深度库普曼对非线性动力学和控制带驱动连续机器人的方法
Navid Feizi1, Filipe C Pedrosa2, Jagadeesan Jayender3
1Canadian Surgical Technologies and Advanced Robotics (CSTAR), London Health Sciences Centre, London, ON N6A 5A5, Canada, and with the School of Biomedical Engineering, Western University, London, ON N6A 3K7, Canada.
概括
这项研究引入了一种新的深度库普曼方法,用于高效建模肌驱动连续机器人 (TDCR). 该方法可以为医疗应用提供准确的实时控制,克服复杂的非线性动态.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制理论 控制理论
- 应用数学 应用数学 应用数学
背景情况:
- 肌驱动连续机器人 (TDCR) 在医学中非常有价值,因为它们的灵活性.
- 由于非线性,计算密集的连续力学,建模TDCR动态具有挑战性.
- 这些复杂的模型阻碍了TDCR的实时控制.
研究的目的:
- 为TDCR非线性动态开发一个高效的,以控制为导向的模型.
- 为了利用深度库普曼的方法来线性化复杂的系统动态.
- 为了实现TDCRs的精确实时位置控制.
主要方法:
- 应用了深度库普曼方法,将TDCR状态转换为非线性多重体.
- 在库普曼框架内,线性与双线性输入项的近似自主动态.
- 在线化的库普曼模型上使用线性二次控制器实现位置控制.
主要成果:
- 拟议的模型准确地捕捉了非线性和空间依赖的光谱变化.
- 实验验证在双带机器人导管上显示了低位置跟踪误差 (1.64毫米和0.60毫米).
- 该方法在多侧面和侧面轨迹方面都表现出有效性.
结论:
- 深度库普曼方法为建模TDCR动态提供了一种高效和准确的方法.
- 这种技术有助于在医疗应用中实时控制TDCR.
- 该研究强调了在各种TDCR系统中广泛适用的潜力.
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