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Hybrid Control of Structures Using Fuzzy Logic
Author(s) -
Subramaniam Ravi S.,
Reinhorn Andrei M.,
Riley Michael A.,
Nagarajaiah Satish
Publication year - 1996
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/j.1467-8667.1996.tb00305.x
Subject(s) - control theory (sociology) , fuzzy logic , fuzzy control system , computer science , controller (irrigation) , benchmark (surveying) , control engineering , control system , engineering , control (management) , artificial intelligence , electrical engineering , geodesy , geography , agronomy , biology
This investigation examined the application of control algorithms based on fuzzy logic to a class of hybrid structural control systems. The investigation included both analytical and experimental verification of the fuzzy control algorithm. The objective of the hybrid system under investigation is to obtain an ideal sliding system with perfect base isolation. As the hybrid system approaches the state of ideal isolation, the effects of imperfections, signal noise, uncertainties, modeling errors, and compensation errors start to play a dominant role in the control performance. Fuzzy logic (or fuzzy set theory) provides a simple framework to capture the effects of nonlinearities and uncertainties in a real problem without an explicit model of the plant or controller. The applicability of this approach was first investigated analytically and then verified using a benchmark experimental model consisting of a 1:4 scale sliding‐base isolated system controlled at its base by a servohydraulic actuator with a digital computer to provide the control signal. The fuzzy controller used feedback from either the acceleration of the moving foundation or the force at the interface to produce control forces in a series of shaking‐table tests. The results from this study show the feasibility of the implementation of fuzzy logic to highly nonlinear problems.