Dynamic Modeling and Adaptive Controlling in GPS-Intelligent Buoy (GIB) Systems Based on Neural-Fuzzy Networks

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.

A Novel Hybrid Data-Driven Modeling Method for Missiles

This paper proposes a novel hybrid data-driven modeling method for missiles. Based on actual flight test data, the missile hybrid model is established by combining neural networks and the mechanism modeling method, considering the uncertainties and nonlinear factors in missiles. This method can avoid the problems in missile mechanism modeling and traditional data-driven modeling, and can also provide a solution for nonlinear dynamic system modeling problems in offline usage scenarios. Finally, the feasibility of the proposed method and the credibility of the established model are verified by simulation experiments and statistical analysis.