{"title":"Aerial Robots Carrying Flexible Cables: Dynamic Shape Optimal Control via Spectral Method Model","authors":"Yaolei Shen;Antonio Franchi;Chiara Gabellieri","doi":"10.1109/TRO.2025.3562459","DOIUrl":null,"url":null,"abstract":"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).","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"3162-3182"},"PeriodicalIF":9.4000,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Robotics","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10969810/","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ROBOTICS","Score":null,"Total":0}
引用次数: 0
Abstract
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).
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.