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一个基于RBFNN的规定的性能控制器,用于航天器近距离操作,以避免碰撞.

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概括

一个新的自适应性强大的控制器通过使用规定的性能控制 (PPC) 和辐射基函数神经网络 (RBFNN) 来增强用于轨道组装的航天器控制. 这确保了灵活结构的精确拖动,同时避免碰撞.

关键词:
适应性神经控制是一种自适应性神经控制.运动控制器运动控制器预先定义的边界界.太空飞船 太空飞船 太空飞船

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科学领域:

  • 机器人和控制系统 机器人和控制系统
  • 航空航天工程 航空航天工程
  • 人工智能的人工智能

背景情况:

  • 在轨道组装 (OOA) 任务需要航天器拖动大型,灵活的结构,面临来自未建模的动态和碰撞风险的挑战.
  • 由于航天器和有效载荷之间的复杂合动态,精确的控制是很困难的,并且近距离操作要求严格避免碰撞.

研究的目的:

  • 开发一种新的适应性强控制器,用于在OOA场景中拖动大型灵活结构时控制航天器的轨道.
  • 整合规定的性能控制 (PPC) 以保证错误界限和避免碰撞,与辐射基函数神经网络 (RBFNN) 集成,以在线补偿未知的动态.

主要方法:

  • 实施规定的性能控制 (PPC) 框架,以执行时间变化的错误界限,确保避免内在碰撞.
  • 利用射线基函数神经网络 (RBFNN) 来在线近似组合航天器-架构系统的未知非线性动态.
  • 通过数值模拟进行验证,将拟议的控制器与传统的PID控制器进行比较,并在卫星模拟器上进行硬件实验.

主要成果:

  • 拟议的控制器在模拟中显示出与PID控制器相比更高的跟踪精度,严格遵守规定的错误约束.
  • 硬件实验证实了控制器的有效性,实现了高精度的轨迹跟踪,误差约为5毫米.
  • 追踪错误始终保持在预定义的安全边界内,验证了控制器的稳定性和安全性.

结论:

  • 开发的自适应性强控制器为轨道建筑中的复杂近距离操作提供了可靠和安全的解决方案.
  • 集成PPC和RBFNN有效地管理来自灵活有效载荷和外部干扰的不确定性,而不需要精确的动态模型.
  • 这种方法显著提高了航天器在太空组装任务中拖动大型结构的控制精度和安全性.