Driverless Bus Path Tracking Based on Fuzzy Pure Pursuit Control with a Front Axle Reference

Lingli Yu,Zongxu Kuang,Baifan Chen,Xiaoxin Yan,Yuqian Zhao

doi:10.3390/app10010230

Abstract

Currently, since the model of a driverless bus is not clear, it is difficult for most traditional path tracking methods to achieve a trade-off between accuracy and stability, especially in the case of driverless buses. In terms of solving this problem, a path-tracking controller based on a Fuzzy Pure Pursuit Control with a Front Axle Reference (FPPC-FAR) is proposed in this paper. Firstly, the reference point of Pure Pursuit is moved from the rear axle to the front axle. It relieves the influence caused by the ignorance of the bus’s lateral dynamic characteristics and improves the stability of Pure Pursuit. Secondly, a fuzzy parameter self-tuning method is applied to improve the accuracy and robustness of the path-tracking controller. Thirdly, a feedback-feedforward control algorithm is devised for velocity control, which enhances the velocity tracking efficiency. The proportional-integral (PI) controller is indicated for feedback control, and the gravity acceleration component in the car’s forward direction is used in feedforward control. Finally, a series of experiments is conducted to illustrate the excellent performances of proposed methods.

Highlights

With the development of new energy and the upgrading of buses, the technology of the driverless bus has become a greater research prospect [1]
In lateral control, a path-tracking controller based on FPPC-FAR is indicated
The reference point of Pure Pursuit (PP) is moved from the rear axle to the front axle

Summary

Introduction

With the development of new energy and the upgrading of buses, the technology of the driverless bus has become a greater research prospect [1]. Some model-free methods, such as proportional-integral-derivative (PID) [7,8,9], fuzzy logic control (FLC) [10,11], and sliding model control (SMC) [12,13,14], are applied Those algorithms perform well even when the model of the driverless bus is vague. Yu proposes an adaptive rolling optimization controller with preview window to track the desired path [15]. It is built on PID, and does not require a high-precision bus model. The robustness of the adaptive rolling optimization controller is poor, too

Methods

Results

Conclusion