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Journal ArticleDOI

Modelling and Control Strategies in Path Tracking Control for Autonomous Ground Vehicles: A Review of State of the Art and Challenges

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TLDR
Critical review of the basic vehicle model usually used; the control strategies usually employed in path tracking control, and the performance criteria used to evaluate the controller’s performance are provided.
Abstract
Autonomous vehicle field of study has seen considerable researches within three decades. In the last decade particularly, interests in this field has undergone tremendous improvement. One of the main aspects in autonomous vehicle is the path tracking control, focusing on the vehicle control in lateral and longitudinal direction in order to follow a specified path or trajectory. In this paper, path tracking control is reviewed in terms of the basic vehicle model usually used; the control strategies usually employed in path tracking control, and the performance criteria used to evaluate the controller's performance. Vehicle model is categorised into several types depending on its linearity and the type of behaviour it simulates, while path tracking control is categorised depending on its approach. This paper provides critical review of each of these aspects in terms of its usage and disadvantages/advantages. Each aspect is summarised for better overall understanding. Based on the critical reviews, main challenges in the field of path tracking control is identified and future research direction is proposed. Several promising advancement is proposed with the main prospect is focused on adaptive geometric controller developed on a nonlinear vehicle model and tested with hardware-in-the-loop (HIL). It is hoped that this review can be treated as preliminary insight into the choice of controllers in path tracking control development for an autonomous ground vehicle.

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Citations
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Research Advances and Challenges of Autonomous and Connected Ground Vehicles

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Towards connected autonomous driving: review of use-cases

TL;DR: Although connectivity can enhance the performance of autonomous vehicles and contribute to the improvement of current transportation system performance, the level of achievable benefits depends on factors such as the penetration rate of connected vehicles, traffic scenarios and the way of augmenting off-board information into vehicle control systems.
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Path Following Control of Autonomous Ground Vehicle Based on Nonsingular Terminal Sliding Mode and Active Disturbance Rejection Control

TL;DR: A robust AGV path following control strategy that is based on nonsingular terminal sliding mode (NTSM) and active disturbance rejection control (ADRC) and the nonlinear error feedback control law is designed by combining the NTSM and exponential approximation law.
References
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Journal ArticleDOI

Improving the trajectory tracking performance of autonomous orchard vehicles using wheel slip compensation

TL;DR: In this paper, the effects of wheel slip estimation and compensation of trajectory tracking for orchard applications were investigated, and a slippage estimator was developed and adapted into a car-like robot model.
Journal ArticleDOI

A Path Tracking Algorithm Using Future Prediction Control with Spike Detection for an Autonomous Vehicle Robot

TL;DR: This paper aims to tackle the lateral error problem by applying the steering control law to the vehicle, and proposes a new path tracking control method by considering the future coordinate of the vehicle and the future estimated lateral error.

Controller Development and Implementation for Path Planning and Following in an Autonomous Urban Vehicle

TL;DR: This system, which is implemented on the ACFR’s (ute) test vehicle, allows the user to enter a trajectory and follow it, while avoiding any detected obstacles along the path.
Journal ArticleDOI

CF-Pursuit: A Pursuit Method with a Clothoid Fitting and a Fuzzy Controller for Autonomous Vehicles

TL;DR: In CF-Pursuit, in order to reduce fitting errors, a clothoid C1 curve is used to replace the circle employed in Pure-P Pursuit but with certain differences, which helps the Pursuit method to decrease tracking errors.
Journal ArticleDOI

Trajectory tracking with obstacle avoidance of redundant manipulator based on fuzzy inference systems

TL;DR: F fuzzy adaptive control is used to drive with obstacle avoidance an industrial redundant manipulator under the hypothesis of uncertain dynamics and operates effectively with weak tracking control errors and bounded actuator torques evolving in admissible range.
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