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Fuzzy and Neuro-fuzzy Control for Smart Structures

In: Computational Intelligence and Optimization Methods for Control Engineering

Author

Listed:
  • Georgios K. Tairidis

    (Technical University of Crete, Institute of Computational Mechanics and Optimization)

  • Georgios E. Stavroulakis

    (Technical University of Crete, Institute of Computational Mechanics and Optimization)

Abstract

Classical control tools often encounter a number of limitations on the investigation of smart composite structures due to nonlinearities and/or other uncertainties. Especially in smart structures, which is the case here, a significant degree of uncertainty is involved due to several imperfections and/or errors of both the controller and the structure itself. For example, in structures with multiple layers, several failures may appear, such as delamination, debonding, fatigue, etc. The use of intelligent fuzzy and adaptive control which is based on neuro-fuzzy techniques can be very helpful in this direction. One may also consider using global optimization algorithms for the fine-tuning of the characteristics of the controllers to maximize their applicability, their efficiency, and their robustness. In other words, the controllers can be designed based on intuition and basic engineering principles, and then they can be subjected to optimization, e.g., to training/learning using artificial neural networks, in order to achieve certain properties.

Suggested Citation

  • Georgios K. Tairidis & Georgios E. Stavroulakis, 2019. "Fuzzy and Neuro-fuzzy Control for Smart Structures," Springer Optimization and Its Applications, in: Maude Josée Blondin & Panos M. Pardalos & Javier Sanchis Sáez (ed.), Computational Intelligence and Optimization Methods for Control Engineering, chapter 0, pages 75-103, Springer.
    • Handle: RePEc:spr:spochp:978-3-030-25446-9_4
    DOI: 10.1007/978-3-030-25446-9_4
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