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

Intelligent vehicles can effivtively improve traffic congestion and mad 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 fit y control, feedforward + fuzzy proportion integration (P1) feed back (F+FPIF) control, and inverse longitudinal dynamics model of vehices. Firstly, a traditional variable time headway model is improved considering the acceleration of the lead ear. 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.

Automated summary

This paper proposes a new vehicle-following control strategy, which effectively addresses traffic congestion and improves road traffic safety by integrating different control models and methods.