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Gravitational Search Algorithm-Based Tuning of Fuzzy Control Systems with a Reduced Parametric Sensitivity

  • Radu-Emil Precup
  • Radu-Codruţ David
  • Emil M. Petriu
  • Stefan Preitl
  • Adrian Sebastian Paul
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 96)

Abstract

This paper proposes the tuning of a class of fuzzy control systems to ensure a reduced parametric sensitivity on the basis of a new Gravitational Search Algorithm (GSA). The GSA is employed to solve the optimization problems characterized by the minimization of objective functions defined as integral quadratic performance indices. The performance indices depend on the control error and on the squared output sensitivity functions of the sensitivity models with respect to the parametric variations of the controlled process. The controlled processes in the fuzzy control systems are benchmarks modeled by second-order linearized systems with an integral component and Takagi-Sugeno proportional-integral fuzzy controllers are designed and tuned for these processes.

Keywords

Fuzzy Controller Iterative Learn Control Gravitational Search Algorithm Fuzzy Control System Clonal Selection Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Abonyi, J.: Fuzzy Model Identification for Control. Birkhäuser, Boston (2003)zbMATHGoogle Scholar
  2. 2.
    Bodenhofer, U., Klawonn, F.: Robust rank correlation coefficients on the basis of fuzzy orderings: Initial steps. Mathware and Soft Computing 15, 5–20 (2008)MathSciNetzbMATHGoogle Scholar
  3. 3.
    Blažič, S., Škrjanc, I., Gerkšič, S., Dolanc, G., Strmčnik, S., Hadjiski, M.B., Stathaki, A.: Online fuzzy identification for an intelligent controller based on a simple platform. Engineering Applications of Artificial Intelligence 22, 628–638 (2009)CrossRefGoogle Scholar
  4. 4.
    Boucher, X., Bonjour, E., Grabot, B.: Formalisation and use of competencies for industrial performance optimisation: A survey. Computers in Industry 58, 98–117 (2007)CrossRefGoogle Scholar
  5. 5.
    Campos, F.M.M.O., Calado, J.M.F.: Approaches to human arm movement control - A review. Annual Reviews in Control 33, 69–77 (2009)CrossRefGoogle Scholar
  6. 6.
    Carrano, E.G., Takahashi, R.H.C., Fonseca, C.M., Neto, O.M.: Non-linear network optimization - An embedding vector space approach. IEEE Transactions on Evolutionary Computation 14, 206–226 (2010)CrossRefGoogle Scholar
  7. 7.
    Chen, J., Kong, C.K.: Performance assessment for iterative learning control of batch units. Journal of Process Control 19, 1043–1053 (2009)CrossRefGoogle Scholar
  8. 8.
    David, R.C., Rădac, M.-B., Preitl, S., Tar, J.K.: Particle swarm optimization-based design of control systems with reduced sensitivity. In: Proceedings of 5th International Symposium on Applied Computational Intelligence and Informatics, Timisoara, Romania, pp. 491–496 (2009)Google Scholar
  9. 9.
    Deb, K., Gupta, S., Daum, D., Branke, J., Mall, A.K., Padmanabhan, D.: Reliability-based optimization using evolutionary algorithms. IEEE Transactions on Evolutionary Computation 13, 1054–1074 (2009)CrossRefGoogle Scholar
  10. 10.
    Ekel, P.Y., Menezes, M., Schuffner Neto, F.H.: Decision making in a fuzzy environment and its application to multicriteria power engineering problems. Nonlinear Analysis: Hybrid Systems 1, 527–536 (2007)MathSciNetzbMATHCrossRefGoogle Scholar
  11. 11.
    Ferreira, J.C., Fonseca, C.M., Gaspar-Cunha, A.: Assessing the quality of the relation between scalarizing function parameters and solutions in multiobjective optimization. In: Proceedings of IEEE Congress on Evolutionary Computation, Trondheim, Norway, pp. 1131–1136 (2009)Google Scholar
  12. 12.
    Haber, R.E., Haber-Haber, R., Jiménez, A., Galán, R.: An optimal fuzzy control system in a network environment based on simulated annealing. An application to a drilling process. Applied Soft Computing 9, 889–895 (2009)Google Scholar
  13. 13.
    Hermann, G., Tar, J.K., Kozłowski, K.R.: Design of a planar high precision motion stage. In: Kozłowski, K.R. (ed.) Robot Motion and Control 2009. LNCIS, vol. 396, pp. 371–379. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  14. 14.
    Hidalgo, D., Melin, P., Castillo, O.: Optimal design of type-2 fuzzy membership functions using genetic algorithms in a partitioned search space. In: Proceedings of IEEE Conference on Granular Computing, Silicon Valley, CA, USA, pp. 212–216 (2010)Google Scholar
  15. 15.
    Holliday, D., Resnick, R., Walker, J.: Fundamentals of Physics, 7th edn. John Wiley & Sons, Hoboken (2005)Google Scholar
  16. 16.
