Mimicking Complexity - Automatic Generation of Models for the Development of Self-adaptive Systems

Jérémy Boes, Pierre Glize, Frédéric Migeon

2013

Abstract

Many methods for complex systems control use a black box approach where the internal states and mechanisms of the controlled process are not needed to be known. Usually, such systems are tested on simulations before their validation on the real world process they were made for. These simulations are based on sharp analytical models of the target process that can be very difficult to obtain. But is it useful in the case of black box methods? Since the control system only sees inputs and outputs and is able to learn, we only need to mimic the typical features of the process (such as non-linearity, interdependencies, etc) in an abstract way. This paper aims to show how a simple and versatile simulator can help the design of systems that have to deal with complexity. We present a generator of models used in the simulator and discuss the results obtained in the case of the design of a control system for heat engines.

References

  1. Astrom, K. J. and Hagglund, T. (1995). PID Controllers: Theory, Design, and Tuning. Instrument Society of America, Research Triangle Park, NC, second edition.
  2. Boes, J., Migeon, F., and Gatto, F. (2013). Self-Organizing Agents for an Adaptive Control of Heat Engine. In International Conference on Informatics in Control, Automation and Robotics (ICINCO), Reykjavik. INSTICC Press.
  3. Borman, G. (1964). Mathematical simulation of internal combustion engine processes and performance including comparison with experiment. PhD thesis, Univ. of Wisconsin.
  4. Catmull, E. and Clark, J. (1978). Recursively generated b-spline surfaces on arbitrary topological meshes. Computer-Aided Design, 10(6):350 - 355.
  5. Colosimo, B. M. and Del Castillo, E., editors (2007). Bayesian Process Monitoring, Control and Optimization. Taylor and Francis, Hoboken, NJ.
  6. Curto-Risso, P. L., Medina, A., and Calvo Hernandez, A. (2009). Optimizing the operation of a spark ignition engine: Simulation and theoretical tools. Journal of Applied Physics, 105(9):094904 -094904-10.
  7. Dabo, M., Langlois, N., Respondek, W., and Chafouk, H. (2008). NCGPC with dynamic extension applied to a Turbocharged Diesel Engine. In Proceedings of the International Federation of Automatic Control 17th World Congress, pages 12065-12070.
  8. Edwards, S. H. (2001). A framework for practical, automated black-box testing of component-based software. Software Testing, Verification and Reliability, 11(2):97-111.
  9. Georgé, J.-P., Gleizes, M.-P., and Camps, V. (2011). Cooperation. In Di Marzo Serugendo, G., editor, Self-organising Software, Natural Computing Series, pages 7-32. Springer Berlin Heidelberg.
  10. Hagan, M. T., Demuth, H. B., and De Jesus, O. (2002). An introduction to the use of neural networks in control systems. International Journal of Robust and Nonlinear Control, 12(11):959-985.
  11. Jankovic, M. and Kolmanovsky, I. (2000). Constructive lyapunov control design for turbocharged diesel engines. IEEE Transactions on Control Systems Technology, 8(2):288 -299.
  12. Lee, C. C. (1990). Fuzzy logic in control systems: Fuzzy logic controller. IEEE Transactions on Systems, Man and Cybernetics, 20(2):404-418.
  13. Nikolaou, M. (2001). Model predictive controllers: A critical synthesis of theory and industrial needs. Advances in Chemical Engineering, 26:131-204.
  14. Stengel, R. F. (1991). Intelligent failure-tolerant control. IEEE Control Systems, 11(4):14-23.
  15. Tahat, L., Vaysburg, B., Korel, B., and Bader, A. (2001). Requirement-based automated black-box test generation. In 25th Annual International Computer Software and Applications Conference., pages 489 -495.
  16. Taralp, T., Devetsikiotis, M., and Lambadaris, I. (1998). Efficient fractional gaussian noise generation using the spatial renewal process. In IEEE International Conference on Communications, pages 1456 -1460.
  17. Videau, S., Bernon, C., Glize, P., and Uribelarrea, J.-L. (2011). Controlling Bioprocesses using Cooperative Self-organizing Agents. In Demazeau, Y., editor, PAAMS, volume 88 of Advances in Intelligent and Soft Computing, pages 141-150. Springer-Verlag.
  18. Wang, H. (2001). Multi-agent co-ordination for the secondary voltage control in power-system contingencies. Generation, Transmission and Distribution, IEEE Proceedings, 148(1):61 -66.
  19. Zhang, S., Broadbelt, L. J., Androulakis, I. P., and Ierapetritou, M. G. (2012). Comparison of biodiesel performance based on hcci engine simulation using detailed mechanism with on-the-fly reduction. Energy and Fuels, 26(2):976-983.
  20. Zhao, Z., Zhang, F., Zhao, C., and Chen, Y. (2008). Modeling and simulation of a hydraulic free piston diesel engine. SAE Technical Paper, pages 01-1528.
Download


Paper Citation


in Harvard Style

Boes J., Glize P. and Migeon F. (2013). Mimicking Complexity - Automatic Generation of Models for the Development of Self-adaptive Systems . In Proceedings of the 3rd International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH, ISBN 978-989-8565-69-3, pages 353-360. DOI: 10.5220/0004483003530360


in Bibtex Style

@conference{simultech13,
author={Jérémy Boes and Pierre Glize and Frédéric Migeon},
title={Mimicking Complexity - Automatic Generation of Models for the Development of Self-adaptive Systems},
booktitle={Proceedings of the 3rd International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH,},
year={2013},
pages={353-360},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004483003530360},
isbn={978-989-8565-69-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH,
TI - Mimicking Complexity - Automatic Generation of Models for the Development of Self-adaptive Systems
SN - 978-989-8565-69-3
AU - Boes J.
AU - Glize P.
AU - Migeon F.
PY - 2013
SP - 353
EP - 360
DO - 10.5220/0004483003530360