Integrated vehicle dynamics control via coordination of active front steering and rear braking

Doumiati, Moustapha; Sename, Olivier; Dugard, Luc; Molina, John Jairo Martinez; Gáspár, Péter; Szabó, Zoltán

doi:10.1016/j.ejcon.2013.03.004

Cited by 190 publications

(119 citation statements)

References 43 publications

(71 reference statements)

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“…Model parameters are given in Table 1. 4 Equation (12) is a part of vehicle model without considering uncertainties. By adding uncertainties, the equation is changed as follows…”

Section: Vehicle Dynamicmentioning

confidence: 99%

Predictor-based model reference adaptive control for a vehicle lateral dynamics considering uncertainties

Khosravi

Lachini

Sarhadi

2015

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article deals with the design and analysis of state predictor-based model reference adaptive control for a vehicle lateral dynamics. The goal of this article is to achieve the desired tracking in the presence of uncertainty in order to guarantee the stability as well as improving the performance of the vehicle. Through Lyapunov stability analysis, the adaptive laws of stable predictor-based state feedback have been derived. In order to evaluate the advantages of proposed controller, first the performance of non-adaptive linear quadratic regulator controller in nominal conditions has been presented. By adding uncertainty to the vehicle dynamics, the performance of controller has been reduced. Then, by augmenting model reference adaptive control to linear quadratic regulator, the performance has been improved. Finally, a considerable improvement has been achieved in transient response and control signal using predictor-based model reference adaptive control. Simulation results show that by adding predictor-based model reference adaptive control to the system, uncertain part of the vehicle dynamics is approximated, and the tracking structure of integrated control (linear quadratic regulator + predictor-based model reference adaptive control) has been successful. KeywordsPredictor-based model reference adaptive control, vehicle lateral dynamic, uncertainty, linear quadratic regulator controller, tracking error Date

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“…Model parameters are given in Table 1. 4 Equation (12) is a part of vehicle model without considering uncertainties. By adding uncertainties, the equation is changed as follows…”

Section: Vehicle Dynamicmentioning

confidence: 99%

Predictor-based model reference adaptive control for a vehicle lateral dynamics considering uncertainties

Khosravi

Lachini

Sarhadi

2015

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…Coupled nonlinear flat control Here, these estimators are used to design the flat control (11) and the feedback loop (12). The following formulae may be used to estimate the 1 st order derivative of y:…”

Section: Design Of the Vehicle Dynamics Control Strategiesmentioning

confidence: 99%

“…In the latter case the control structure may include either steering actuators (mainly front and rear) [5]- [8] olivier.sename@gipsa-lab.grenoble-inp.fr actuators (eventually at the four wheels) [9], [10]. Few recent works have been concerned with the use of both steering and braking actions as explained in [11]. Let us mention, among others, [12] where control allocation is considered for the optimal distribution of the control actions, or [13] where an a posteriori braking/steering control distribution is devised.…”

Section: Introductionmentioning

confidence: 99%

Study and comparison of non linear and LPV control approaches for vehicle stability control

Fergani

Menhour

Sename

et al. 2013

21st Mediterranean Conference on Control and Automation

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This paper proposes a study and a comparison between two efficient and relatively recent vehicle control dynamics strategies, namely, the non linear Flatness control strategy and the LPV/H∞ control strategy. The first one concerns a controller based on the differential algebraic flatness of non linear systems and an algebraic non linear estimation applied to commercial vehicles. The second one is a LPV/H∞ (Linear Varying Parameter with the H∞ norm ) control using a stability monitoring system to achieve the vehicle dynamics control objective. These two strategies use Active Steering and Electro-Mechanical Braking actuators and aim at improving the vehicle stability and steerability by designing a multivariable controller that acts simultaneously on the lateral and longitudinal dynamics of the car. Simulations are performed on a complex nonlinear full vehicle model, the same driving scenario is applied for the two control strategies. The model parameters are those of a Renault Mégane Coupé, obtained by identification with real data. Promising simulations results are obtained. Comparison between the two proposed strategies and the uncontrolled vehicle show the reliability and the robustness of the proposed solutions, even if one is developed within the linear control framework while the other one is a non linear control approach.

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“…For example, in (Chung and Yi, 2006) motor vehicle stability control based on the side slip angle was proposed, as well as the the evaluation of certain control schemes on a virtual test track. In (Doumiati et al, 2013) the investigation on the coordination of active front steering and rear braking for a driver-assist system in yaw control of a motor vehicle was proposed. In (Güvenç et al, 2009) asymmetrical load in a vehicle, combined with µ-split braking forces caused by side wind or unilateral tire pressure loss was examined.…”

Section: Introductionmentioning

confidence: 99%

Selected examples of referring the examined stochastic technical stability to the ISO standards

Kisilowski

Zalewski

2018

jtam

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In this paper, certain examples of comparison between the selected definition of the stochastic technical stability (for motor vehicle models) and the ISO definitions (developed for real vehicles) have been discussed. Being able to refer the examined stability of a mathematical model to the real object may result in verifying the properties of the latter on the basis of model tests, instead of the field trials. Such an attempt has been presented previously, but in a more general approach. The aim of this paper is to present a certain attempt to compare the results obtained for a simulated vehicle model in virtual environment with the specific definitions of stability dedicated to the real motor vehicles. The background of this considerations liesin the examination of the stochastic technical stability, which allows any mechanical system undergo external random disturbances, such as road irregularities.

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Integrated vehicle dynamics control via coordination of active front steering and rear braking

Cited by 190 publications

References 43 publications

Predictor-based model reference adaptive control for a vehicle lateral dynamics considering uncertainties

Predictor-based model reference adaptive control for a vehicle lateral dynamics considering uncertainties

Study and comparison of non linear and LPV control approaches for vehicle stability control

Selected examples of referring the examined stochastic technical stability to the ISO standards

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