LPV Unknown Input Observer-Based Estimation of Driver Intervention Torque and Vehicle Dynamics for Human-Machine Shared Driving

作     者：Anh-Tu Nguyen Thierry-Marie Guerra Jiyun Lu Juntao Pan Hamid Taghavifar 

作者机构：Laboratory LAMIH UMR CNRS 8201 Université Polytechnique Hauts-de-France Valenciennes France School of Electrical and Information Engineering North Minzu University Yinchuan China Concordia University Montreal Canada 

出 版 物：《IFAC-PapersOnLine》 

年 卷 期：2023年第56卷第2期

页      面：5679-5684页

主　　题：Vehicle dynamics sideslip angle estimation torque estimation unknown input observer driver steering intervention linear parameter-varying model 

摘      要：This paper presents an unknown input observer (UIO)-based method to jointly estimate the vehicle dynamics and the driver torque within the framework of human-machine shared driving. To deal with the time-varying vehicle longitudinal speed, the vehicle dynamics is represented as a linear parameter-varying (LPV) model. Based on an unknown input (UI) decoupling technique, an LPV observer is designed, which can guarantee an asymptotic estimation performance of both the vehicle dynamics and the driver torque. Via Lyapunov stability theory, we propose sufficient conditions, expressed in terms of linear matrix inequalities, for LPV unknown input observer design. High-fidelity Simulink-CarSim co-simulations are carried out to show the effectiveness of the proposed LPV UIO-based estimation method for driver-automation shared driving dynamics. Moreover, a comparative study is performed with a recent LPV method to highlight the practical interest of the new estimation solution.