Model-free autonomous control of four-wheel steering using artificial flow guidance
Qingwei Liu, Timothy Gordon, Shammi Rahman
Abstract
Four-wheel steering (4WS) is an effective technique to improve handling performance and lateral stability of road vehicles.Conventionally, controllers utilise the driver's actions and vehicle dynamics to coordinate front and rear-axle steering.This paper proposes a novel approach for 4WS controller design, based on the concept of Artificial Flow Guidance (AFG), which relies on a spatially distributed motion reference through a two-dimensional vector field.This field provides high-level guidance while lower-level steering controllers to control axle centres motions relative to the flow.These flow vectors, computed in real-time via simple geometric construction, can be pre-computed globally to evaluate the guidance algorithm's efficacy.When controlling only the front axle, this same approach can function as an autonomous driving system.Relying solely on a spatial reference field and control targets' velocities enables the controller to work in a simple and robust fashion, without using a reference vehicle dynamics model or lengthy parameter tuning.The proposed approach's effectiveness is validated through co-simulation with MATLAB/Simulink and the CarMaker simulation platform.AFG control performance is found to be at least comparable to that of more complex 4WS controllers using methods such as MPC; in the cases considered, AFG provides superior path-tracking performance.