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Improved Integral LOS Guidance and Path-Following Control for an Unmanned Robot Sailboat via the Robust Neural Damping Technique

Published online by Cambridge University Press:  05 July 2019

Guoqing Zhang ,
Jiqiang Li ,
Bo Li  and
Xianku Zhang
Guoqing Zhang*
Affiliation:
(Navigation College, Dalian Maritime University, Dalian, China) (Collaborative Innovation Center for Transport Studies, Dalian Maritime University, Dalian, China)
Jiqiang Li
Affiliation:
(Navigation College, Dalian Maritime University, Dalian, China)
Bo Li
Affiliation:
(Navigation College, Dalian Maritime University, Dalian, China)
Xianku Zhang
Affiliation:
(Navigation College, Dalian Maritime University, Dalian, China)
*
(E-mail: zhangxk@dlmu.edu.cn)
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Abstract

This paper introduces a scheme for waypoint-based path-following control for an Unmanned Robot Sailboat (URS) in the presence of actuator gain uncertainty and unknown environment disturbances. The proposed scheme has two components: intelligent guidance and an adaptive neural controller. Considering upwind and downwind navigation, an improved version of the integral Line-Of-Sight (LOS) guidance principle is developed to generate the appropriate heading reference for a URS. Associated with the integral LOS guidance law, a robust adaptive algorithm is proposed for a URS using Radial Basic Function Neural Networks (RBF-NNs) and a robust neural damping technique. In order to achieve a robust neural damping technique, one single adaptive parameter must be updated online to stabilise the effect of the gain uncertainty and the external disturbance. To ensure Semi-Global Uniform Ultimate Bounded (SGUUB) stability, the Lyapunov theory has been employed. Two simulated experiments have been conducted to illustrate that the control effects can achieve a satisfactory performance.

Keywords

Path-followingURSIntegral LOS guidanceRobust neural damping technique

Information

Type
Research Article
Information
The Journal of Navigation , Volume 72 , Issue 6 , November 2019 , pp. 1378 - 1398
Copyright
Copyright © The Royal Institute of Navigation 2019 

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