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Experimental study of identification and control of structures using neural network. Part 2: control
Author(s) -
BaniHani Khaldoon,
Ghaboussi Jamshid,
Schneider Stephen P.
Publication year - 1999
Publication title -
earthquake engineering and structural dynamics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.218
H-Index - 127
eISSN - 1096-9845
pISSN - 0098-8847
DOI - 10.1002/(sici)1096-9845(199909)28:9<1019::aid-eqe852>3.0.co;2-x
Subject(s) - engineering , actuator , robustness (evolution) , artificial neural network , system identification , control engineering , control theory (sociology) , controller (irrigation) , control system , computer science , control (management) , artificial intelligence , data modeling , agronomy , biochemistry , chemistry , biology , electrical engineering , gene , software engineering
Experimental verifications of a recently developed active structural control method using neural networks are presented in this paper. The experiments were performed on the earthquake simulator at the University of Illinois at Urbana—Champaign. The test specimen was a 1/4 scale model of a three‐storey building. The control system consisted of a tendon/pulley system controlled by a single hydraulic actuator at the base. The control mechanism was implemented through four active pre‐tensioned tendons connected to the hydraulic actuator at the first floor. The structure modelling and system identification has been presented in a companion paper. ( Earthquake Engng. Struct. Dyn. 28 , 995–1018 (1999)). This paper presents the controller design and implementation. Three controllers were developed and designed: two neurocontrollers, one with a single sensor feedback and the other with three sensor feedback, and one optimal controller with acceleration feedback. The experimental design of the neurocontrollers is accomplished in three steps: system identification, multiple emulator neural networks training and finally the neurocontrollers training with the aid of multiple emulator neural networks. The effectiveness of both neurocontrollers are demonstrated from experimental results. The robustness and the relative stability are presented and discussed. The experimental results of the optimal controller performance is presented and assessed. Comparison between the optimal controller and neurocontrollers is presented and discussed. Copyright © 1999 John Wiley & Sons, Ltd.