Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163809
Title: A gain-scheduled robust controller for autonomous vehicles path tracking based on LPV system with MPC and H∞
Authors: Tian, Ying
Yao, Qiangqiang
Hang, Peng
Wang, Shengyuan
Keywords: Engineering::Mechanical engineering
Issue Date: 2022
Source: Tian, Y., Yao, Q., Hang, P. & Wang, S. (2022). A gain-scheduled robust controller for autonomous vehicles path tracking based on LPV system with MPC and H∞. IEEE Transactions On Vehicular Technology, 71(9), 9350-9362. https://dx.doi.org/10.1109/TVT.2022.3176384
Journal: IEEE Transactions on Vehicular Technology
Abstract: Due to the uncertainty of vehicle model parameters, modeling errors and external disturbances, the performance of path tracking control system is poor, especially under high velocity and large curvature extreme conditions. To address this issue, this paper presents a novel gain-scheduled robust control strategy based on linear parameter varying system with model predictive control (MPC) and H∞. Firstly, fully considering the influence of the time-varying characteristics of vehicle velocity and tire cornering stiffness on the path tracking system model, a novel linear parameter varying system model is built for path tracking control of autonomous vehicle. Then, a path tracking robust controller is designed based on gain-scheduled approach, and the linear matrix inequality (LMI) is applied to solve optimization problem, in which MPC and H∞ robust control theory are applied to the controller design process. Finally, the simulation experiments have verified that the proposed novel robust control strategy can improve the path tracking accuracy and ensure the vehicle lateral and roll stability, especially under high velocity and large curvature extreme conditions. Meanwhile, the proposed robust controller shows superiority to suppress the parameters uncertainty, modeling error and external disturbance.
URI: https://hdl.handle.net/10356/163809
ISSN: 0018-9545
DOI: 10.1109/TVT.2022.3176384
Schools: School of Mechanical and Aerospace Engineering 
Rights: © 2022 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:MAE Journal Articles

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