最佳DMD库普曼数据驱动控制一个虫机器人的控制
Mehran Rahmani1, Sangram Redkar1
1The Polytechnic School, Ira Fulton School of Engineering, Arizona State University, Mesa, AZ 85212, USA.
Biomimetics (Basel, Switzerland)
|November 26, 2024
概括
这项研究介绍了一种最佳的数据驱动控制器,用于生物灵感的虫机器人. 它使用库普曼理论和动态模式分解来线性化机器人.
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
- 生物启发工程 生物启发工程
背景情况:
- 生物灵感机器人模仿自然系统,而虫机器人在医学和救援方面具有潜力.
- 由于非线性动力学和外部噪声,控制虫机器人是复杂的.
- 现有的控制方法与这些机器人固有的复杂性作斗争.
研究的目的:
- 开发一个最佳的数据驱动控制器,以提高虫机器人的机动性.
- 为应对虫机器人控制中非线性动力学和外部干扰所带来的挑战.
- 通过模拟验证一种新型控制策略的有效性.
主要方法:
- 从虫机器人的非线性动态模型获取数据.
- 考普曼理论的应用来导出一个线性动态模型.
- 使用动态模式分解 (DMD) 来计算库普曼运算符.
- 实施线性二次调节器 (LQR) 进行控制.
主要成果:
- 通过库普曼理论成功对虫机器人的非线性动力学进行线性化.
- 通过动态模式分解有效生成库普曼运算符.
- 在模拟中证明了线性二次调节器 (LQR) 控制器的性能.
- 验证拟议的数据驱动控制方法的有效性.
结论:
- 建议的最佳数据驱动控制器有效地管理虫机器人的动态.
- 库普曼理论和DMD为线性化复杂的机器人系统提供了强大的框架.
- 该LQR控制器确保稳定和准确的控制,为实际应用铺平了道路.
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