Adaptive Neural Network Control with Backstepping for Surface Ships with Input Dead-Zone

This paper addresses the problem of adaptive neural network controller with backstepping technique for fully actuated surface vessels with input dead-zone. The combination of approximation-based adaptive technique and neural network system is used for approximating the nonlinear function of the ship...

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Published in:Mathematical problems in engineering 2013-01, Vol.2013 (2013), p.1-9
Main Authors: Wu, Huiyong, Zhao, Ang, Shao, Xingchao, Xia, Guoqing
Format: Article
Language:English
Subjects:
Adaptive control
Adaptive control systems
Approximation
Computer simulation
Control algorithms
Control theory
Controllers
Design
Engineering
Explicit knowledge
Fuzzy logic
International conferences
Mathematical models
Mathematical problems
Motion control
Network control
Neural networks
Nonlinear dynamics
Nonlinearity
Parameter estimation
Robots
Ship motion
Ships
Studies
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cited_by cdi_FETCH-LOGICAL-c389t-2be24095781ab84602ce6895c8deac039880b781fd96ece09f647f26d51b6bcf3
cites cdi_FETCH-LOGICAL-c389t-2be24095781ab84602ce6895c8deac039880b781fd96ece09f647f26d51b6bcf3
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container_title Mathematical problems in engineering
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creator Wu, Huiyong
Zhao, Ang
Shao, Xingchao
Xia, Guoqing
description This paper addresses the problem of adaptive neural network controller with backstepping technique for fully actuated surface vessels with input dead-zone. The combination of approximation-based adaptive technique and neural network system is used for approximating the nonlinear function of the ship plant. Through backstepping and Lyapunov theory synthesis, an indirect adaptive network controller is derived for dynamic positioning ships without dead-zone property. In order to improve the control effect, a dead-zone compensator is derived using fuzzy logic technique to handle the dead-zone nonlinearity. The main advantage of the proposed controller is that it can be designed without explicit knowledge about the ship motion model, and dead-zone nonlinearity is well compensated. A set of simulations is carried out to verify the performance of the proposed controller.
doi_str_mv 10.1155/2013/530162
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source Wiley-Blackwell Open Access Collection; Publicly Available Content Database (Proquest) (PQ_SDU_P3); IngentaConnect Journals
subjects Adaptive control
Adaptive control systems
Approximation
Computer simulation
Control algorithms
Control theory
Controllers
Design
Engineering
Explicit knowledge
Fuzzy logic
International conferences
Mathematical models
Mathematical problems
Motion control
Network control
Neural networks
Nonlinear dynamics
Nonlinearity
Parameter estimation
Robots
Ship motion
Ships
Studies
title Adaptive Neural Network Control with Backstepping for Surface Ships with Input Dead-Zone
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