Simulation and experiment of automatic steering control for lane keeping manoeuvre

Abstract

This paper presents a 9-Degree of Freedom (9-DOF) vehicle model combined with a closed-loop driver model for the purpose of developing automatic steering control. The driver model was developed to control the steering angle and uses a lookup table path as a reference for the control input. The proposed driver model for the outer-loop controllers are combination of Proportional-Integral (PI) control with a yaw effect adaptive fuzzy logic control. A stepper motor model, rack and pinion model, and kinematic model of steering system are also introduced as an inner-loop subsystem for Stepper Motor Actuated Steering (SMAS) system. The outer-loop and inner-loop controllers were evaluated by simulation using predefined lane keeping path at 80 km/h constant speed. The Software-in-the-Loop Simulation (SILS) results were validated using an instrumented automatic steering test rig through the Hardware-in-the-Loop Simulation (HILS). The SILS and HILS results show that the proposed automatic steering control is capable of reducing lateral path error and improving lateral path manoeuvre significantly.

Lane Departure Warning Estimation Using Yaw Acceleration

“…The vehicle dynamics modelling of a passenger vehicle as used in [14] is considered in this paper that consists of a single sprung mass connected to four unsprung masses and is represented as a nine degree-of-freedom system. The sprung mass is allowed to pitch, roll, and yaw, as well as to displace in lateral and longitudinal directions [14]. Each tire is also permitted to rotate along its axle axis, and only the two front tires are free to steer.…”

Hybrid controller approach for an autonomous ground vehicle path tracking problem

“…A dynamical model based controller called optimal preview control is also proposed by Snider with favorable results[11].Some fuzzy logic based control strategies are also proposed in the literature. A control strategy is proposed by P. Ping et al such that the cross-track error is compensated by a PI controller and the yaw angle error is compensated with a fuzzy logic controller[16]. In another work a full state feedback controller method is supported by a fuzzy logic based gain scheduler to compensate parameter changes in the model, and implementation on a real vehicle is considered[17].Model Predictive Control (MPC) methods is one of the most popular methods in the literature.…”