Vehicle Lateral Control and Yaw Stability Control through Differential Braking

Zhao, Chenming; Xiang, Weidong; Richardson, Paul

doi:10.1109/isie.2006.295624

Cited by 31 publications

(12 citation statements)

References 10 publications

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“…The nonlinear vehicle model could have different number of degree-of-freedom (DOF) where it represents the dynamics motions and complexity of vehicle models. As utilized in [2,[12][13][14], the 7 DOF vehicle model represents the dynamic motions of vehicle body, that is, longitudinal, lateral, yaw, and four wheels. The dynamic equations for the longitudinal, lateral, and yaw motions of the vehicle body are described as follows.…”

Section: Vehicle Model For Simulationmentioning

confidence: 99%

A Review of Active Yaw Control System for Vehicle Handling and Stability Enhancement

Aripin

Sam

Danapalasingam

et al. 2014

International Journal of Vehicular Technology

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Yaw stability control system plays a significant role in vehicle lateral dynamics in order to improve the vehicle handling and stability performances. However, not many researches have been focused on the transient performances improvement of vehicle yaw rate and sideslip tracking control. This paper reviews the vital elements for control system design of an active yaw stability control system; the vehicle dynamic models, control objectives, active chassis control, and control strategies with the focus on identifying suitable criteria for improved transient performances. Each element is discussed and compared in terms of their underlying theory, strengths, weaknesses, and applicability. Based on this, we conclude that the sliding mode control with nonlinear sliding surface based on composite nonlinear feedback is a potential control strategy for improving the transient performances of yaw rate and sideslip tracking control.

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Section: Vehicle Model For Simulationmentioning

confidence: 99%

A Review of Active Yaw Control System for Vehicle Handling and Stability Enhancement

Aripin

Sam

Danapalasingam

et al. 2014

International Journal of Vehicular Technology

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“…Assuming that the model is observable and that i n ≥ , we find from (3) that (11) which implies that the row space of 2i X is contained within the row space of ( ) f f U Y ′ . But also from (2) and (10) we find that…”

Section: A System Identification Based Vehicle Modelmentioning

confidence: 68%

Robust Lateral Controller for an Unmanned Vehicle via a System Identification Method

Lee

Park

et al. 2010

JMTL

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In this paper, we developed a lateral dynamic model of an unmanned vehicle by using a system identification method and designed a lateral controller. The system input is the steering wheel angle of the vehicle with constant longitudinal speed and the output is the yaw of the vehicle. With the system identification method using subspace method, to achieve a control objective, we designed a robust lateral controller using the model equation. We, also compared the robust control performance designed by using the system identification method with that of the physically modeled system.

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“…In this study, f is entered based on fuzzy logic 59,60 and sliding surface in equation (21). The input of this fuzzy rule is sliding surface and its output is f value, and it is changed by changing the sliding surface.…”

Section: Although Inmentioning

confidence: 99%

“…In the past 10 years, the researchers have employed different control rules including sliding mode control (SMC), [3][4][5][6][7][8][9][10][11][12] HN robust control, 13,14 model predictive control (MPC), [15][16][17][18][19][20] fuzzy control, [21][22][23][24][25][26] backstepping, 27,28 adaptive control, [29][30][31] proportional-integralderivative (PID) controllers, [32][33][34][35] linear-quadratic regulator (LQR), 22,36,37 optimization algorithms 38,39 and solution of linear matrix inequalities (LMI) 40 to design controllers. Sliding mode control as a non-linear control plays an important role against different friction changes of road and different velocities in the presence of parameter uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Vehicle lateral control in the presence of uncertainty for lane change maneuver using adaptive sliding mode control with fuzzy boundary layer

Norouzi

Kazemi

Azadi

2017

Proceedings of the Institution of Mechanical Engineers, Part I:

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Recent researches on advanced driver-assistance systems indicate great advances in terms of safety and comfort in automated driving. Advanced driver-assistance systems use control systems to perform most of the maneuvers as performed by the driver in the past. One of the useful advanced driver-assistance systems is automatic lane change system in order to avoid accidents. This study designs the controller of an automatic lane change system for an autonomous vehicle. The control law in this study is adaptive sliding mode control. To avoid chattering in adaptive sliding mode control, fuzzy boundary layer is used. Also, adaptive law is used for sliding-based switching gain. This adaptive controlling law is used to avoid the calculation of upper bound of system uncertainties. In this study, based on the boundary conditions, the vehicle lane change path planning and different maneuver periods are evaluated. To simulate the designed controller, CarSim-Simulink joint simulation model is used. This linkage leads to a full non-linear vehicle model. The results of simulation show excellent tracking for dry road conditions and acceptable tracking in icy and wet roads in some maneuvers of above 4-s long.

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Vehicle Lateral Control and Yaw Stability Control through Differential Braking

Cited by 31 publications

References 10 publications

A Review of Active Yaw Control System for Vehicle Handling and Stability Enhancement

A Review of Active Yaw Control System for Vehicle Handling and Stability Enhancement

Robust Lateral Controller for an Unmanned Vehicle via a System Identification Method

Vehicle lateral control in the presence of uncertainty for lane change maneuver using adaptive sliding mode control with fuzzy boundary layer

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