simulators. The reconfiguration is performed either by using flight controls that are not used in the particular flight phase [13,14], by modifying control laws [15][16][17][18] or by readjusting the mission plan [19,20]. In each of those approaches a valid mathematical model is required.…”