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非线性动态建模和基于模型的AI驱动的控制磁活性软连续机器人在流体环境中的控制
Seyed Alireza Moezi1, Ramin Sedaghati1, Subhash Rakheja1
1Department of Mechanical, Industrial and Aerospace Engineering, Concordia University, 1455 De Maisonneuve Blvd. West, Montreal, QC H3G 1M8, Canada.
ISA transactions
|November 6, 2023
概括
本研究介绍了一种新的动态模型和人工智能控制,用于在流体环境中的磁活性软连续机器人 (MSCR). 先进的控制方法准确地引导MSCR,在模拟的身体条件下表现出卓越的性能.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 生物医学工程 生物医学工程
- 控制系统 控制系统
背景情况:
- 磁活性软连续机器人 (MSCRs) 由于其多式联动机器人,因此对生物医学应用具有前景.
- 准确的建模和控制对于在复杂的生物环境中指导MSCR至关重要.
- 了解合的磁力机械行为和流体相互作用对于MSCR开发至关重要.
研究的目的:
- 为MSCRs开发一种新的非线性动态模型,以考虑流体阻尼和阻力.
- 在模拟的生物流体环境中创建基于AI的基于模型的控制方法,用于精确的MSCR导航.
- 通过实验评估和比较来验证模型和控制策略.
主要方法:
- 开发了一种非线性磁粘弹性动态模型,该模型结合了流体流动的阻尼和阻力.
- 一个分数顺序的滑动模式控制 (FOSMC) 算法,与深度强化学习 (DRL) 集成,被设计用于轨迹跟踪.
- 建立了一个硬件在循环中的实验框架,以在各种条件下测试DRL-FOSMC算法.
主要成果:
- 开发的动态模型显示了MSCR响应的理论预测和实验数据之间的准确相关性.
- 在模拟的流体环境中,DRL-FOSMC算法证明了增强的跟踪性能和减少聊天.
- 实验案例研究证实了拟议的DRL-FOSMC算法在现有控制方法上的优越性.
结论:
- 新的动态模型准确地捕捉了MSCRs在流体环境中的行为.
- 由人工智能驱动的DRL-FOSMC为在模拟的生物条件下导航MSCR提供了有效和精确的控制.
- 这项研究提升了MSCRs在未来生物医学应用中的潜力,这些应用需要精确的体内转向.
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