Version 1
: Received: 4 April 2020 / Approved: 8 April 2020 / Online: 8 April 2020 (08:51:47 CEST)
How to cite:
Zhang, D.; Aqeel Ashraf, M.; Liu, Z.; Peng, W.; Golkar, M.J.; Mosavi, A. Dynamic Modeling and Adaptive Controlling in GPS-Intelligent Buoy (GIB) Systems Based on Neural-Fuzzy Networks. Preprints2020, 2020040123. https://doi.org/10.20944/preprints202004.0123.v1
Zhang, D.; Aqeel Ashraf, M.; Liu, Z.; Peng, W.; Golkar, M.J.; Mosavi, A. Dynamic Modeling and Adaptive Controlling in GPS-Intelligent Buoy (GIB) Systems Based on Neural-Fuzzy Networks. Preprints 2020, 2020040123. https://doi.org/10.20944/preprints202004.0123.v1
Zhang, D.; Aqeel Ashraf, M.; Liu, Z.; Peng, W.; Golkar, M.J.; Mosavi, A. Dynamic Modeling and Adaptive Controlling in GPS-Intelligent Buoy (GIB) Systems Based on Neural-Fuzzy Networks. Preprints2020, 2020040123. https://doi.org/10.20944/preprints202004.0123.v1
APA Style
Zhang, D., Aqeel Ashraf, M., Liu, Z., Peng, W., Golkar, M.J., & Mosavi, A. (2020). Dynamic Modeling and Adaptive Controlling in GPS-Intelligent Buoy (GIB) Systems Based on Neural-Fuzzy Networks. Preprints. https://doi.org/10.20944/preprints202004.0123.v1
Chicago/Turabian Style
Zhang, D., Mohammad Javad Golkar and Amir Mosavi. 2020 "Dynamic Modeling and Adaptive Controlling in GPS-Intelligent Buoy (GIB) Systems Based on Neural-Fuzzy Networks" Preprints. https://doi.org/10.20944/preprints202004.0123.v1
Abstract
Recently, various relations and criteria have been presented to establish a proper relationship between control systems and control Global Positioning System (GPS)-intelligent buoy system. Given the importance of controlling the position of buoys and the construction of intelligent systems, in this paper, dynamic system modeling is applied to position marine buoys through the improved neural network with a backstepping technique. This study aims at developing a novel controller based on adaptive fuzzy neural network to optimally track the dynamically positioned vehicle on water with unavailable velocities and unidentified control parameters. In order to model the network with the proposed technique, uncertainties and the unwanted disturbances are studied in the neural network. The presented study aims at developing a neural controlling which applies the vectorial back-stepping technique to the surface ships, which have been dynamically positioned with undetermined disturbances and ambivalences. Moreover, the objective function is to minimize the output error for the neural network (NN) based on closed-loop system. The most important feature of the proposed model for the positioning buoys is its independence from comparative knowledge or information on the dynamics and the unwanted disturbances of ships. The numerical and obtained consequences demonstrate that the controller system can adjust the routes and the position of the buoys to the desired objective with relatively few position errors.
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
