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Journal of Applied Sciences
  Year: 2013 | Volume: 13 | Issue: 10 | Page No.: 1691-1697
DOI: 10.3923/jas.2013.1691.1697
Adaptive Neural Network Control for Ship Steering System Using Filtered Backstepping Design
Junsheng Ren and Lu Liu

Abstract:
As regards ship course control, the ship is characterized by a nonlinear function with uncertainties, representing maneuvering characteristics. This study addresses the design of adaptive controller for ship steering system. The control objective is to drive the course to track a prescribed time-varying signal. We use filtered backstepping method to design the control law. Radial Basis Function (RBF) neural network learns the system’s uncertainties and nonlinearities online. An adaptive law is combined with a control design including a filtered backstepping controller and RBF neural network approximator. Our analysis revealed even if there is no a priori knowledge about ship's system dynamics, the design can guarantee the ultimately uniformly boundedness for ship steering closed-loop system. Furthermore, the controller contains only one online learning parameter and the laborious differential computation in conventional ship method become unnecessary. Ship maneuvering scenario is simulated to verify the effectiveness of our approach.
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How to cite this article:

Junsheng Ren and Lu Liu, 2013. Adaptive Neural Network Control for Ship Steering System Using Filtered Backstepping Design. Journal of Applied Sciences, 13: 1691-1697.

DOI: 10.3923/jas.2013.1691.1697

URL: https://scialert.net/abstract/?doi=jas.2013.1691.1697

 
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