Fixed-time path following control for automated ground vehicle subject to prescribed performance and lateral tire force constraint - Wang, Zhongnan; Liang, Zhongchao (ISA transactions, 2025)
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. •A prescribed performance control (PPC) scheme is developed to ensure that path-following performance remains within predefined boundaries throughout the entire process. This allows the tracking error to follow a preplanned evolution, which is significant for autonomous driving tasks.•The proposed strategy limits front tire sideslip angles to the linear region, preventing nonlinear tire behavior. This enhances vehicle stability and robustness, especially under excessive control inputs in PPC, contributing to safer handling in challenging driving conditions.•The control protocol stabilizes preview error within fixed time, independent of initial vehicle states. It also estimates front tire cornering stiffness without prior knowledge, maintaining robust tracking performance under model uncertainty and variable tire conditions.