基于强化学习的无人驾驶地表车辆系统的固定时间最佳时间变化的形成控制
Qiaokun Kang1, Qintao Gan1, Ruihong Li1
1Shijiazhuang Campus, Army Engineering University of PLA, Shijiazhuang, 050003, China.
ISA transactions
|August 9, 2025
概括
本研究介绍了无人地面车辆系统 (USVS) 的新增强化学习 (RL) 策略,以在固定的时间内实现最佳的形成控制,即使系统动态未知,状态无法测量.
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:
- 无人驾驶地面车辆系统 (USVS) 需要强大的控制策略来协调操作.
- 部分无法测量的状态和未知的动态在USVS形成控制中带来了重大挑战.
- 现有的控制方法往往难以同时保证固定时间的融合和最佳性能.
研究的目的:
- 为美国海军舰艇制定一个分布式的固定时间最佳时间变化的阵列控制 (TVFC) 战略.
- 解决部分无法测量的状态和未知的动态的系统.
- 为了实现形成控制和成本优化目标.
主要方法:
- 一个固定时间的自适应神经网络状态观测器 (FANNSO) 旨在重建未知的动态和无法测量的状态.
- 提出了一个带有指数项的分布式优化性能指数函数.
- 使用演员关键强化学习结构开发了一个分布式固定时间最佳TVFC策略.
主要成果:
- 拟议的控制策略确保错误信号保持在固定的时间内.
- 强化学习算法可自适应地调整控制器以获得最佳性能.
- 模拟结果验证了开发方法的有效性和优越性.
结论:
- 基于RL的TVFC新战略有效地处理了不确定性的USVS.
- FANNSO成功地重建了系统状态和动态.
- 该方法为USVS的固定时间最佳形成控制提供了一个强大的解决方案.
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