Observer-Based Robust Control of Vehicle Dynamics for Rollover Mitigation in Critical Situations

Dahmani, H.; Pagès, O.; Hajjaji, Ahmed El; Daraoui, Nawal

doi:10.1109/tits.2013.2281135

Cited by 50 publications

(39 citation statements)

References 22 publications

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“…Most of the vehicle dynamic linear models used consider the sprung mass of the vehicle as the total mass and ignore the unsprung mass. However, this assumption can introduce an imprecision in the roll estimation, since the unsprung mass is approximately 10% of the vehicle mass [18]. In (1) β, ψ, and φ v are the sideslip, the yaw, and the roll angle of the vehicle, respectively.…”

Section: A Nonlinear Model Analysismentioning

confidence: 99%

Observer-Based State Feedback Control for Vehicle Chassis Stability in Critical Situations

Dahmani

Pagès

2015

IEEE Trans. Contr. Syst. Technol.

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This brief describes an observer-based state feedback tracking controller for vehicle dynamics with a four-wheel active steering system as well as an active suspension system. The objective of the proposed controller is to improve the vehicle behavior by forcing the lateral dynamics and the load transfer ratio to achieve the desired vehicle behavior in critical situations. A Takagi-Sugeno (TS) representation of the lateral forces has been used in order to take the nonlinearities into account. Based on the obtained fuzzy model, a TS observer has been designed with estimated membership functions in order to consider the unavailability of the sideslip and the roll angles for measurement. Based on the Lyapunov function and the H ∞ approach, the observer and controller design has been formulated in terms of Linear Matrix Inequality constraints. The proposed techniques have been evaluated through a fishhook test conducted in the CarSim professional software package.

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Section: A Nonlinear Model Analysismentioning

confidence: 99%

Observer-Based State Feedback Control for Vehicle Chassis Stability in Critical Situations

Dahmani

Pagès

2015

IEEE Trans. Contr. Syst. Technol.

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“…From the previous equation, it is easily seen that the RI can be estimated from a simple measure (accelerometer); position parameters can be considered initially constants with their nominal values. In the following, a robust controller is designed, and a stability analysis is performed; that is, the RI for delta tricycles can be experimentally estimated by (24) and the following analysis is performed to demonstrate that a differential braking controller can mitigate the rollover risk in delta tricycles.…”

Section: Rollover Detectionmentioning

confidence: 99%

“…Although a rollover index (RI) is found in the regulations for 4 and more wheels vehicles ( [20]), it is presented as a definition but not as a way to estimate it, much less to mitigate it through an active security system. Many authors have used this definition to generate tractable mathematical models for estimating the RI to mitigate the risk of rollover in 4-wheel vehicles even using multiple actuators (see, for instance, [13,[21][22][23][24][25]). However, the rollover dynamics of a 3-wheel vehicle in delta configuration (a front wheel and two rear wheels) differs considerably from that in a 4-wheel vehicle and its modelling is not a trivial task; at the moment, there is not in the literature a mathematical modelling that allows the dynamical estimation of the rollover risk in tricycles.…”

Section: Introductionmentioning

confidence: 99%

The Rollover Risk in Delta Tricycles: A New Rollover Index and Its Robust Mitigation by Rear Differential Braking

Licea

Rodríguez

Pérez-Pinal

et al. 2018

Mathematical Problems in Engineering

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Although there are efforts to electrify and diversify small vehicles, active safety on motorcycles and tricycles (also known as auto rickshaw, tuk-tuk, mototaxi, etc.) has been relegated until a few years ago. For instance, the electric tricycles (even the combustion ones) marketed today do not have an active safety system that prevents or mitigates the risk of rollover, despite how prone they are to such a situation. The concern for the increase in its marketing is latent and, unfortunately, there are very few related studies. In this article, we present the obtaining and validation of a new rollover index for tricycles demonstrating its effectiveness in predicting and detecting the risk, even statically, by means of a simple quantity. In addition, a controller for the mitigation of the risk of rollover is presented which, by means of a Lyapunov type analysis, it is shown to be robust to changes in parameters, such as the center of gravity height, using a polytopic representation of the system and a differential braking strategy on the rear wheels. Numerical simulations, including video simulation captures, of the operation of the rollover mitigation system using widely recognized commercial software, are also presented. This work can be extended to vehicles with a suspension system or for trikes without autocamber.

