The goal of prognostic decision making (PDM) is to utilize information on anticipated system health changes in selecting future actions. One of the key challenges in PDM is find- ing a sufficiently expressive yet compact mathematical representation of the system for use with decision optimization algorithms. In this paper we describe a general modeling approach for a class of PDM problems with non-linear system degradation processes and uncertainties in state estimation, action effects, and future operating conditions. The approach is based on continuous Partially Observable Markov Decision Processes (POMDPs) used in conjunction with ’black box’ system simulations. The proposed modeling framework can be cast into simpler representations, depending on which sources of uncertainty are being included. The approach is illustrated with a mission planning case study for an unmanned aerial vehicle (UAV). In the case study a PDM system is tasked with optimizing the vehicle route after an in-flight component fault is detected. A stochastic algorithm (based on particle filtering) is used for decision optimization, with a second, deterministic algorithm providing a performance evaluation baseline. Both algorithms utilize a UAV physics simulator for generating predictions of future vehicle states. Performance benchmarking is done on a set of mission scenarios of increasing complexity.
How to Cite
prognostics, decision-making, UAV
Barsali, S., & Ceraolo, M. (2002, March). Dynamical Models of Lead-Acid Batteries: Implementation Issues. IEEE Transactions on Energy Conversion, 17(1), 16–23.
Bellman, R. (1957a). Dynamic Programming. Princeton, NJ: Princeton University Press.
Bellman, R. (1957b). A Markovian Decision Process (Tech. Rep.). Santa Monica, CA: RAND Corporation.
Bertsekas, D. P. (1995). Dynamic Programming and Optimal Control. Athena Scientific.
Bogdanov, A., Chiu, S., Gokdere, L. U., & Vian, J. (2006). Stochastic Optimal Control of a Servo Motor with a Lifetime Constraint. In Proceedings of the 45th IEEE conference on decision and control.
Brown, D. W., & Vachtsevanos, G. J. (2011). A Prognostic Health Management Based Framework for Fault- Tolerant Control. In Annual conference of the prognostics and health management society. Montreal, Canada.
Chen, M., & Rincon-Mora, G. (2006). Accurate Electrical Battery Model Capable of Predicting Runtime and I-V Performance. IEEE Transactions on Energy Conversion, 21(2), 504–511.
Chen, Y., Song, L., & Evans, J. W. (1996). Modeling Studies On Battery Thermal Behaviour, Thermal Runaway, Thermal Management, and Energy Efficiency. In Proceedings of the 31st Intersociety energy conversion engineering conference (iecec).
Daigle, M. J., Bregon, A., & Roychoudhury, I. (2012). A Distributed Approach to System-Level Prognostics. In Annual conference of the prognostics and health management society. Montreal, Canada.
Daigle, M. J., & Roychoudhury, I. (2010). Qualitative Event- Based Diagnosis: Case Study on the Second International Diagnostic Competition. In 21st international workshop on
principles of diagnosis.
Daigle, M. J., Saxena, A., & Goebel, K. (2012). An Efficient Deterministic Approach to Model-based Prediction Uncertainty Estimation. Annual Conference of the Prognostics and Health Management Society.
Denney, E., Pai, G., & Habli, I. (2012). Perspectives on Software Safety Case Development for Unmanned Aircraft. In Ieee/ifip international conference on dependable systems and networks. Boston, MA.
Edwards, D., Orchard, M. E., Tang, L., Goebel, K., & Vachtsevanos, G. (2010). Impact of Input Uncertainty on Failure Prognostic Algorithms: Extending the Remaining Useful Life of Nonlinear Systems. In Annual conference of the prognostics and health management society. Portland, OR.
Ferguson, D., & Stentz, A. (2006). Using Interpolation to Improve Path Planning: The Field D* Algorithm. Journal of Field Robotics, 23(2), 79–101.
Gordon, N., Salmond, D., & Smith, A. (1993). Novel Approach to Nonlinear/non-Gaussian Bayesian State Estimation. IEE Proceedings F (Radar and Signal Processing), 140(2), 107–113.
Hogge, E., Quach, C., Vazquez, S., & Hill, B. (2011). A Data System for a Rapid Evaluation Class of Subscale Aerial Vehicle, NASA/TM2011-217145 (Tech. Rep.). Hamp- ton, VA.
Jacobson, D. H., & Mayne, D. Q. (1970). Differential Dynamic Programming. American Elsevier.
Knuth, D. E. (1968). The Art of Computer Programming. Addison-Wesley.
Lewis, K. W. (1984). The Cumulative Effects of Roughness and Reynolds Number on NACA 0015 Airfoil Section Characteristics (Tech. Rep.). Texas Tech University.
MATLAB version 220.127.116.114 (2010b). (2010). Natick, Mas- sachusetts: The MathWorks Inc.
Milanfar, P., & Lang, J. (1996). Monitoring the Thermal Condition of Permanent-Magnet Synchronous Motors. IEEE Transactions On Aerospace And Electronic Sys- tems, 32(4), 1421–1429.
Miller, S. (2008). Lift, Drag, and Moment of a NACA 0015 Airfoil (Tech. Rep.). Ohio State University.
Narasimhan, S., & Brownston, L. (2007). HyDE - A General Framework for Stochastic and Hybrid Model-based Diagnosis. In Proceedings of the international workshop on the principles of diagnosis. Nashville, TN.
Orchard, M. E. (2007). A Particle Filtering-based Framework for On-line Fault Diagnosis and Failure Prognosis. Doctoral thesis, Georgia Institute of Technology.
Pereira, E. B., Galvao, R. K. H., & Yoneyama, T. (2010). Model Predictive Control using Prognosis and Health Monitoring of Actuators. In 2010 ieee international symposium on industrial electronics.
Pineau, J., Gordon, G., & Thrun, S. (2006). Anytime Point- Based Approximations for Large POMDPs. Journal of Artificial Intelligence Research, 27, 335–380.
Saha, B., Quach, C., & Goebel, K. (2012). Optimizing Battery Life for Electric UAVs using a Bayesian Framework. In 2012 IEEE aerospace conference.
Tang, L., Hettler, E., Zhang, B., & Decastro, J. (2011). A Testbed for Real-Time Autonomous Vehicle PHM and Contingency Management Applications. In Annual conference of the prognostics and health management society.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.