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Nonlinear control of structure using neuro-predictive algorithm
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  • Journal title : Smart Structures and Systems
  • Volume 16, Issue 6,  2015, pp.1133-1145
  • Publisher : Techno-Press
  • DOI : 10.12989/sss.2015.16.6.1133
 Title & Authors
Nonlinear control of structure using neuro-predictive algorithm
Baghban, Amir; Karamodin, Abbas; Haji-Kazemi, Hasan;
 Abstract
A new neural network (NN) predictive controller (NNPC) algorithm has been developed and tested in the computer simulation of active control of a nonlinear structure. In the present method an NN is used as a predictor. This NN has been trained to predict the future response of the structure to determine the control forces. These control forces are calculated by minimizing the difference between the predicted and desired responses via a numerical minimization algorithm. Since the NNPC is very time consuming and not suitable for real-time control, it is then used to train an NN controller. To consider the effectiveness of the controller on probability of damage, fragility curves are generated. The approach is validated by using simulated response of a 3 story nonlinear benchmark building excited by several historical earthquake records. The simulation results are then compared with a linear quadratic Gaussian (LQG) active controller. The results indicate that the proposed algorithm is completely effective in relative displacement reduction.
 Keywords
structural control;active controller;neural network controller;neuro-predictive algorithm;model predictive control (MPC);fragility curves;
 Language
English
 Cited by
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An experimental study of vibration control of wind-excited high-rise buildings using particle tuned mass dampers,;;;;

Smart Structures and Systems, 2016. vol.18. 1, pp.93-115 crossref(new window)
1.
An experimental study of vibration control of wind-excited high-rise buildings using particle tuned mass dampers, Smart Structures and Systems, 2016, 18, 1, 93  crossref(new windwow)
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