在昆虫启发的飞行系统中基于机翼动力学的飞行控制策略:深度增强学习提供了解决方案,并激发了控制器设计在翻动MAV中的灵感
Yujing Xue1,2, Xuefei Cai1,2, Ru Xu1,2
1Shanghai Jiao Tong University and Chiba University International Cooperative Research Center (SJTU-CU ICRC), 800 Dongchuan Road, Minhang District, Shanghai 200240, China.
Biomimetics (Basel, Switzerland)
|July 28, 2023
概括
这项研究引入了一种新的深度强化学习 (DRL) 控制器,用于稳定大黄蜂的飞行. 基于机翼动力学的DRL方法有效地管理大干扰,增强生物灵感微型飞机的自主控制.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 生物启发工程 生物启发工程
- 空气动力学 航空动力学
背景情况:
- 昆虫通过翅膀的动,甚至在动荡的条件下,实现了非凡的飞行稳定性.
- 传统的控制器在与非线性动态和昆虫飞行的巨大扰动作斗争.
- 现有的方法往往无法为受昆虫启发的系统提供强大的6-DoF (六度自由度) 控制.
研究的目的:
- 开发和验证一种基于翼动力学的新型控制器,用于稳定在重大扰动下悬浮的熊蜂.
- 利用深度强化学习 (DRL) 来优化生物灵感飞行器的飞行控制策略.
- 解决传统控制器在处理复杂,非线性飞行动态方面的局限性.
主要方法:
- 通过将CFD数据驱动的空气动力学模型与6DoF飞行动力学模型集成在Open AI Gym框架内创建了一个高保真度的模拟环境.
- 一个基于机翼动力学的新型控制器被优化使用非政策的软演员-关键 (SAC) 算法,具有自动调整.
- 控制策略被训练成一个四维的行动空间来管理飞行动态.
主要成果:
- 经过DRL优化的控制器在实现大黄蜂悬浮的快速稳定方面展示了可行性和稳定性.
- 该系统成功地处理了全部6DoF大的干扰,超过了传统方法.
- 基于机翼动力学的DRL策略在自主飞行控制方面被证明是有效的.
结论:
- 拟议的基于6-DoF翼动力学的DRL控制策略为自主控制设计提供了一种高效的方法.
- 这种方法非常适用于生物灵感的翼微型空中飞行器 (MAV).
- 该研究强调了DLR在挑战性环境中促进MAV的稳定性和控制方面的潜力.
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