Adaptive neural network control for ship steering system using filtered backstepping design

被引：9

作者：

MOT , Key Laboratory of Marine Simulation and Control, Navigation College, Dalian Maritime University, 116026 Liaoning, China ^{[1
]}

不详 ^{[2
]}

机构：

[1] MOT (Ministry of Transportation), Key Laboratory of Marine Simulation and Control, Navigation College, Dalian Maritime University

[2] Department of Insurance, School of Finance, Dongbei University of Finance and Economics

来源：

J. Appl. Sci. | / 10卷 / 1691-1697期

关键词：

Adaptive control; Filtered backstepping; Neural network; Ship steering system;

D O I：

10.3923/jas.2013.1691.1697

中图分类号：

学科分类号：

摘要：

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. © 2013 Asian Network for Scientific Information.

引用

页码：1691 / 1697

页数：6

共 11 条

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