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“…Direct measurements of the vehicle roll-angle and roll-over index in experimental tests show the effectiveness of the proposed model predictive controller and the accuracy of the roll angle estimator. Dahmani et al [10] used a robust controller to control roll stabilization in limit conditions.…”

Section: Introductionmentioning

confidence: 99%

A Reliability Approach to Development of Rollover Prediction for Heavy Vehicles Based on SVM Empirical Model With Multiple Observed Variables

Zhu

Yin

et al. 2020

IEEE Access

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The rapid development of cooperative vehicle-infrastructure system (CVIS) improves the communication reliability between vehicles and road environment. These communications enable the accurate vehicle rollover prediction in Human-Vehicle-Road interaction. However, considering the strong nonlinear characteristics of Human-Vehicle-Road interaction and the uncertainty of modeling, the traditional deterministic method cannot meet the requirement of accurate prediction of rollover hazard for heavy vehicles. In order to improve the accuracy of vehicles rollover prediction, this paper proposes a developed rollover prediction algorithm based on the multiple observed variables by combining the failure probability in reliability and the empirical model. This approach applies the probability method of uncertainty to the design of dynamic rollover prediction algorithm for heavy vehicles and establishes a classification model of heavy vehicles based on support vector machine (SVM) with multiple observed variables. The failure probability of rollover limit state of heavy vehicles is calculated by Monte Carlo Sampling (MCS), Radial-Based Importance Sampling (RBIS), and Truncated Importance Sampling (TIS), respectively. Then the Fishhook, Double Lane Change tests, and J-turn tests, simulated in TruckSim, are carried out to validate the proposed algorithm. The simulation results show that the rollover prediction algorithm based on failure probability can effectively improve the rollover prediction accuracy for heavy vehicles. Moreover, based on the communication in CVIS, the failure probability can be obtained before entering the specific road. Meanwhile, this approach can reduce the external interference of strong non-linear characteristics of Human-Vehicle-Road interaction and the uncertainty of the modeling to the system, thus improving the prediction accuracy of active safety performance of heavy vehicles significantly. INDEX TERMS Failure probability, heavy vehicles, load transfer ratio, rollover risk prediction, SVM classification model. BIN LI received the Ph.D. degree from Shanghai Jiao Tong University, China, in 2010. He was Research Fellow on electric vehicle project with the University of Waterloo and on mobile robotic control project with McGill University. He is currently a Researcher working on stability and safety control of commercial vehicle and passenger car with Concordia University. His research interests include vehicle system modeling, dynamics and control, vehicle control system design and optimization, electrified vehicle, integrated vehicle motion control, and autonomous vehicle control. WEI MA was born in Xingtai, Hebei, China, in 1991. He received the B.S. degree in vehicle service engineering from the Hebei University of Engineering, Handan, China, in 2018, where he is currently pursuing the M.S. degree in equipment intelligence and safety engineering. His research interest includes vehicle dynamic control.

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Observer-Based Robust Control of Vehicle Dynamics for Rollover Mitigation in Critical Situations

Cited by 50 publications

References 22 publications

Observer-Based State Feedback Control for Vehicle Chassis Stability in Critical Situations

Observer-Based State Feedback Control for Vehicle Chassis Stability in Critical Situations

The Rollover Risk in Delta Tricycles: A New Rollover Index and Its Robust Mitigation by Rear Differential Braking

A Reliability Approach to Development of Rollover Prediction for Heavy Vehicles Based on SVM Empirical Model With Multiple Observed Variables

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