Knowledge-Based Controller Optimised with Particle Swarm Optimisation for Adaptive Path Tracking Control of an Autonomous Heavy Vehicle

Noor Hafizah Amer,Zulkiffli Abd Kadir,Khisbullah Hudha,Amar Faiz Zainal Abidin,Hairi Zamzuri,Muhamad Murrad,Vimal Rau Aparow

doi:10.5772/intechopen.92667

Abstract

This chapter discusses the development of an adaptive path tracking controller equipped with a knowledge-based supervisory algorithm for an autonomous heavy vehicle. The controller was developed based on a geometric/kinematic controller, the Stanley controller. One of the mostly known issues with any geometric/kinematic controller is that a properly tuned controller may not be valid in a different operating region than the one it was being tuned/optimised on. Therefore, this study proposes an adaptive algorithm to automatically choose an optimal controller parameter depending on the manoeuvring and vehicle conditions. An optimal knowledge database is developed for an adaptive algorithm to automatically obtain the parameter values based on the vehicle speed, v, and heading error, ϕ. Several simulations are carried out with different trajectories and speeds to evaluate the effectiveness of the controller against its predecessors, namely, Stanley and the non-adaptive modified Stanley (Mod St) controllers. The simulated steering actions are then compared against human driver’s experimental data along the predefined paths. It was shown that the proposed adaptive algorithm managed to guide the heavy vehicle successfully and adapt to various trajectories with different vehicle speeds while recording lateral error improvement of up to 82% compared to the original Stanley controller.

Highlights

This study proposed a new adaptive steering control strategy for trajectory tracking controller of a heavy vehicle
This controller considers the yaw rate error feedback, which can improve the overall trajectory tracking performance. It has more controller parameters, which made the controller more sensitive to tuning, which, in turn, improve the tracking performance upon parameter selection. This explains the exceptional performance by the modified Stanley (Mod St) controller shown in the graphs
The controller was developed based on an established Stanley controller that was modified to increase its sensitivity to the parameter changes

Summary

Introduction

This study proposed a new adaptive steering control strategy for trajectory tracking controller of a heavy vehicle. The controller aims to automatically steer the vehicle along the desired trajectory and adapt to various speeds and trajectories. A path tracking controller is a controller module that is developed to provide electronic actuation to the vehicle system while navigating the vehicle automatically. An effective controller needs to be developed to ensure a functional steering module while autonomously navigating through various paths. One of the most common types of controllers is kinematic controllers such as Pure Pursuit and Follow-the-Carrot due to the simplicity and stability it can provide. This type of controllers relies on the kinematic properties of the vehicles such as speed and acceleration, as well as the travelled distance for the controller feedback. Compared to other dynamic controllers that require the kinetic properties of the vehicle such as torques, moments, and forces, geometric and kinematic properties are relatively easier to measure

Objectives

Results

Conclusion