基于学习的容错的最佳形成控制直升机:一个增量完全执行系统方法
IEEE transactions on cybernetics
|September 24, 2025
概括
这项研究引入了一种增量完全执行系统方法 (FASA) 与强化学习 (RL) 来增强直升机形成控制,即使有故障的交叉板. 该方法确保了尽管执行器故障,强大的和最佳的形成性能.
科学领域:
- 航空航天工程 航空航天工程
- 控制系统 控制系统
- 机器人技术 机器人技术 机器人技术
背景情况:
- 直升机的形成控制对于任务至关重要,但受到执行器故障的挑战.
- 现有的方法通常需要准确的模型和大量的计算资源.
- 多直升机系统中的容错性仍然是一个活跃的研究领域.
研究的目的:
- 为多架直升机开发一个强大的和最佳的形成控制策略,这些直升机的冲击板有缺陷.
- 为了减轻执行器故障对形成稳定性和性能的影响.
- 为了减少对精确直升机动力学模型和计算负载的依赖.
主要方法:
- 建立了直升机模型,包括在执行器故障下的空气动力学,翻动和冲击板动力学.
- 应用了增量全动系统方法 (FASA) 来稳定构造并处理不确定性.
- 整合强化学习 (RL) 与批评网络,以优化对故障影响的控制策略.
主要成果:
- 增量FASA有效地抑制了一次性不确定性,并重新安排了系统动态.
- 基于RL的控制确保了尽管单个直升机执行器故障,但形成性能令人满意.
- 拟议的控制方案表明对模型准确性和计算资源的依赖减少.
结论:
- 综合的FASA和RL方法提供了稳定和高效的解决方案,用于控制直升机的形成与执行器故障.
- 数字模拟验证了拟议的控制方案的有效性和稳定性.
- 这种方法提高了成本效益,并简化了复杂的空中阵列的控制系统设计.
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