A Mission Based Fault Reconfiguration Framework for Spacecraft Applications

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.

[1]  Tad Hogg,et al.  Multiagent control of self-reconfigurable robots , 2002, Artif. Intell..

[2]  et al,et al.  Overview of the Far Ultraviolet Spectroscopic Explorer Mission , 2000, astro-ph/0005529.

[3]  Youmin Zhang,et al.  Bibliographical review on reconfigurable fault-tolerant control systems , 2003, Annu. Rev. Control..

[4]  Raman K. Mehra,et al.  Failure detection and identification and fault tolerant control using the IMM-KF with applications to the Eagle-Eye UAV , 1998, Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171).

[5]  S L Britain,et al.  Automatic reconfiguration of a robotic arm using a multi-agent approach , 2008 .

[6]  P. Pandurang Nayak,et al.  A Model-Based Approach to Reactive Self-Configuring Systems , 1996, AAAI/IAAI, Vol. 2.

[7]  S. M. Veres,et al.  A Multi-Agent Approach to Integrated FDI & Reconfiguration of Autonomous Systems , 2010 .

[8]  Bryce A. Roberts,et al.  FUSE in-orbit attitude control with two reaction wheels and no gyroscopes , 2003, SPIE Astronomical Telescopes + Instrumentation.

[9]  Sriraam Natarajan,et al.  Transfer in variable-reward hierarchical reinforcement learning , 2008, Machine Learning.

[10]  Martin L. Puterman,et al.  Markov Decision Processes: Discrete Stochastic Dynamic Programming , 1994 .

[11]  N. E. Wu Robust feedback design with optimized diagnostic performance , 1997 .

[12]  M.A. Wiering,et al.  Reinforcement Learning in Continuous Action Spaces , 2007, 2007 IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning.

[13]  R. J. Williams,et al.  Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning , 2004, Machine Learning.

[14]  John R. Broussard,et al.  Application of precomputed control laws in a reconfigurable aircraft flight control system , 1989 .

[15]  Peter L. Bartlett,et al.  Reinforcement Learning in POMDP's via Direct Gradient Ascent , 2000, ICML.

[16]  Peter Norvig,et al.  Artificial intelligence - a modern approach, 2nd Edition , 2003, Prentice Hall series in artificial intelligence.

[17]  Ella M. Atkins,et al.  Fault Tolerance for Spacecraft Attitude Management , 2010 .

[18]  Youmin Zhang,et al.  Integrated active fault-tolerant control using IMM approach , 2001 .

[19]  David J. Sahnow,et al.  Operations with the new FUSE Observatory: three-axis control with one reaction wheel , 2006, SPIE Astronomical Telescopes + Instrumentation.

[20]  E. Rogers,et al.  Fault Tolerant Controller Design to Ensure Operational Safety in Satellite Formation Flying , 2006, Proceedings of the 45th IEEE Conference on Decision and Control.

[21]  John J. Deyst,et al.  Maximum Likelihood Failure Detection Techniques Applied to the Shuttle RCS Jets , 1976 .

[22]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[23]  Paul M. Frank,et al.  Fault diagnosis in dynamic systems: theory and application , 1989 .

[24]  S.M. Veres,et al.  A class of BDI agent architectures for autonomous control , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).