Ronghua Zhang , Yaonan Wang , Wenfang Xie , Pengcheng Li , Haoran Tan , Yiming Jiang
{"title":"Adaptive finite-time synchronized control of multi-robotic fiber placement system with model uncertainties and disturbances","authors":"Ronghua Zhang , Yaonan Wang , Wenfang Xie , Pengcheng Li , Haoran Tan , Yiming Jiang","doi":"10.1016/j.isatra.2025.05.022","DOIUrl":null,"url":null,"abstract":"<div><div>The use of multiple robots to manufacture composite components represents a critical development direction for fiber placement systems (FPSs). In multi-robotic fiber placement systems (MRFPSs) with heterogeneous mechanical structures, robots collaborate to perform fiber placement tasks. Consequently, robot synchronization emerges as a primary factor in determining the performance of the fiber placement process. However, the difficulty in establishing accurate system models and the presence of disturbances are two significant challenges to achieving precise robot synchronization. Additionally, the system is expected to exhibit desirable dynamic characteristics, such as finite-time error convergence. To address these issues and requirements, we propose a novel adaptive finite-time synchronization control (AFSC) algorithm for the system. Specifically, a finite-time sliding mode observer is developed to handle kinematic uncertainty. A novel fast non-singular terminal sliding mode (FNTSM) manifold is constructed in the AFSC algorithm. Moreover, the control algorithm integrates an adaptive law to handle dynamic uncertainty and an adaptive term to counteract disturbances. Performance analysis demonstrates that the AFSC ensures that the coupled, synchronization, and tracking errors converge to zero within finite time. Furthermore, simulations and experiments are conducted to validate the effectiveness of the AFSC algorithm.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"164 ","pages":"Pages 197-210"},"PeriodicalIF":6.5000,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ISA transactions","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0019057825002563","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
The use of multiple robots to manufacture composite components represents a critical development direction for fiber placement systems (FPSs). In multi-robotic fiber placement systems (MRFPSs) with heterogeneous mechanical structures, robots collaborate to perform fiber placement tasks. Consequently, robot synchronization emerges as a primary factor in determining the performance of the fiber placement process. However, the difficulty in establishing accurate system models and the presence of disturbances are two significant challenges to achieving precise robot synchronization. Additionally, the system is expected to exhibit desirable dynamic characteristics, such as finite-time error convergence. To address these issues and requirements, we propose a novel adaptive finite-time synchronization control (AFSC) algorithm for the system. Specifically, a finite-time sliding mode observer is developed to handle kinematic uncertainty. A novel fast non-singular terminal sliding mode (FNTSM) manifold is constructed in the AFSC algorithm. Moreover, the control algorithm integrates an adaptive law to handle dynamic uncertainty and an adaptive term to counteract disturbances. Performance analysis demonstrates that the AFSC ensures that the coupled, synchronization, and tracking errors converge to zero within finite time. Furthermore, simulations and experiments are conducted to validate the effectiveness of the AFSC algorithm.
期刊介绍:
ISA Transactions serves as a platform for showcasing advancements in measurement and automation, catering to both industrial practitioners and applied researchers. It covers a wide array of topics within measurement, including sensors, signal processing, data analysis, and fault detection, supported by techniques such as artificial intelligence and communication systems. Automation topics encompass control strategies, modelling, system reliability, and maintenance, alongside optimization and human-machine interaction. The journal targets research and development professionals in control systems, process instrumentation, and automation from academia and industry.