航向(导航)
强化学习
稳健性(进化)
控制理论(社会学)
滑模控制
计算机科学
水下
控制器(灌溉)
模式(计算机接口)
控制工程
控制(管理)
工程类
人工智能
非线性系统
地质学
农学
化学
航空航天工程
生物化学
物理
操作系统
海洋学
基因
生物
量子力学
作者
Dianrui Wang,Yue Shen,Junhe Wan,Qixin Sha,Guangliang Li,Guanzhong Chen,Bo He
标识
DOI:10.1016/j.apor.2021.102960
摘要
For autonomous underwater vehicles (AUVs), control over AUV heading is of key importance to enable high-performance locomotion control. In this study, the heading control is achieved by using the robust sliding mode control (SMC) method. The performance of the controller can be seriously affected by its parameters. However, it is time-consuming and labor-intensive to manually adjust the parameters. Most of the existing methods rely on the accurate AUV model or prior knowledge, which are difficult to obtain. Therefore, this study is concerned with the problem of automatically tuning the SMC parameters through reinforcement learning (RL). First, an AUV dynamic model with and without current influence was successfully established. Second, a continuous hybrid Model-based Model-free (MbMf) RL method based on the deterministic policy gradient was introduced and explained. Then, the framework for tuning the parameters of SMC by the RL method was described. Finally, to demonstrate the robustness and effectiveness of our approach, extensive numerical simulations were conducted on the established AUV model. The results show that our method can automatically tune the SMC parameters. The performance is more effective than SMC with fixed parameters or SMC with a purely model-free learner.
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