Articles | Volume 13, issue 2
https://doi.org/10.5194/ms-13-713-2022
https://doi.org/10.5194/ms-13-713-2022
Research article
 | 
08 Aug 2022
Research article |  | 08 Aug 2022

Autonomous vehicle trajectory tracking lateral control based on the terminal sliding mode control with radial basis function neural network and fuzzy logic algorithm

Binyu Wang, Yulong Lei, Yao Fu, and Xiaohu Geng

Related subject area

Subject: Dynamics and Control | Techniques and Approaches: Numerical Modeling and Analysis
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Cited articles

Abatari, H. T. and Tafti, A. D.: Using a fuzzy PID controller for the path following of a car-like mobile robot, First Rsi/ism International Conference on Robotics & Mechatronics, IEEE, https://doi.org/10.1109/ICRoM.2013.6510103, 2013. 
Boumediene, S., Samira, C., and Hassane, A.: Fuzzy swarm trajectory tracking control of unmanned aerial vehicle, Journal of Computational Design and Engineering, 7, 435–447, https://doi.org/10.1093/jcde/qwaa036, 2020. 
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Chen, W., Tan, D., Wang, H., Wang, J., and Xia, G.: A Class of Driver Directional Control Model Based on Trajectory Prediction, Journal of Mechanical Engineering, 52, 106–115 https://doi.org/10.3901/JME.2016.14.106, 2016. 
Dugoff, H., Fancher P. S., and Segel L.: An analysis of tire action properties and their influence on vehicle dynamic performance, SAE Transcations, 79, 1219–1243 https://doi.org/10.4271/700377, 1970. 
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Short summary
A terminal sliding mode controller is used to design the controller, and a radial basis function neural network method is used for adaptive approximation of system parameters. In order to eliminate chattering, a fuzzy algorithm is designed for fuzzy control of control gain. The simulation verified that the controller designed in this paper can effectively carry out trajectory tracking and the lateral control of electric vehicles and eliminate chattering to a certain extent.