Neural network based reinforcement learning control of autonomous underwater vehicles with control input saturation

Rongxin Cui, Chenguang Yang, Yang Li, Sanjay Sharma

科研成果: 书/报告/会议事项章节会议稿件同行评审

18 引用 (Scopus)

摘要

In this paper, the trajectory tracking control of the autonomous underwater vehicle (AUV) has been investigated in discrete time, for ease of digital computer calculation. A reinforcement learning scheme is employed using two neural networks, whereas the first one is to compensate for uncertainties for the controller, and the second one is to estimate the evaluation function, such that optimal tracking performance could be achieve for the AUV. Simulation results show that the errors convergence to a adjustable neighborhood around zero, and optimization has been achieved in the sense of reinforcement learning.

源语言英语
主期刊名2014 UKACC International Conference on Control, CONTROL 2014 - Proceedings
出版商Institute of Electrical and Electronics Engineers Inc.
50-55
页数6
ISBN(电子版)9781479950119
DOI
出版状态已出版 - 1 10月 2014
活动10th UKACC International Conference on Control, CONTROL 2014 - Loughborough, 英国
期限: 9 7月 201411 7月 2014

出版系列

姓名2014 UKACC International Conference on Control, CONTROL 2014 - Proceedings

会议

会议10th UKACC International Conference on Control, CONTROL 2014
国家/地区英国
Loughborough
时期9/07/1411/07/14

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