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research-article

Adaptive Unified Monitoring System Design for Tire-Road Information

[+] Author and Article Information
Shuo Cheng

State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China
chengs16@mails.tsinghua.edu.cn

Ming-ming Mei

State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China
mmm16@mails.tsinghua.edu.cn

Xu Ran

State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China
ranxu@tsinghua.edu.cn

Liang Li

State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China
liangl@tsinghua.edu.cn

Lin Zhao

State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China
linzhao@cqu.edu.cn

1Corresponding author.

ASME doi:10.1115/1.4043113 History: Received April 10, 2018; Revised October 15, 2018

Abstract

Knowledge of the tire-road information is not only very crucial in many active safety applications but also of great importance for self-driving cars. The tire-road information mainly consists of tire-road friction coefficient and road-tire friction forces. However, Precise measurement of tire-road friction coefficient and tire forces is difficult to implement without expensive equipment. Therefore, the monitoring of tire-road information utilizing either accurate models or improved estimation algorithms is essential. Considering easy availability and good economy, this paper proposes a novel adaptive unified monitoring system (AUMS) to simultaneously observe the tire-road friction coefficient and tire forces, i. e. vertical, longitudinal, and lateral tire forces. Firstly, the vertical tire forces can be calculated considering vehicle body roll and load transfer. The longitudinal, and lateral tire forces are estimated by an adaptive unified sliding mode observer (AUSMO). Then, the road-tire friction coefficient is observed through the designed mode-switch observer (MSO). The designed MSO contains two modes: when the vehicle is under driving or brake, a slip slope method (SSM) is used, and a recursive least-squares (RLS) identification method is utilized in the SSM; when the vehicle is under steering, a comprehensive friction estimation method is adopted. The performance of the proposed AUMS is verified by both the MATLAB/ Simulink CarSim co-simulation and the real car experiment. The results demonstrate the effectiveness of the proposed AUMS to provide accurate monitoring of tire-road information.

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