Observer Design for an AUV Intercepting Targets Based on Nonlinear-in-Parameter Neural Network

被引：0

作者：

Guo, Xinxin ^{[1
]}

Yan, Weisheng ^{[1
]}

Cui, Peng ^{[1
]}

机构：

[1] Northwestern Polytech Univ, Sch Marine Sci & Technol, Xian 710072, Peoples R China

来源：

IEEE ICARM 2016 - 2016 INTERNATIONAL CONFERENCE ON ADVANCED ROBOTICS AND MECHATRONICS (ICARM) | 2016年

关键词：

UNDERWATER VEHICLE; IDENTIFICATION;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

To realize the system identification of an Autonomous Underwater Vehicle (AUV), a state observer based on nonlinear-in-parameter neural network is designed, which can be divided into two parts, namely, a nonlinear neural network and a conventional observer. Simulation studies are carried out, which are fixed target interception and moving target interception. The simulation results show that the presented observer can identify the AUV system states with unknown kinematics and dynamics model.

引用

页码：388 / 393

页数：6

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