NEUROKONTROL UNTUK VIBRASI PADA STRUKTUR MDOF NONLINIER HISTERESIS
Significant progress has been achieved in the active control of civil engineering structures, not only in the control algorithm, but also in the scale model testing and full-scale implementation. At the present time, most control algorithms used in the active control of structures are based on the o...
Saved in:
| Main Author: | |
|---|---|
| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/1641 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Significant progress has been achieved in the active control of civil engineering structures, not only in the control algorithm, but also in the scale model testing and full-scale implementation. At the present time, most control algorithms used in the active control of structures are based on the optimization of the instantaneous objective function. The artificial neural network controller is a newly developed technique for the purposes of control and has many attributes, such as massive parallelism, adaptability, robustness, and the inherent capability to handle nonlinear systems. In the structural control problems the sources of nonlinearity are caused by either large displacements or material nonlinearity and damage. These types of nonlinearity are possible under dynamic loading, especially during earthquakes. This thesis explores the possibilities of application of nonlinear structural control using neural networks to develop a robust structural control method. The control algorithm in the mathematically formulated methods is replaced by a neural network controller (neurocontroller). Two feed forward neural networks are utilized in a control system, one as an emulator and the other as a controller. A neural network based control algorithm has been developed and tested in the computer simulation of active control of structures. First, an emulator neural network has been trained to forecast the future response of the structure. Then the trained emulator has been used in developing the training data needed by neurocontroller. Finally the trained neurocontroller has been used in controlling the structure for different dynamic loading conditions. Results from computer simulation studies have shown great promise for the control of civil engineering structures under dynamic loadings using the artificial neural network controller. |
|---|