Path following method for AUV based on Q-Learning and RBF neural network
In the underwater docking process, the oscillation on AUV velocity brings extra challenge on AUV path following. A Q-learning based Sliding Mode Control (SMC) method to increase the path following performances is proposed. Firstly, AUV guidance law is designed to reduce the path following error. Hea...
Format: | Article |
---|---|
Language: | zho |
Published: |
The Northwestern Polytechnical University
2021-06-01
|
Series: | Xibei Gongye Daxue Xuebao |
Subjects: | |
Online Access: | https://www.jnwpu.org/articles/jnwpu/full_html/2021/03/jnwpu2021393p477/jnwpu2021393p477.html |
Similar Items
-
Deep Interactive Reinforcement Learning for Path Following of Autonomous Underwater Vehicle
by: Qilei Zhang, et al.
Published: (2020-01-01) -
Research Status and Prospect of AUV Path Planning Algorithms
by: GUO Yinjing, MENG Qingliang, KONG Fang, LYU Wenhong
Published: (2020-12-01) -
Adaptive Variable Structure Control With Neuron for Path Tracking of Beaver AUV
by: Lin-Lin Wang, et al.
Published: (2020-01-01) -
Bilevel Optimization-Based Time-Optimal Path Planning for AUVs
by: Xuliang Yao, et al.
Published: (2018-11-01) -
A Data-Driven Intermittent Online Coverage Path Planning Method for AUV-Based Bathymetric Mapping
by: Jianguang Shi, et al.
Published: (2020-09-01)