Self-Organizing Neuro-Fuzzy Control for Underwater Robotic Vehicles. | ||||
| MEJ- Mansoura Engineering Journal | ||||
| Article 2, Volume 27, Issue 1, March 2002, Page 1-13 PDF (599.62 K) | ||||
| Document Type: Research Studies | ||||
| DOI: 10.21608/bfemu.2021.142440 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
A. Yassien1; Mohamed Sherif Moustafa Abou-ElMagd ElKasas2; Sabry Fouad Saraya  3; M. R. El-Basyouni4
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| 1Computer and System Department.,Faculty of Engineering., El-Mansoura University., Mansoura., Egypt | ||||
| 2Computer and System Department., Faculty of Engineering., El-Mansoura Univeersity., Mansoura., Egypt | ||||
| 3Computer and System Department., Faculty of Engineering., El-Mansoura University., Mansoura., Egypt | ||||
| 4Faculty of Specific Education., El-Mansoura University. | ||||
| Abstract | ||||
| Underwater robotic vehicles have become an important tool for various underwater tasks because they have greater speed, endurance and depth capability, as well as a higher factor of safety, than human divers. However, most vehicle control system designs have been based on a simplified vehicle model, which has often resulted in poor performance because the nonlinear and time-varying vehicle dynamics lave parameters uncertainty. It is desirable to have an advanced control system with the capability of learning and adapting to change in the vehicle dynamies and parameters. The proposed system is possessing neural network's learning ability. There are no rules initially in the proposed system. They are created and adapted as on-line leaning proceeds via simultaneous structure and parameter identification. The identification process of the controller includes both structure and parameter learning. | ||||
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