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Computational Science, Engineering & Technology Series
ISSN 1759-3158
Edited by: Y. Tsompanakis and B.H.V. Topping
Chapter 7

Neurofuzzy Control for Smart Structures

G.E. Stavroulakis1,2, I. Papachristou1, S. Salonikidis1, I. Papalaios1 and G.K. Tairidis1

1Department of Production Engineering and Management, Institute of Computational Mechanics and Optimization, Technical University of Crete, Chania, Greece
2Department of Civil Engineering, Technical University of Braunschweig, Germany

Full Bibliographic Reference for this chapter
G.E. Stavroulakis, I. Papachristou, S. Salonikidis, I. Papalaios, G.K. Tairidis, "Neurofuzzy Control for Smart Structures", in Y. Tsompanakis and B.H.V. Topping, (Editor), "Soft Computing Methods for Civil and Structural Engineering", Saxe-Coburg Publications, Stirlingshire, UK, Chapter 7, pp 149-172, 2011. doi:10.4203/csets.29.7
Keywords: structural control, smart structures, neural control, fuzzy control, adaptive neural fuzzy inference.

Smart structures incorporate sensors and actuators, as well as control mechanisms that provide the smart (intelligent) behaviour. The control system is, therefore, an important part of the system. Linear feedback of linear systems can be studied by using classical methods of structural control. Useful theoretical results and algorithms exist for the study of linear control systems. Nonlinearity in the system or in the controller makes the problem much more complicated. The theoretical results are considerably restricted in the nonlinear case. Similar difficulties appear in the presence of a system of other uncertainty. Attempts to solve this problem in classical control have already been studied (such as H_inf control). Fuzzy and hybrid neuro-fuzzy controllers can be used in smart structures with many advantages with respect to the aforementioned difficulties. The so-called soft computing control is discussed in the present chapter. Emphasis is placed on techniques of fuzzy and neurofuzzy control. A concrete example for a smart beam equipped with piezoelectric sensors and actuators is presented. Practical tools for the design of the proposed methods within MATLAB/SIMULINK and ANFIS or similar, modern software tools are also mentioned.

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