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基于特征增强的自适应神经网络控制用于四旋翼机.

Bang Song1, Mengxing Huang1

  • 1School of Information and Communication Engineering, Hainan University, Haikou 570228, China.

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概括
此摘要是机器生成的。

本研究引入了一种适应神经网络 (ANN) 控制器,用于四旋翼飞机的功能增强 (FA),增强干扰估计和学习率,以实现稳定的飞行控制.

关键词:
适应神经网络 (ANN) 是一种神经网络.功能增强 (FA) 的功能增强 (FA)输入到状态稳定 (ISS)四旋翼机四旋翼机四旋翼机状态预测器 (SP)

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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 控制系统
  • 人工智能的人工智能

背景情况:

  • 四旋翼控制受到未知的内部和外部干扰的挑战.
  • 现有的自适应神经网络 (ANN) 控制器需要提高学习精度和速度.
  • 功能增强 (FA) 和状态预测 (SP) 为增强ANN性能提供了潜在的解决方案.

研究的目的:

  • 为四旋翼机设计一个自适应神经网络 (ANN) 控制器.
  • 用特征增强 (FA) 和状态预测器 (SP) 来提高ANN控制器的学习精度和速度.
  • 为了确保闭环控制系统的稳定性.

主要方法:

  • 设计了一个具有两个组件结构的自适应神经网络 (ANN) 控制器 (位置和态度子控制器).
  • 实现了特征增强 (FA),以增强ANN学习数据特征的能力.
  • 引入了一个状态预测器 (SP) 来预测状态错误并优化 ANN 的学习速度.

主要成果:

  • 拟议的ANN控制器有效地估计了四旋翼机中未知干扰项.
  • 功能增强 (FA) 提高了ANN的学习准确性.
  • 状态预测器 (SP) 成功提高了ANN的学习率.
  • 稳定性分析证实闭环系统是输入到状态稳定的 (ISS).

结论:

  • 开发的具有特征增强 (FA) 和状态预测 (SP) 的自适应神经网络 (ANN) 控制器展示了四旋翼控制的卓越性能.
  • 控制器有效处理未知的干扰,确保系统的稳定性.
  • 通过模拟和实验平台验证,拟议的控制算法为四旋翼应用提供了强大的解决方案.