Title :
Design of Ship Controller and Ship Model Based on Neural Network Identification Structures
Author_Institution :
Fac. of Electr. Eng. Sarajevo, Sarajevo
Abstract :
This paper proposes computationally efficient artificial neural network models for identification of both fuzzy logic ship controller and nonlinear ship model. The first objective demonstrates how to use a nonlinear network to identify the fuzzy controller and compare control surfaces of these two controllers as well as performance indices. The second objective is to use the nonlinear network to identify nonlinear plant in recursive on-line mode and the third one is to integrate designed two neural networks in one control scheme to test resulting system response in the closed loop system.
Keywords :
closed loop systems; fuzzy control; fuzzy logic; neurocontrollers; nonlinear control systems; ships; closed loop system; fuzzy logic ship controller; neural network identification structure; nonlinear ship model; Artificial neural networks; Closed loop systems; Computer networks; Control systems; Fuzzy control; Fuzzy logic; Marine vehicles; Neural networks; Nonlinear control systems; System testing; Ship dynamics; adaptive learning rate; course-keeping; fuzzy logic controller; identification; neural networks; trajectory tracking;
Conference_Titel :
Automation Congress, 2006. WAC '06. World
Conference_Location :
Budapest
Print_ISBN :
1-889335-33-9
DOI :
10.1109/WAC.2006.376022