通过Actor-Critic强化学习,通过多个无人水面船的自适应性最佳周围控制
IEEE transactions on neural networks and learning systems
|October 18, 2024
概括
本研究介绍了使用演员关键强化学习 (RL) 对多个无人水面船 (USV) 的最佳周围控制算法. 这种新方法可以将轨迹长度和能源消耗减少20%以上.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 人工智能的人工智能
- 海洋工程 海洋工程
背景情况:
- 协调多艘无人水面船 (USV) 提出了复杂的控制挑战.
- 现有的USV环绕控制方法经常与非线性和优化作斗争.
- 强化学习 (RL) 为动态环境中的自适应控制提供了一种有前途的方法.
研究的目的:
- 为多个USVs开发一个最佳的周围控制算法.
- 利用关键演员的强化学习 (RL) 来优化USV的合并过程.
- 为了确保USV形成控制的稳定性和效率.
主要方法:
- 通过汉密尔顿 - 雅各比 - 贝尔曼 (HJB) 方程制定多个USV最佳周围控制问题.
- 实现一个基于贝尔曼残余错误的网络更新规律的自适应性演员-关键RL控制范式.
- 在二级USV中使用虚拟控制器和实际控制器,以确保最佳的周围控制.
- 使用利亚普诺夫理论函数分析控制器稳定性.
主要成果:
- 拟议的基于RL的演员关键环境控制器成功实现了周围的目标.
- 该算法优化了多个USV的演变过程.
- 与现有控制器相比,观察到轨道长度显著减少9.76%和能源消耗减少20.85%.
结论:
- 开发的演员关键RL算法为多个USV的最佳周围控制提供了有效的解决方案.
- 这种方法通过减少轨迹长度和能源消耗来提高效率.
- 该方法在复杂的USV协调任务中表现出强大的稳定性和最佳性能.
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