JRM Vol.24 No.1 pp. 5-15
doi: 10.20965/jrm.2012.p0005


Probabilistic Planning for Predictive Condition Monitoring and Adaptation Within the Self-Optimizing Energy Management of an Autonomous Railway Vehicle

Benjamin Klöpper*, Christoph Sondermann-Wölke**,
and Christoph Romaus**

*National Institute of Informatics, Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan

**University of Paderborn, Germany

January 2, 2011
March 22, 2011
February 20, 2012
self-optimizing systems, dependability, probabilistic planning, energy management
Self-optimizing mechatronic systems are a new class of technical systems. On the one hand, new challenges regarding dependability arise from their additional complexity and adaptivity. On the other hand, their abilities enable new concepts and methods to improve the dependability of mechatronic systems. This paper introduces a multi-level dependability concept for selfoptimizing mechatronic systems and shows how probabilistic planning can be used to improve the availability and reliability of systems in the operating phase. The general idea to improve the availability of autonomous systems by applying probabilistic planning methods to avoid energy shortages is exemplified on the example of an innovative railway vehicle.
Cite this article as:
B. Klöpper, C. Sondermann-Wölke, and C. Romaus, “Probabilistic Planning for Predictive Condition Monitoring and Adaptation Within the Self-Optimizing Energy Management of an Autonomous Railway Vehicle,” J. Robot. Mechatron., Vol.24 No.1, pp. 5-15, 2012.
Data files:
  1. [1] R. Isermann, “Mechatronic Systems: Fundamentals,” Springer-Verlag, London, 2005.
  2. [2] J. Gausemeier, S. Kahl, and S. Pook, “From Mechatronics to Self-Optimizing Systems,” In Self-optimizing Mechatronic Systems: Design the Future, 7th Int. Heinz Nixdorf Symposium, 2008.
  3. [3] J. C. Laprie (Ed.), “Dependability: Basic Concepts and Terminology in English, French, German, Italian, and Japanese,” Springer-Verlag, Wien, 1992.
  4. [4] B. Klöpper, C. Sondermann-Wölke, C. Romaus, and H. Voecking, “Probabilistic Planning Integrated in a Multi-level Dependability Concept for Mechatronic Systems,” In 2009 IEEE Symposium on Computational Intelligence in Control and Automation, pp. 104-111, 2009.
  5. [5] S. Russell and P. Norvig, “Artificial Intelligence: A Modern Approach,” Prentice Hall, 3 edition, 2009.
  6. [6] G. Della Penna, D. Magazzeni, F. Mercorio, and B. Intrigila, “UPMurphi: A Tool for Universal Planning on PDDL+ Problems,” In Proc. of 19th Int. Conf. on Automated Planning and Scheduling, pp. 106-113, 2009.
  7. [7] P. Maier and M. Sachenbacher, “Self-Monitoring and Control for Embedded Systems using Hybrid Constraint Automata,” In Self-X in Engineering, pp. 8-23. MV-Verlag, 2009.
  8. [8] P. Adelt, N. Esau, and A. Schmidt, “Hybrid Planning for an Air Gap Adjustment System Using Fuzzy Models,” J. of Robotics and Mechatronics, Vol.21, No.5, pp. 647-655, 2009.
  9. [9] P. Adelt and B. Klöpper, “Buildings Blocks and Prototypical Implementation of a Hybrid Planning Architecture,” In Self-x in Engineering, pp. 55-67, 2009.
  10. [10] I. Little and S. Thiebaux, “Concurrent Probabilistic Planning in the Graphplan Framework,” In Proc. of the Int. Conf. on Automated Planning & Scheduling, June 6-10, 2006, Cumbria, pp. 263-273, 2006.
  11. [11] N. Onder, G. C. Whelan, and L. Li, “Engineering a Conformant Probabilistic Planner,” J. of Artificial Intelligence Research, Vol.25, pp. 1-15, 2006.
  12. [12] J. Blythe, “Planning Under Uncertainty in Dynamic Domains,” Ph.D. thesis, Carnegie Mellon University Computer Science Department, 1998.
