承载柔性电缆的空中机器人:基于谱法模型的动态形状最优控制

IF 9.4 1区 计算机科学 Q1 ROBOTICS
Yaolei Shen;Antonio Franchi;Chiara Gabellieri
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引用次数: 0

摘要

在这项工作中,我们提出了一种基于模型的最优边界控制设计,用于由携带柔性电缆的四旋翼组成的空中机器人系统。整个系统采用偏微分方程结合常微分方程描述的边界条件进行建模。采用适当的正交分解(POD)方法将原无限维系统投影到由正交基函数张成的有限低维空间上。基于该降阶模型,在线实现非线性模型预测控制,以最优预测方式实现柔性电缆的位置和形状轨迹跟踪。本文提出的基于pod的简化建模和最优控制范式通过基于精确高维有限差分法的模型进行了仿真验证,并使用真实的四旋翼和电缆进行了实验验证。结果表明,基于pod的预测控制方法(允许在整个系统状态下关闭控制回路)的可行性,以及与最佳调谐比例积分导数(PID)控制器(允许仅在四旋翼状态下关闭控制回路)相比,其优越的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Aerial Robots Carrying Flexible Cables: Dynamic Shape Optimal Control via Spectral Method Model
In this work, we present a model-based optimal boundary control design for an aerial robotic system composed of a quadrotor carrying a flexible cable. The whole system is modeled by partial differential equations combined with boundary conditions described by ordinary differential equations. The proper orthogonal decomposition (POD) method is adopted to project the original infinite-dimensional system on a finite low-dimensional space spanned by orthogonal basis functions. Based on such a reduced-order model, nonlinear model predictive control is implemented online to realize both position and shape trajectory tracking of the flexible cable in an optimal predictive fashion. The proposed POD-based reduced modeling and optimal control paradigms are verified in simulation using an accurate high-dimensional finite difference method-based model and experimentally using a real quadrotor and a cable. The results show the viability of the POD-based predictive control approach (allowing to close the control loop on the full system state) and its superior performance compared to an optimally tuned proportional–integral–derivative (PID) controller (allowing to close the control loop on the quadrotor state only).
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来源期刊
IEEE Transactions on Robotics
IEEE Transactions on Robotics 工程技术-机器人学
CiteScore
14.90
自引率
5.10%
发文量
259
审稿时长
6.0 months
期刊介绍: The IEEE Transactions on Robotics (T-RO) is dedicated to publishing fundamental papers covering all facets of robotics, drawing on interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, and beyond. From industrial applications to service and personal assistants, surgical operations to space, underwater, and remote exploration, robots and intelligent machines play pivotal roles across various domains, including entertainment, safety, search and rescue, military applications, agriculture, and intelligent vehicles. Special emphasis is placed on intelligent machines and systems designed for unstructured environments, where a significant portion of the environment remains unknown and beyond direct sensing or control.
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