A Fuzzy On-Line Self-Tuning Control Algorithm for Vehicle Adaptive Cruise Control System with the Simulation of Driver Behavior

Research of Adaptive Cruise Control (ACC) is an important issue of intelligent vehicle (IV). As we all known, a real and experienced driver can control vehicle's speed very well under every traffic environment of ACC working. So a direct and feasible way for establishing ACC controller is to build a human-like longitudinal control algorithm with the simulation of driver behavior of speed control. In this paper, a novel fuzzy self-tuning control algorithm of ACC is established and this controller's parameters can be tuned on-line based on the evaluation indexes that can describe how the driver consider the quality of dynamical characteristic of vehicle longitudinal dynamics. With the advantage of the controller's parameter on-line self-tuning, the computational workload from matching design of ACC controller is also efficiently reduced. The simulation results show that this algorithm can realize traditional ACC function such as following and cut-in and show good adaptable for the strong nonlinear of high-speed vehicle dynamics and robustness for the change of driving condition, such as different wind, load and gear ratio.