基于强化学习的可变扫描飞机的轨迹跟踪控制
1The College of Information Engineering and Artificial Intelligence, YangZhou University, Yangzhou 225009, China.
Biomimetics (Basel, Switzerland)
|May 24, 2024
概括
本研究介绍了一种增量深度确定性政策梯度 (IDDPG) 算法,用于不确定的飞机轨迹跟踪. 增强的IDDPG控制器有效地管理了变形的飞机控制,尽管环境干扰.
科学领域:
- 航空航天工程 航空航天工程
- 控制系统 控制系统
- 人工智能的人工智能
背景情况:
- 轨迹跟踪对于先进的飞机机动作至关重要.
- 不确定性和环境变化给飞行控制带来了重大挑战.
- 现有的控制方法可能会与变形飞机的动态复杂性作斗争.
研究的目的:
- 开发一种先进的控制算法,用于在不确定的环境中跟踪轨迹.
- 提高飞行控制人员适应动态飞行条件的能力.
- 为了验证拟议的控制器对变形飞机的有效性.
主要方法:
- 开发了一个增量深度决定性政策梯度 (IDDPG) 算法.
- 视线 (LOS) 方法被整合到路径跟踪中.
- 为了提高适应性,纳入了长短期记忆 (LSTM) 单元.
- 根据位置和态度错误制定了一个奖励函数.
主要成果:
- IDDPG算法证明了四翼可变扫描 (FWVS) 飞机的有效轨迹跟踪.
- 由于LSTM集成,控制器显示出更好的适应环境变化的能力.
- 用干扰因子进行的模拟证实了控制器在跟踪登轨迹方面的稳定性.
结论:
- 拟议的IDDPG算法为不确定性下的FWVS飞机的轨迹跟踪控制提供了一个强大的解决方案.
- 集成LSTM单元显著提高了控制器适应动态飞行条件的适应性.
- 开发的控制器在与外部干扰复杂的机动作过程中有效地管理变形飞机.
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