Adaptive Neural Network Control for Marine Shafting System Using Dynamic Surface Control

2007 ◽  
Author(s):  
Pey Yuen Tao ◽  
Shuzhi Sam Ge ◽  
Tong Heng Lee
Keyword(s):  
Neural Network ◽  
Dynamic Surface Control ◽  
Network Control ◽  
Neural Network Control ◽  
Adaptive Neural Network ◽  
Dynamic Surface ◽  
Surface Control ◽  
Adaptive Neural Network Control

Adaptive neural network control for course-keeping of ships with input constraints

2017 ◽  
Vol 41 (4) ◽  
pp. 1010-1018 ◽  
Author(s):  
Qingling Wang ◽  
Changyin Sun ◽  
Yangyang Chen
Keyword(s):  
Neural Network ◽  
Control Method ◽  
Dynamic Surface Control ◽  
Backstepping Technique ◽  
Input Constraints ◽  
Adaptive Neural Network ◽  
Dynamic Surface ◽  
Controlled System ◽  
Surface Control ◽  
Adaptive Neural Network Control

In this paper, an adaptive neural network (NN) control method is proposed for the problem of nonlinear course control of ships with input constraints and unknown direction control gains. Specifically, dynamic surface control is used to overcome the problem of explosion of complexity inherent in the backstepping technique, and the Nussbaum function is employed to deal with the unknown signs of control gains. It is proved that the proposed adaptive NN control method, which is composed of dynamic surface control and a backstepping technique with the Nussbaum gain function, is able to guarantee uniform ultimate boundedness of all the signals in the controlled system. In addition, the tracking error between the output of the controlled system and a desired trajectory is shown to converge to a small neighbourhood of the origin. Finally, one example is introduced to illustrate the proposed theoretical results.

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