  13. [13] L. Johannesson, M. Asbogard, and B. Egardt, “Assessing the Potential of Predictive Control for Hybrid Vehicle Powertrains Using Stochastic Dynamic Programming,” Intelligent Transportation Systems, IEEE Trans. on, Vol.8, No.1, pp. 71-83, 2007.
  14. [14] C. Romaus, K. Gathmann, and J. Böcker, “Optimal Energy Management for a Hybrid Energy Storage System for Electric Vehicles Based on Stochastic Dynamic Programming,” In Vehicle Power and Propulsion Conf. (VPPC 2010), IEEE, 2010.
  15. [15] S. Ross, J. Pineau, S. Paquet, and B. Chaib-draa, “Online Planning Algorithms for POMDPs,” J. of Artificial Intelligence Research, Vol.32, pp. 663-704, 2008.
  16. [16] C. Sondermann-Wölke and W. Sextro, “Integration of Condition Monitoring in Self-Optimizing Function Modules Applied to the Active Railway Guidance Module,” Int. J. on Advances in Intelligent Systems, Vol.3, No.1-12, pp. 65-74, 2010.
  17. [17] C. Sondermann-Wölke, J. Geisler, T. Müller, A. Trächtler, and J. Böcker, “The active guidance module of a rail-bound vehicle as an application for the dependability oriented design in selfoptimizing systems,” In ASME 2008 Int. Design Engineering Technical Conf. & Computers and Information in Engineering Conf., 2008.
  18. [18] “ISO 17359, Condition monitoring and diagnostic of machines – general guidelines,” 2003.
  19. [19] C. Henke, N. Fröhleke, and J. Böcker, “Advanced Convoy Control Strategy for Autonomously Driven Railway Vehicles,” IEEE Conf. on Intelligent Transportation Systems, 2006.
  20. [20] H. Grotstollen, “Design of Long-Stator Linear Motor Drives for RailCab TestTrack,” J. of Power Electronics (JPE), KIPE, Vol.5, pp. 166-172, 2005.
  21. [21] T. Schneider, B. Schulz, C. Henke, K. Witting, D. Steenken, and J. Böcker, “Energy Transfer via Linear Doubly-Fed Motor in Different Operating Modes,” In Int. Electric Machines and Drives Conf. (IEMDC), 2009.
  22. [22] C. Romaus, J. Böcker, K. Witting, A. Seifried, and O. Znamenshchykov, “Optimal Energy Management for a Hybrid Energy Storage System Combining Batteries and Double Layer Capacitors,” In Energy Conversion Congress and Exposition (ECCE), 2009.
  23. [23] H. Vöcking and A. Trächtler, “Self-optimization of an Active Suspension System Regarding Energy Requirements,” In Int. Conf. on Control, Automation and Systems 2008, 2008.
  24. [24] W. v. Wezel and R. Jorne, “Paradoxes in Planning,” Engineering Applications of Artificial Intelligence, Vol.14, pp. 269-286, 2001.
  25. [25] D. Warren, “Generating Conditional Plans and Programms,” In Proc. of the summer Conf. on AI and Simulation, 1977.
  26. [26] J. Blythe, “Artificial intelligence today: recent trends and developments,” chapter An overview of planning under uncertainty, pp. 85-110, Lecture Notes in Computer Science, Springer-Verlag, Berlin, Heidelberg, 1999.
  27. [27] R. Dearden, “Planning and learning in hybrid discrete domains – continuous models,” In 20th Int. Joint Conf. on Artificial Intelligence, 2005.
  28. [28] I. Ben-Gal, “Encyclopedia of Statistics in Quality & Reliability,” chapter Bayesian Networks, Wiley & Sons, 2007.
  29. [29] J. Pearl, “Probabilistic Reasoning in Intelligent Systems – Networks of Plausible Inference,” Morgan Kaufmann, 1988.
  30. [30] W. Dangelmaier, B. Klöpper, and A. Blecken, “An Agent Based Modeling Approach for Stochastic Planning Parameters,” In Holo-MAS 2007, Vol.4659 of Lecture Notes in Artificial Intelligence, pp. 225-236, 2007.

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