{"title":"Fixed-time path following control for automated ground vehicle subject to prescribed performance and lateral tire force constraint","authors":"Zhongnan Wang, Zhongchao Liang","doi":"10.1016/j.isatra.2025.05.017","DOIUrl":null,"url":null,"abstract":"<div><div>Large initial errors in prescribed performance control (PPC) methods are prone to generating the excessive inputs. In the context of path-following control for Automated Ground Vehicles (AGVs), such excessive inputs result in large steering angles, which can induce significant tire sideslip angles. Under these conditions, the tires may enter the nonlinear working region, generating uncontrolled lateral tire forces and potentially compromising vehicle stability. To address this issue, this paper proposes a path-following control protocol for AGVs that integrates the prescribed performance constraints with lateral tire force limitations. Specifically, the protocol constrains the lateral force of the front tires by saturating their sideslip angles, ensuring they remain within linear and safe operational thresholds to enhance vehicle stability. Furthermore, unknown parameters of tire dynamics, such as the front tires’ cornering stiffness and the norm of the unknown weights in the Radial Basis Function Neural Network (RBFNN) for the rear tires, are estimated using adaptive laws. These enhancements enable the proposed protocol to achieve path-following control objectives while mitigating vehicle instabilities caused by excessive inputs. Finally, the effectiveness of the proposed controller is validated through Hardware-in-the-Loop (HiL) tests, in which enhanced path-following performance and improved vehicle stability are demonstrated.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"163 ","pages":"Pages 280-291"},"PeriodicalIF":6.5000,"publicationDate":"2025-05-17","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/S0019057825002514","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
Large initial errors in prescribed performance control (PPC) methods are prone to generating the excessive inputs. In the context of path-following control for Automated Ground Vehicles (AGVs), such excessive inputs result in large steering angles, which can induce significant tire sideslip angles. Under these conditions, the tires may enter the nonlinear working region, generating uncontrolled lateral tire forces and potentially compromising vehicle stability. To address this issue, this paper proposes a path-following control protocol for AGVs that integrates the prescribed performance constraints with lateral tire force limitations. Specifically, the protocol constrains the lateral force of the front tires by saturating their sideslip angles, ensuring they remain within linear and safe operational thresholds to enhance vehicle stability. Furthermore, unknown parameters of tire dynamics, such as the front tires’ cornering stiffness and the norm of the unknown weights in the Radial Basis Function Neural Network (RBFNN) for the rear tires, are estimated using adaptive laws. These enhancements enable the proposed protocol to achieve path-following control objectives while mitigating vehicle instabilities caused by excessive inputs. Finally, the effectiveness of the proposed controller is validated through Hardware-in-the-Loop (HiL) tests, in which enhanced path-following performance and improved vehicle stability are demonstrated.
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.