    Johanyák, Z.C., Kovács, S.: Sparse fuzzy system generation by rule base extension. In: Proceedings of 11th International Conference on Intelligent Engineering Systems, Budapest, Hungary, pp. 99–104 (2007)Google Scholar
  17. 17.
    Ko, M., Tiwari, A., Mehnen, J.: A review of soft computing applications in supply chain management. Applied Soft Computing 10, 661–674 (2010)CrossRefGoogle Scholar
  18. 18.
    Köppen, M.: Light-weight evolutionary computation for complex image-processing applications. In: Proceedings of 6th International Conference on Hybrid Intelligent Systems, Auckland, New Zealand, pp. 3–3 (2006)Google Scholar
  19. 19.
    López-Ibáñez, M., Stützle, T.: The impact of design choices of multiobjective antcolony optimization algorithms on performance: An experimental study on the biobjective TSP. In: Proceedings of Genetic and Evolutionary Computation Conference, Portland, OR, USA, pp. 71–78 (2010)Google Scholar
  20. 20.
    Navarro-López, E.M., Licéaga-Castro, E.: Combining passivity and classical frequency-domain methods: An insight into decentralised control. Applied Mathematics and Computation 215, 4426–4438 (2010)MathSciNetzbMATHCrossRefGoogle Scholar
  21. 21.
    Nouyan, S., Gross, R., Bonani, M., Mondada, F., Dorigo, M.: Teamwork in self-organized robot colonies. IEEE Transactions on Evolutionary Computation 13, 695–711 (2009)CrossRefGoogle Scholar
  22. 22.
    Oliveira, C., Henggeler Antunes, C.: Multiple objective linear programming models with interval coefficients – an illustrated overview. European Journal of Operational Research 181, 1434–1463 (2007)zbMATHCrossRefGoogle Scholar
  23. 23.
    Precup, R.E., Preitl, S.: Optimisation criteria in development of fuzzy controllers with dynamics. Engineering Applications of Artificial Intelligence 17, 661–674 (2004)CrossRefGoogle Scholar
  24. 24.
    Precup, R.E., Preitl, S., Korondi, P.: Fuzzy controllers with maximum sensitivity for servosystems. IEEE Transactions on Industrial Electronics 54, 1298–1310 (2007)CrossRefGoogle Scholar
  25. 25.
    Preitl, S., Precup, R.E.: An extension of tuning relations after symmetrical optimum method for PI and PID controllers. Automatica 35, 1731–1736 (1999)zbMATHCrossRefGoogle Scholar
  26. 26.
    Rashedi, E., Nezamabadi-pour, H., Saryazdi, S.: GSA: A gravitational search algorithm. Information Sciences 179, 2232–2248 (2009)zbMATHCrossRefGoogle Scholar
  27. 27.
    Rashedi, E., Nezamabadi-pour, H., Saryazdi, S.: BGSA: binary gravitational search algorithm. Natural Computing 9, 727–745 (2010)MathSciNetzbMATHCrossRefGoogle Scholar
  28. 28.
    Ridluan, A., Manic, M., Tokuhiro, A.: EBaLM-THP – artificial neural network thermo-hydraulic prediction tool for an advanced nuclear components. Nuclear Engineering and Design 239, 308–319 (2009)CrossRefGoogle Scholar
  29. 29.
    Rosenwasser, E., Yusupov, R.: Sensitivity of Automatic Control Systems. CRC Press, Boca Raton (2000)zbMATHGoogle Scholar
  30. 30.
    Saxena, A., Saad, A.: Evolving an artificial neural network classifier for condition monitoring of rotating mechanical systems. Applied Soft Computing 7, 441–454 (2007)CrossRefGoogle Scholar
  31. 31.
    Vaščák, J.: Fuzzy cognitive maps in path planning. Acta Technica Jaurinensis, Series Intelligentia Computatorica 1, 467–479 (2008)Google Scholar
  32. 32.
    Wang, X., Gao, X.Z., Ovaska, S.J.: Fusion of clonal selection algorithm and harmony search method in optimisation of fuzzy classification systems. International Journal of Bio-Inspired Computation 1, 80–88 (2009)CrossRefGoogle Scholar
  33. 33.
    Wang, Y.G., Xu, X.M.: PID tuning for unstable processes with sensitivity specification. In: Proceedings of Chinese Control and Decision Conference, Guilin, China, pp. 3460–3464 (2009)Google Scholar
  34. 34.
    Xu, J., Wu, H., Wang, Y.: Unpower aerocraft augmented state feedback tracking guaranteed cost control. Journal of Systems Engineering and Electronics 19, 125–130 (2008)Google Scholar
  35. 35.
    Zhou, H., Schaefer, G., Shi, C.: A mean shift based fuzzy c-means algorithm for image segmentation. In: Proceedings of 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vancouver, BC, Canada, pp. 3091–3094 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Radu-Emil Precup
    • 1
  • Radu-Codruţ David
    • 1
  • Emil M. Petriu
    • 2
  • Stefan Preitl
    • 1
  • Adrian Sebastian Paul
    • 1
  1. 1.Department of Automation and Applied Informatics“Politehnica” University of TimisoaraTimisoaraRomania
  2. 2.School of Information Technology and EngineeringUniversity of OttawaOttawaCanada

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