Chinese Journal of Mechanical Engineering >

2021 , Vol. 34 >Issue 5: 88 - 88

DOI: https://doi.org/10.1186/s10033-021-00597-w

Original Article

Neural-Fuzzy-Based Adaptive Sliding Mode Automatic Steering Control of Vision-based Unmanned Electric Vehicles

  • Jinghua Guo ,
  • Keqiang Li ,
  • Jingjing Fan ,
  • Yugong Luo ,
  • Jingyao Wang
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  • 1. School of Aerospace Engineering, Xiamen University, Xiamen, 361005, China;
    2. State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, 100084, China;
    3. School of Electrical and Control Engineering, North China University of Technology, Beijing, 100144, China

收稿日期: 2020-04-09

  修回日期: 2021-03-29

  网络出版日期: 2022-03-22

基金资助

Supported by National Basic Research Project of China (Grant No. 2016YFB0100900), National Natural Science Foundation of China (Grant No. 61803319), Shenzhen Municipal Science and Technology Projects of China (Grant No. JCYJ20180306172720364), Fundamental Research Funds for the Central Universities of China (Grant No. 20720190015), State Key Laboratory of Automotive Safety and Energy of China (Grant No. KF2011).

收起

Neural-Fuzzy-Based Adaptive Sliding Mode Automatic Steering Control of Vision-based Unmanned Electric Vehicles

  • Jinghua Guo ,
  • Keqiang Li ,
  • Jingjing Fan ,
  • Yugong Luo ,
  • Jingyao Wang
Expand
  • 1. School of Aerospace Engineering, Xiamen University, Xiamen, 361005, China;
    2. State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, 100084, China;
    3. School of Electrical and Control Engineering, North China University of Technology, Beijing, 100144, China

Received date: 2020-04-09

  Revised date: 2021-03-29

  Online published: 2022-03-22

Supported by

Supported by National Basic Research Project of China (Grant No. 2016YFB0100900), National Natural Science Foundation of China (Grant No. 61803319), Shenzhen Municipal Science and Technology Projects of China (Grant No. JCYJ20180306172720364), Fundamental Research Funds for the Central Universities of China (Grant No. 20720190015), State Key Laboratory of Automotive Safety and Energy of China (Grant No. KF2011).

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摘要

This paper presents a novel neural-fuzzy-based adaptive sliding mode automatic steering control strategy to improve the driving performance of vision-based unmanned electric vehicles with time-varying and uncertain parameters. Primarily, the kinematic and dynamic models which accurately express the steering behaviors of vehicles are constructed, and in which the relationship between the look-ahead time and vehicle velocity is revealed. Then, in order to overcome the external disturbances, parametric uncertainties and time-varying features of vehicles, a neural-fuzzy-based adaptive sliding mode automatic steering controller is proposed to supervise the lateral dynamic behavior of unmanned electric vehicles, which includes an equivalent control law and an adaptive variable structure control law. In this novel automatic steering control system of vehicles, a neural network system is utilized for approximating the switching control gain of variable structure control law, and a fuzzy inference system is presented to adjust the thickness of boundary layer in real-time. The stability of closed-loop neural-fuzzy-based adaptive sliding mode automatic steering control system is proven using the Lyapunov theory. Finally, the results illustrate that the presented control scheme has the excellent properties in term of error convergence and robustness.

关键词： Vision-based unmanned electric vehicles; Automatic steering; Neural-fuzzy adaptive sliding control; Vehicle lateral dynamics

本文引用格式

Jinghua Guo , Keqiang Li , Jingjing Fan , Yugong Luo , Jingyao Wang . Neural-Fuzzy-Based Adaptive Sliding Mode Automatic Steering Control of Vision-based Unmanned Electric Vehicles[J]. Chinese Journal of Mechanical Engineering, 2021 , 34(5) : 88 -88 . DOI: 10.1186/s10033-021-00597-w

Abstract

This paper presents a novel neural-fuzzy-based adaptive sliding mode automatic steering control strategy to improve the driving performance of vision-based unmanned electric vehicles with time-varying and uncertain parameters. Primarily, the kinematic and dynamic models which accurately express the steering behaviors of vehicles are constructed, and in which the relationship between the look-ahead time and vehicle velocity is revealed. Then, in order to overcome the external disturbances, parametric uncertainties and time-varying features of vehicles, a neural-fuzzy-based adaptive sliding mode automatic steering controller is proposed to supervise the lateral dynamic behavior of unmanned electric vehicles, which includes an equivalent control law and an adaptive variable structure control law. In this novel automatic steering control system of vehicles, a neural network system is utilized for approximating the switching control gain of variable structure control law, and a fuzzy inference system is presented to adjust the thickness of boundary layer in real-time. The stability of closed-loop neural-fuzzy-based adaptive sliding mode automatic steering control system is proven using the Lyapunov theory. Finally, the results illustrate that the presented control scheme has the excellent properties in term of error convergence and robustness.

Key words： Vision-based unmanned electric vehicles; Automatic steering; Neural-fuzzy adaptive sliding control; Vehicle lateral dynamics

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