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Insect-controlled Robot: A Mobile Robot Platform to Evaluate the Odor-tracking Capability of an Insect
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基于改进的松鼠搜索算法,对一台翻翼机器人进行模型识别.

Xiaoyang Wu1, Wei He2, Tingting Meng1

  • 1Key Laboratory of Intelligent Bionic Unmanned Systems, Ministry of Education, School of Intelligent Science and Technology, University of Science and Technology Beijing, Beijing 100083, China.

ISA transactions
|October 17, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的系统识别方法,用于飞翼机器人 (FWR),增强动态模型. 一个改进的松鼠搜索算法准确地识别参数,改善FWR控制的飞行特征预测.

关键词:
飞机器人 (FWR) 是一种机器人.松鼠搜索算法 松鼠搜索算法系统识别 系统识别验证 验证 验证 验证

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相关实验视频

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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
  • 航空航天工程 航空航天工程

背景情况:

  • 飞翼机器人 (FWR) 提出了复杂的动态建模挑战.
  • 准确的动态模型对于FWR控制和性能优化至关重要.
  • 现有的方法可能无法完全捕捉到羽翼动的复杂性.

研究的目的:

  • 开发一种新的系统识别方法,用于创建一个真正的动态模型的飞翼机器人 (FWR).
  • 解决和解决模型不确定性,源于飞翼动力学.
  • 验证已识别的动态模型在表示FWR飞行特征方面的有效性.

主要方法:

  • 构建一个独立的框架,为每一个翻动的翅膀,以提高模型的结构真实性.
  • 采用改进的松鼠搜索算法来识别动态模型的参数.
  • 通过实验测试在三个不同的机动中验证已识别的模型.

主要成果:

  • 提出的方法成功地识别了FWR动态模型中的未知参数.
  • 实验性比较证实了改进的松鼠搜索算法在参数识别中的效率.
  • 经过验证的动态模型准确地反映了在各种机动中未经调整的FWR的飞行特征.

结论:

  • 新的系统识别方法为FWR提供了一个真实的动态模型.
  • 改进的松鼠搜索算法在复杂的机器人系统中有效识别参数.
  • 这项研究为飞翼机器人应用中的先进控制策略奠定了基础.