Integrated Controller Design for Path Following in Autonomous Vehicles

Mashadi, Behrooz; Ahmadizadeh, Pouyan; Majidi, Majid

doi:10.4271/2011-01-1032

Cited by 18 publications

(10 citation statements)

References 9 publications

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“…A linear quadratic regulator (LQR) is used as the controller design methodology, where the controller gain

K_{LQR}

is calculated by optimally solving some chosen cost function [28]. The LQR controller has been used in prior studies for steering controllers [29, 30]. Snider Jarrod [31] performed an in‐depth analysis on the standard LQR controller and found that while a well‐tuned controller could provide good path following there was no single controller which provided good control over changing vehicle speeds.…”

Section: Lqr Controllermentioning

confidence: 99%

See 1 more Smart Citation

Influence of wireless communication transport latencies and dropped packages on vehicle stability with an offsite steering controller

Bennett

Kapp

Botha

et al. 2020

IET Intelligent Transport Systems

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“…A linear quadratic regulator (LQR) is used as the controller design methodology, where the controller gain

K_{LQR}

Section: Lqr Controllermentioning

confidence: 99%

“…In such cases, a plant model can be used to estimate the actual plant's states. In [30] an observer is used to estimate states that cant be measured. In this study, the plant is estimated using the ARX‐model, resulting in an LQR controller that makes use of an adaptive plant model that changes as the vehicle travels.…”

Section: Lqr Controllermentioning

confidence: 99%

Influence of wireless communication transport latencies and dropped packages on vehicle stability with an offsite steering controller

Bennett

Kapp

Botha

et al. 2020

IET Intelligent Transport Systems

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“…Since the path-tracking problem of 4WS vehicles is more complex than that of FWS vehicles, the control strategies of path tracking for 4WS vehicles are still relatively limited. 14 Mashadi et al 15 applied the linear quadratic regulator (LQR) technique to 4WS vehicles for path-tracking controller design and drew the conclusion that 4WS vehicles show potential for path-tracking control of an AGV. Although the LQR controller designed above has good path-tracking capability, it is only suitable for the nominal model without parametric perturbations and external disturbances.…”

Section: Introductionmentioning

confidence: 99%

Integrated chassis control algorithm design for path tracking based on four-wheel steering and direct yaw-moment control

Hang

Chen

2018

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article focuses on an integrated chassis control algorithm design for path tracking utilizing four-wheel steering and direct yaw-moment control. The designed integrated chassis control algorithm mainly consists of three parts: (1) taking the parametric uncertainties, external disturbances, measurement noise and unmodeled dynamics into consideration, a robust controller is designed for path tracking utilizing μ synthesis approach; (2) the control allocation algorithm is proposed to distribute the output torque requirements to each in-wheel motor based on the weighted least square; and (3) considering that vehicle lateral velocity is a critical state variable for path-tracking control, while it is not easy to measure with low-cost sensors, a state observer is designed for lateral velocity estimation using unscented Kalman filter. To verify the performance of the designed integrated chassis control algorithm, three simulation maneuvers including the single-lane change, curve and the double-lane change are carried out in MATLAB/Simulink with a high-fidelity, full-vehicle model built in CarSim. The proposed integrated chassis control algorithm is compared with other three control algorithms, that is, active front steering, four-wheel steering and active front steering + direct yaw-moment control, and simulation results indicate that the integrated chassis control algorithm has superior path-tracking performance and handling stability. The robust performance of the integrated chassis control algorithm is also verified under the variation of vehicle velocity and different road conditions.

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“…In this paper, we limit autonomous vehicles to ground vehicles, which are increasingly being studied by several researchers from academia, industry, and military. Several control methods are being used, including fuzzy logic [9], hybrid control [10], H-infinity control [11], and linear quadratic regulators [12]. The best comparative studies on model predictive control strategies for autonomous guidance vehicles can be found in Park et al [13], Yoon et al [14], and Falcone et al [15], where a nonlinear dynamics model of a vehicle is used for the controller design of an active front steering manoeuvre in a double-lane change scenario.…”

Section: Introductionmentioning

confidence: 99%

Study of Model Predictive Control for Path-Following Autonomous Ground Vehicle Control under Crosswind Effect

Yakub

Abu

Sarip

et al. 2016

Journal of Control Science and Engineering

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We present a comparative study of model predictive control approaches of two-wheel steering, four-wheel steering, and a combination of two-wheel steering with direct yaw moment control manoeuvres for path-following control in autonomous car vehicle dynamics systems. Single-track mode, based on a linearized vehicle and tire model, is used. Based on a given trajectory, we drove the vehicle at low and high forward speeds and on low and high road friction surfaces for a double-lane change scenario in order to follow the desired trajectory as close as possible while rejecting the effects of wind gusts. We compared the controller based on both simple and complex bicycle models without and with the roll vehicle dynamics for different types of model predictive control manoeuvres. The simulation result showed that the model predictive control gave a better performance in terms of robustness for both forward speeds and road surface variation in autonomous path-following control. It also demonstrated that model predictive control is useful to maintain vehicle stability along the desired path and has an ability to eliminate the crosswind effect.

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Integrated Controller Design for Path Following in Autonomous Vehicles

Cited by 18 publications

References 9 publications

Influence of wireless communication transport latencies and dropped packages on vehicle stability with an offsite steering controller

Influence of wireless communication transport latencies and dropped packages on vehicle stability with an offsite steering controller

Integrated chassis control algorithm design for path tracking based on four-wheel steering and direct yaw-moment control

Study of Model Predictive Control for Path-Following Autonomous Ground Vehicle Control under Crosswind Effect

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