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Volume 9 Issue 9
Sep.  2022

IEEE/CAA Journal of Automatica Sinica

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Article Contents
H. Mo, Y. H. Meng, F.-Y. Wang, and D. R. Wu, “Interval type-2 fuzzy hierarchical adaptive cruise following-control for intelligent vehicles,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 9, pp. 1658–1672, Sept. 2022. doi: 10.1109/JAS.2022.105806
Citation: H. Mo, Y. H. Meng, F.-Y. Wang, and D. R. Wu, “Interval type-2 fuzzy hierarchical adaptive cruise following-control for intelligent vehicles,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 9, pp. 1658–1672, Sept. 2022. doi: 10.1109/JAS.2022.105806

Interval Type-2 Fuzzy Hierarchical Adaptive Cruise Following-Control for Intelligent Vehicles

doi: 10.1109/JAS.2022.105806
Funds:  This work was partially supported by the National Natural Science Foundation of China (61473048, 61074093, 61873321)
More Information
  • Intelligent vehicles can effectively improve traffic congestion and road traffic safety. Adaptive cruise following-control (ACFC) is a vital part of intelligent vehicles. In this paper, a new hierarchical vehicle-following control strategy is presented by synthesizing the variable time headway model, type-2 fuzzy control, feedforward + fuzzy proportion integration (PI) feedback (F+FPIF) control, and inverse longitudinal dynamics model of vehicles. Firstly, a traditional variable time headway model is improved considering the acceleration of the lead car. Secondly, an interval type-2 fuzzy logic controller (IT2 FLC) is designed for the upper structure of the ACFC system to simulate the driver’s operating habits. To reduce the nonlinear influence and improve the tracking accuracy for the desired acceleration, the control strategy of F+FPIF is given for the lower control structure. Thirdly, the lower control method proposed in this paper is compared with the fuzzy PI control and the traditional method (no lower controller for tracking desired acceleration) separately. Meanwhile, the proportion integration differentiation (PID), linear quadratic regulator (LQR), subsection function control (SFC) and type-1 fuzzy logic control (T1 FLC) are respectively compared with the IT2 FLC in control performance under different scenes. Finally, the simulation results show the effectiveness of IT2 FLC for the upper structure and F+FPIF control for the lower structure.


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    • The traditional variable time headway model is improved by considering the influence of acceleration of lead car on car-following safety distance, and used as the safety distance model of the vehicle following system. Meanwhile, its stability is proved
    • An interval type-2 fuzzy logic controller (IT2 FLC), which has no relation to the parameters of vehicle model, is designed for the upper control structure to simulate the driver's operating habits. It takes relative speed and distance difference as the input variables, the desired acceleration as the output variable
    • "Feedforward + fuzzy PI feedback" control is utilized for the lower structure to improve the tracking speed and accuracy of the desired acceleration


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