预先定义的时间动态自动触发,对有干扰的自动地面车辆进行最佳控制
IEEE transactions on cybernetics
|December 15, 2025
概括
本研究引入了一种使用强化学习 (RL) 的自动地表车辆 (ASV) 的新控制方法. 动态自触发框架可以降低计算负载,同时确保稳定的运动控制.
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:
- 自主地表车辆 (ASV) 需要强大的控制策略来有效地在动态环境中导航.
- 干扰和计算限制对ASVs的实时运动控制构成重大挑战.
- 现有的控制方法在计算和通信负担方面往往缺乏效率.
研究的目的:
- 为面临干扰的ASV开发预定义时间的最佳运动控制策略.
- 实施一个动态的自我触发框架,以减少计算和通信开销.
- 通过强化学习在指定时间框架内确保稳定的ASV运动.
主要方法:
- 制定一个预定义时间的二级积分滑动模式控制 (SOISM) 策略.
- 使用单一关键网络开发预定义时间近似最佳运动 (AOM) 控制策略.
- 适应自动触发的动态框架的应用,用于自适应控制更新.
- 设计新型Lyapunov函数和触发条件用于稳定性分析.
主要成果:
- 拟议的SOISM和AOM控制策略有效地消除了对ASVs的干扰影响.
- 动态自动触发框架显著降低了计算和通信负担.
- 滑动模式动态和受干扰的ASV的稳定性在指定的时间框架内被证明.
- 模拟结果证实了开发的运动控制方法的有效性.
结论:
- 综合控制方法确保在干扰条件下SVS的稳定和最佳运动.
- 动态自动触发框架提高了控制系统的效率.
- 这项研究为ASV运动控制提供了强大且计算效率高的解决方案.
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