cc-DRL:一个凸的组合深度增强学习飞行控制设计的morphing四旋翼飞机
IEEE transactions on cybernetics
|July 9, 2025
概括
这项研究引入了一种新的凸合组合深度强化学习 (cc-DRL) 算法,用于控制变形四旋翼. 这种方法有效地管理了复杂的飞行动态,而传统模型失败了,使得精确的轨迹跟踪和稳定.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统 控制系统
- 航空航天工程 航空航天工程
背景情况:
- 变形四旋翼机通过结构变形提供了增强的飞行性能.
- 变形四旋翼的复杂飞行动力学使得准确的数学建模非常困难.
- 现有的基于模型的控制理论不足以改变四旋翼控制.
研究的目的:
- 提出一种新的飞行控制算法,用于将四旋翼变形为具有臂长变形的四旋翼.
- 解决这些复杂系统的轨迹跟踪和态度稳定方面的挑战.
- 在没有建模的动态存在时,克服基于模型的控制的局限性.
主要方法:
- 开发了一种凸组合深度强化学习 (cc-DRL) 方法.
- 使用无模型控制技术,特别是深度强化学习 (DRL).
- 靠近政策优化 (PPO) 用于在线训练控制法律在各种手臂长度模式.
- 凸合组合 (CC) 技术将训练的控制规律集成到一个统一的方案中.
主要成果:
- 拟议的cc-DRL算法证明了飞行轨迹跟踪的有效性.
- 实现了变形四旋翼机与臂长变形的态度稳定.
- 模拟结果验证了cc-DRL方法相对于传统方法的性能和优势.
结论:
- cc-DRL算法为控制具有复杂动态的变形四旋翼提供了可行的解决方案.
- 这种无模型的方法成功克服了基于模型的控制的局限性.
- 这项研究强调了DRL与凸形组合相结合的潜力,用于先进的飞行器控制.
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