Automatic emergency braking/anti-lock braking system coordinated control with road adhesion coefficient estimation for heavy vehicle

被引:17
作者
Shaohua, Li [1 ]
Guiyang, Wang [2 ]
Hesen, Wang [2 ]
Lipeng, Zhang [3 ]
机构
[1] Shijiazhuang Tiedao Univ, State Key Lab Mech Behav & Syst Safety Traff Engn, Shijiazhuang, Hebei, Peoples R China
[2] Shijiazhuang Tiedao Univ, Sch Mech Engn, 17 East North Second Ring Rd, Shijiazhuang 050043, Hebei, Peoples R China
[3] Yanshan Univ, Hebei Key Lab Special Delivery Equipment, Qinhuangdao, Hebei, Peoples R China
基金
美国国家科学基金会;
关键词
SLIDING-MODE CONTROL; WHEEL SLIP; PREDICTIVE CONTROL; CONTROL STRATEGY; DESIGN;
D O I
10.1049/itr2.12229
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The road adhesion coefficient is a key factor influencing automatic emergency braking (AEB) and anti-lock braking system (ABS) safety control of trucks. With the fading factor introduced, and the covariance gain adjusted in real time, the strong tracking unscented Kalman filter (STUKF) algorithm is modified to estimate the road adhesion coefficient more accurately. Composed of an ABS fuzzy sliding mode controller (SMC) and an AEB controller, an AEB/ABS coordinated control strategy with an adhesion coefficient estimation is designed for a three-axle heavy vehicle. The control effects are verified through experiments on various road conditions based on a hardware-in-loop test platform. The test results show that the proposed control strategy has a better braking efficiency than the traditional AEB/ABS and AEB control strategy without adhesion coefficient estimation and can decrease braking distance by 8.4% and braking time by 5.9%, which is beneficial to the vehicle longitudinal safety.
引用
收藏
页码:1521 / 1534
页数:14
相关论文
共 38 条
[21]   Self-learning fuzzy sliding-mode control for antilock braking systems [J].
Lin, CM ;
Hsu, CF .
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2003, 11 (02) :273-278
[22]   Unknown Input Observer Based Approach for Distributed Tube-Based Model Predictive Control of Heterogeneous Vehicle Platoons [J].
Luo, Qianyue ;
Nguyen, Anh-Tu ;
Fleming, James ;
Zhang, Hui .
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2021, 70 (04) :2930-2944
[23]   Robust predictive control of wheel slip in antilock braking systems based on radial basis function neural network [J].
Mirzaeinejad, Hossein .
APPLIED SOFT COMPUTING, 2018, 70 :318-329
[24]   Intelligent braking system for stability enhancement of vehicle braking, using fuzzy logic controllers [J].
Naderi, P. (p.naderi@srttu.edu), 1600, Inderscience Enterprises Ltd., 29, route de Pre-Bois, Case Postale 856, CH-1215 Geneva 15, CH-1215, Switzerland (06) :381-398
[25]   Coordination of Lateral Vehicle Control Systems Using Learning-Based Strategies [J].
Nemeth, Balazs .
ENERGIES, 2021, 14 (05)
[26]   Design of Pedestrian Target Selection With Funnel Map for Pedestrian AEB System [J].
Park, Min-Ki ;
Lee, Sang-Yeob ;
Kwon, Chan-Keun ;
Kim, Soo-Won .
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2017, 66 (05) :3597-3609
[27]  
Peng Jishen, 2017, Chinese Journal of Sensors and Actuators, V30, P1029, DOI 10.3969/j.issn.1004-1699.2017.07.010
[28]   Survey on Wheel Slip Control Design Strategies, Evaluation and Application to Antilock Braking Systems [J].
Pretagostini, Francesco ;
Ferranti, Laura ;
Berardo, Giovanni ;
Ivanov, Valentin ;
Shyrokau, Barys .
IEEE ACCESS, 2020, 8 :10951-10970
[29]   Adaptive Estimation of Vehicle Velocity From Updated Dynamic Model for Control of Anti-Lock Braking System [J].
Rafatnia, Sadra ;
Mirzaei, Mehdi .
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 23 (06) :5871-5880
[30]   Design and hardware-in-loop implementation of collision avoidance algorithms for heavy commercial road vehicles [J].
Rajaram, Vignesh ;
Subramanian, Shankar C. .
VEHICLE SYSTEM DYNAMICS, 2016, 54 (07) :871-901