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概括
此摘要是机器生成的。

本研究介绍了灵活关节机器人 (FJR) 的新型控制器,该控制器使用辐射基函数神经网络 (RBFNN) 干扰观察器 (DOB) 来管理不确定性和干扰,提高控制精度.

关键词:
干扰补偿 干扰补偿干扰观察员 (DOB) 的意思是干扰观察员.式关节机器人 (FJR) 是一个机器人.非线性灵活动态的非线性灵活动态.辐射基础功能神经网络 (RBFNN)

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科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 控制系统工程 控制系统工程
  • 人工智能的人工智能

背景情况:

  • 灵活的联合机器人 (FJR) 由于参数不确定性和外部干扰,在精确控制方面面临挑战.
  • 传统的干扰观察器 (DOB) 需要精确的动态模型,这对于FJR来说很难实现,限制了它们的有效性.
  • 未建模的动态和外部干扰阻碍了工业机器人应用中的强大和高效的控制.

研究的目的:

  • 制定FJR的强有力的控制策略,以解决参数不确定性和外部时间变化的干扰.
  • 提高FJR控制在工业环境中的精度和可靠性.
  • 提高机器人系统中干扰补偿的计算效率.

主要方法:

  • 设计了一种混合控制器,将基于名义模型的干扰观察器 (DOB) 与辐射基函数神经网络 (RBFNN) 结合起来.
  • 一个自适应的RBFNN重量更新定律是使用Lyapunov稳定理论来选择性估计未建模的动态而制定的.
  • 控制器在FJR系统上经过实验验证.

主要成果:

  • 拟议的基于RBFNN的DOB有效地弥补了FJR中未建模的动态和外部干扰.
  • 适应性定律使选择性估计成为可能,最大限度地减少计算负载,同时确保强大的补偿.
  • 实验结果证实了混合控制器在提高控制精度和处理快速变化的干扰方面的有效性.

结论:

  • 混合RBFNNDOB状态反控制器为控制具有不确定性和干扰的FJR提供了强大的和计算效率高的解决方案.
  • 基于Lyapunov的自适应方法确保了稳定性和准确的跟踪误差限制.
  • 这种方法显著提高了DOB在复杂机器人系统中的应用性和性能.