Citation: Hossain, T.; Habibullah, H.;
Islam, R. Steering and Speed Control
System Design for Autonomous
Vehicles by Developing an Optimal
Hybrid Controller to Track Reference
Trajectory. Machines 2022, 10, 420.
https://doi.org/10.3390/
machines10060420
Academic Editors: Luis Payá, Oscar
Reinoso García and Helder Jesus
Araújo
Received: 7 April 2022
Accepted: 13 May 2022
Published: 26 May 2022
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Article
Steering and Speed Control System Design for Autonomous
Vehicles by Developing an Optimal Hybrid Controller to Track
Reference Trajectory
Tagor Hossain * , Habib Habibullah and Rafiqul Islam
Faculty of STEM, University of South Australia , Mawson Lakes, SA 5095, Australia;
habibullah.habibullah@unisa.edu.au (H.H.); md.islam@unisa.edu.au (R.I.)
* Correspondence: md_tagor.hossain@mymail.unisa.edu.au
Abstract:
In this paper, a longitudinal and lateral control system of an autonomous vehicle is
presented by developing a novel hybrid trajectory tracking algorithm. In this proposed method, the
longitudinal control system is developed based on the curvature information of the reference path.
The autonomous vehicle modifies the desired speed according to the estimated size and types of the
reference trajectory curves. This desired speed is integrated into the PID controller to maintain an
optimal speed of the vehicle while following the given path. The lateral control system is designed
based on feedforward (preview control) and feedback (LQR) controllers to reduce lateral errors
between the trajectory and autonomous vehicle. The feedforward and the feedback controllers
generate precise steering angles to eliminate orientation and lateral errors caused by the curvature
of the trajectory and external disturbances. The effectiveness of the proposed method is evaluated
by comparing simulation and experimental results with different trajectory tracking algorithms on
simulated and experimented paths. It is proven that the proposed algorithm is capable of significantly
minimizing lateral errors on sharp curves compared to other path tracking methods.
Keywords:
sharp curves calculation; curve speed estimation; longitudinal and lateral control;
path tracking
1. Introduction
Autonomous vehicles are one of the most popular research topics currently. Au-
tonomous driving offers comfortable, efficient, and safe transportation. In recent years,
technologies have dramatically advanced, therefore, autonomous vehicle’s computational
power has increased, whereas sensing and computing time are reduced [
1
]. Autonomous
vehicles have to understand and have cooperation with some major functions, such as
localization and mapping, perception, trajectory generation, and control for automated
driving [
2
]. Reference trajectory tracking is one of the fundamental issues of autonomous
vehicle control and navigation systems. The main objective of the trajectory tracking sys-
tem is to generate control commands to follow the predefined path by considering the
autonomous vehicle’s relevant motion constraints. Various trajectory tracking methods
exist in the literature. Three types of widely used path tracking methods are geometric
methods (pure pursuit, Stanley controller), optimal control based methods (LQR, optimal
preview control), and model based methods (PID, MPC, sliding mode control, fuzzy logic
controller). This section describes a review of some path tracking algorithms which are
already being tested for trajectory tracking of autonomous vehicles.
Several geometric and kinematic path tracking controllers are used for autonomous
vehicle path tracking purposes; among them, the pure pursuit controller [
3
] is probably the
most simple and widely used path tracking controller [4].
Machines 2022, 10, 420. https://doi.org/10.3390/machines10060420 https://www.mdpi.com/journal/machines
