@article{3e492757035d4fec95f06206cb5fb00c,
title = "Observer-based coordinated control for blended braking system with actuator delay",
abstract = "The coordinated control of a blended braking system is always a difficult task. In particu-lar, blended braking control becomes more challenging when the braking actuator has an input time-delay and some states of the braking system cannot be measured. In order to improve the tracking performance, a coordinated control system was designed based on the input time-delay and state observation for a blended braking system comprising a motor braking system and friction braking system. The coordinated control consists of three parts: Sliding mode control, a multi-input single-output observer, and time-delay estimation-based Smith Predictor control. The sliding mode control is used to calculate the total command braking torque according to the desired braking performance and vehicle states. The multi-input single-output observer is used to simultaneously esti-mate the input time-delay and output braking torque of the friction braking system. With time-delay estimation-based Smith Predictor control, the friction braking system is able to effectively track the command braking torque of the friction braking system. The tracking of command braking torque is realized through the coordinated control of the motor braking system and friction braking system. In order to validate the effectiveness of the proposed approach, numerical simulations on a quarter-vehicle braking model were performed.",
keywords = "Friction braking torque observer, Sliding mode control, Smith Predictor, Time-delay observer",
author = "Wenfei Li and Huiyun Li and Chao Huang and Kun Xu and Tianfu Sun and Haiping Du",
note = "Funding Information: This research was funded by the National Natural Science Foundation of China, grant no. 62073311; the Key Program of Natural Science Foundation of Shenzhen, grant nos. JCYJ20200109115403807 and JCYJ20200109115414354; Science and Technology Development Fund, Macao S.A.R. (FDCT), no. 0015/2019/AKP; Shenzhen Institute of Artificial Intelligence and Robotics for Society and Guang-Dong Basic and Applied Basic Research Foundation (no. 2020B515130004). Funding Information: Acknowledgments: This work was supported by a grant from the National Natural Science Foun‐ dation of China (grant no. 62073311), the Key Program of Natural Science Foundation of Shenzhen (grant nos. JCYJ20200109115403807 and JCYJ20200109115414354), and CAS Key Laboratory of Hu‐ man‐Machine Intelligence‐Synergy Systems, Shenzhen Institutes of Advanced Technology, Shen‐ zhen Engineering Laboratory for Autonomous Driving Technology, the Science and Technology Development Fund, Macao S.A.R. (FDCT) nos. 0015/2019/AKP and 2019B121205007, Shenzhen In‐ stitute of Artificial Intelligence and Robotics for Society, and Guang‐Dong Basic and Applied Basic Research Foundation (no. 2020B515130004). The authors would like to thank the National Natural Science Foundation of China, the Key Program of Natural Science Foundation of Shenzhen, and Science and Technology Development Fund, Macao S.A.R. (FDCT), for their support. Funding Information: Funding: This research was funded by the National Natural Science Foundation of China, grant no. 62073311; the Key Program of Natural Science Foundation of Shenzhen, grant nos. JCYJ20200109115403807 and JCYJ20200109115414354; Science and Technology Development Fund, Macao S.A.R. (FDCT), no. 0015/2019/AKP; Shenzhen Institute of Artificial Intelligence and Robotics for Society and Guang‐Dong Basic and Applied Basic Research Foundation (no. 2020B515130004). Publisher Copyright: {\textcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland.",
year = "2021",
month = aug,
day = "11",
doi = "10.3390/act10080193",
language = "English",
volume = "10",
journal = "Actuators",
issn = "2076-0825",
publisher = "MDPI AG",
number = "8",
}