Designing a fuzzy logic controller to adjust the angle of tires in four wheel steering vehicles

Hakima, Ali; Ameli, Sina

doi:10.1109/icarcv.2010.5707765

Cited by 9 publications

(8 citation statements)

References 12 publications

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“…To facilitate implementation and simplicity, a linear 4WS vehicle model based on 2-DOF is used to design a trajectory tracking controller. 19,20 Regardless of rolling, the state-space equation of the vehicle mode can be written as follows…”

Section: Linear 4ws Vehicle Modelmentioning

confidence: 99%

Emergency obstacle avoidance trajectory tracking control based on active disturbance rejection for autonomous vehicles

Liu

Wei

Sang

2020

International Journal of Advanced Robotic Systems

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To solve the problem of understeer and oversteer for autonomous vehicle under high-speed emergency obstacle avoidance conditions, considering the effect of steering angular frequency and vehicle speed on yaw rate for four-wheel steering vehicles in the frequency domain, a feed-forward controller for four-wheel steering autonomous vehicles that tracks the desired yaw rate is proposed. Furthermore, the steering sensitivity coefficient of the vehicle is compensated linearly with the change in the steering angular frequency and vehicle speed. In addition, to minimize the tracking errors caused by vehicle nonlinearity and external disturbances, an active disturbance rejection control feedback controller that tracks the desired lateral displacement and desired yaw angle is designed. Finally, CarSim® obstacle avoidance simulation results show that an autonomous vehicle with the four-wheel steering path tracking controller consisting of feed-forward control and feedback control could not only improve the tire lateral forces but also reduce tail flicking (oversteer) and pushing ahead (understeer) under high-speed emergency obstacle avoidance conditions.

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Section: Linear 4ws Vehicle Modelmentioning

confidence: 99%

Emergency obstacle avoidance trajectory tracking control based on active disturbance rejection for autonomous vehicles

Liu

Wei

Sang

2020

International Journal of Advanced Robotic Systems

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“…The scheme shown in the Fig. 4 represents the control architecture used in this work [11]. For the deployment control system the inputs are desired orientation rate ( d ) and the desired slip angle (β).…”

Section: Fig 4 Control System Architecturementioning

confidence: 99%

Fuzzy Control Strategy Applied to Adjustment of Front Steering Angle of a 4WSD Agricultural Mobile Robot

2012

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Abstract. This paper presents the preliminary studies of the control strategy based in fuzzy logic, projected for the steering system of AGRIBOT project that consist of a wheeled autonomous mobile robotic in real scale endowed with four independent steering and driven wheels (4WSD). In this work we present a preliminary fuzzy controller design applied to front steering angle, using a multivariable plant which incorporates simplified linear model of lateral dynamics of a vehicle whose input are linear combination of rear and front steering angles. The fuzzy control strategy was decided because provides flexible way to deploy with embedded systems. Simulations are used to illustrate the designed controller performance. We use Ackerman geometry to trace front steering angle that allows the vehicle to perform correctly a given maneuver preserving a minimum level of stability and maneuverability. The goal is to establish a relationship between steering input commands and the control commands to the actuators so that it is possible to adjust the attitude of the actuators over the movement axis, as the trajectory change.Keywords: Ackerman geometry, fuzzy controller, Two-Track Vehicle, Lateral dynamics, mobile robot, bike model, steering angle.Resumen. Este trabajo presenta los resultados preliminares de la estrategia de control basada en lógica difusa, proyectada para el sistema de dirección del proyecto Agribot, que consiste en un robot móvil autónomo con ruedas en escala real dotado de dirección a las cuatro ruedas motrices independientes (4WSD). Se presenta un diseño preliminar de un controlador difuso aplicado a ángulo de dirección frontal, utilizando una planta multivariable que incorpora el modelo lineal simplificado de la dinámica lateral de un vehículo, cuya entrada es la combinación lineal de los ángulos de dirección trasera como delantera. Se eligió como estrategia el control difuso debido a que proporciona una forma flexible para desplegar los sistemas embebidos. Las simulaciones se usan para ilustrar el rendimiento del controlador diseñado. Se acudió a la geometría Ackerman para trazar el ángulo de dirección delantera que permite que el vehículo funcione correctamente en una maniobra dada la preservación de un nivel mínimo de estabilidad y maniobrabilidad. El objetivo es establecer una relación entre la entrada de órdenes de dirección y los comandos de control a los actuadores de modo que es posible ajustar la actitud de los actuadores sobre el eje de movimiento, como el cambio de trayectoria.Palabras clave: Geometría Ackerman; Controlador difuso; Vehículos de pista; Dinámica lateral; Robot móvil; Modelo de bicicleta.

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“…For four-wheel independent-drive electric vehicles (4WIDEVs), Yin et al [6] focus on global yaw moment generation and a torque distribution management system, considering tire consumption and yaw moment effectiveness for improved stability and handling. Fuzzy control theory is used in [7,8] to establish controllers determining the rear wheel steering angle based on the influence of the front wheel steering angle and speed, effectively reducing side-slip angle, yaw rate, and roll angle peaks. Furthermore, Tian et al [9] investigate skid steering and differential steering of 4WIDEVs, particularly in cases of steering failure.…”

Section: Introductionmentioning

confidence: 99%

A Novel Real-Time Robust Controller of a Four-Wheel Independent Steering System for EV Using Neural Networks and Fuzzy Logic

Kosmidis,

Ioannidis,

Vokas

et al. 2023

Mathematics

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In this study a four-wheel independent steering (4WIS) system for an electric vehicle (EV) steered by stepper motors is presented as a revolutionary real-time control technique employing neural networks in combination with fuzzy logic, where the use of the neural network greatly simplifies the computational process of fuzzy logic. The control of the four wheels is based on a variation of a Hopfield Neural Network (VHNN) method, in which the input is the error of each steering motor and the output is processed by a hyperbolic tangent function (HTF) feeding the fuzzy logic controller (FLC), which ultimately drives the stepper motor. The whole system consists of the four aforementioned blocks which work in sync and are inseparable from each other with the common goal of driving all the steering stepper motors at the same time. The novelty of this system is that each wheel monitors the condition of the others, so even in the case of the failure of one wheel, the vehicle does not veer off course. The results of the simulation show that the suggested control system is very resilient and workable at all angles and speeds.

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Designing a fuzzy logic controller to adjust the angle of tires in four wheel steering vehicles

Cited by 9 publications

References 12 publications

Emergency obstacle avoidance trajectory tracking control based on active disturbance rejection for autonomous vehicles

Emergency obstacle avoidance trajectory tracking control based on active disturbance rejection for autonomous vehicles

Fuzzy Control Strategy Applied to Adjustment of Front Steering Angle of a 4WSD Agricultural Mobile Robot

A Novel Real-Time Robust Controller of a Four-Wheel Independent Steering System for EV Using Neural Networks and Fuzzy Logic

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