Neural Network for Structural Dynamic Model Identification

1995 ◽  
Vol 121 (12) ◽  
pp. 1377-1381 ◽  
Author(s):  
H. M. Chen ◽  
G. Z. Qi ◽  
J. C. S. Yang ◽  
F. Amini
Keyword(s):  
Neural Network ◽  
Dynamic Model ◽  
Model Identification ◽  
Structural Dynamic ◽  
Structural Dynamic Model

scholarly journals Neural Network Identification and Control of a Parametrically Excited Structural Dynamic Model of an F-15 Tail Section

2000 ◽  
Vol 7 (6) ◽  
pp. 355-361 ◽  
Author(s):  
Ayman A. El-Badawy ◽  
Ali H. Nayfeh ◽  
Hugh Van Landingham
Keyword(s):  
Neural Network ◽  
Computer Simulation ◽  
Dynamic Model ◽  
Parametric Excitation ◽  
Vibration Suppression ◽  
Great Promise ◽  
Structural Dynamic ◽  
Neural Network Controller ◽  
Structural Dynamic Model ◽  
Computer Simulation Studies

We investigated the design of a neural-network-based adaptive control system for a smart structural dynamic model of the twin tails of an F-15 tail section. A neural network controller was developed and tested in computer simulation for active vibration suppression of the model subjected to parametric excitation. First, an emulator neural network was trained to represent the structure to be controlled and thus used in predicting the future responses of the model. Second, a neurocontroller to determine the necessary control action on the structure was developed. The control was implemented through the application of a smart material actuator. A strain gauge sensor was assumed to be on each tail. Results from computer-simulation studies have shown great promise for control of the vibration of the twin tails under parametric excitation using artificial neural networks.

scholarly journals Omnidirectional Mobile Robot Dynamic Model Identification by NARX Neural Network and Stability Analysis Using the APLF Method

Symmetry ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1430
Author(s):  
Liang Xin ◽  
Yuchao Wang ◽  
Huixuan Fu
Keyword(s):  
Neural Network ◽  
Stability Analysis ◽  
Mobile Robot ◽  
Dynamic Model ◽  
Complex Dynamics ◽  
Model Identification ◽  
Theoretical Method ◽  
Omnidirectional Mobile Robot ◽  
Path Dependent ◽  
Narx Neural Network

In this paper, the NARX neural network system is used to identify the complex dynamics model of omnidirectional mobile robot while rotating with moving, and analyze its stability. When the mobile robot model rotates and moves at the same time, the dynamic model of the mobile robot is complex and there is motion coupling. The change of the model in different states is a kind of symmetry. In order to solve the problem that there is a big difference between the mechanism modeling motion simulation and the actual data, the dynamic model identification of mobile robot in special state based on NARX neural network is proposed, and the stability analysis method is given. To verify that the dynamic model of NARX identification is consistent with that of the mobile robot, the Activation Path-Dependent Lyapunov Function (APLF) algorithm is used to distinguish the NARX neural network model expressed by LDI. However, the APLF method needs to calculate a large number of LMIs in practice and takes a lot of time, and, to solve this problem, an optimized APLF method is proposed. The experimental results verify the effectiveness of the theoretical method.

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