Experimental study of identification and control of structures using neural network. Part 2: control
โ Scribed by Bani-Hani, Khaldoon; Ghaboussi, Jamshid; Schneider, Stephen P.
- Publisher
- John Wiley and Sons
- Year
- 1999
- Tongue
- English
- Weight
- 675 KB
- Volume
- 28
- Category
- Article
- ISSN
- 0098-8847
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โฆ Synopsis
Experimental veri"cations 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 "rst #oor. The structure modelling and system identi"cation 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 identi"cation, multiple emulator neural networks training and "nally the neurocontrollers training with the aid of multiple emulator neural networks. The e!ectiveness 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.
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This paper proposes an integrated approach to the modelling and optimization of structural control systems in tall buildings. In this approach, an artificial neural network is applied to model the structural dynamic responses of tall buildings subjected to strong earthquakes, and a genetic algorithm