PID with Deep Reinforcement Learning and Heuristic Rules for Autonomous UAV Landing

被引：0

作者：

Yuan, Man ^{[1
]}

Wang, Chang ^{[1
]}

Zhang, Pengpeng ^{[2
]}

Wei, Changyun ^{[2
]}

机构：

[1] Natl Univ Def Technol, Changsha 410073, Hunan, Peoples R China

[2] Hohai Univ, Changzhou 213022, Jiangsu, Peoples R China

来源：

PROCEEDINGS OF 2022 INTERNATIONAL CONFERENCE ON AUTONOMOUS UNMANNED SYSTEMS, ICAUS 2022 | 2023年 / 1010卷

基金：

中国国家自然科学基金;

关键词：

UAV landing; Deep reinforcement learning; DDPG; PID; STRATEGY;

D O I：

10.1007/978-981-99-0479-2_174

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Unmanned Aerial Vehicle (UAV) landing is a challenging task in dynamic environments. The PID controller can be used, but it suffers from the problem of manual parameter tuning. In this paper, we propose that PID can be combined with deep reinforcement learning to learn the PID parameters autonomous learning. Besides, heuristic rules of how to adjust the PID parameters can be used to further speed up the learning. We demonstrate the effectiveness of the proposed method in a simulated quadrotor UAV landing task.

引用

页码：1876 / 1884

页数：9

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