Vehicle dynamic estimation with road bank angle consideration for rollover detection: theoretical and experimental studies

Dahmani, H.; Chadli, Mohammed; Rabhi, Abdelhamid

doi:10.1080/00423114.2013.839819

Cited by 41 publications

(20 citation statements)

References 16 publications

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“…Takagi-Sugeno fuzzy model [137] Fuzzy linear regression [9] Fuzzy preview control [138] Model reference adaptive fuzzy control [44] Neuro-fuzzy method [123] Hierarchical fuzzy integral [24] Fuzzy clustering [139] Type-II fuzzy system [140] Sliding mode control [141] Grey predictor [142] PI and PID control [91,143] Genetic algorithm [71,144] Optimization gradient method [145] Multi-objective optimization [108] Variable structure control [146] Reinforcement learning [94] Bayesian methods [147] in automotive engineering applications. The following positions can be especially mentioned in this context:…”

Section: Conventional Fuzzy Methods Methods / Tools Integrated With Fmentioning

confidence: 99%

A review of fuzzy methods in automotive engineering applications

Ivanov

2015

Eur. Transp. Res. Rev.

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Purpose The paper addresses evolution of fuzzy systems for core applications of automotive engineering. Methods The presented study is based on the analysis of bibliography dedicated to fuzzy sets and fuzzy control for ground vehicles. A special attention is given to fuzzy approaches used in the following domains of automotive engineering: vehicle dynamics control systems, driver and driving environment identification, ride comfort control, and energy management of electric vehicles. Results The bibliographical analysis, supplemented with statistics of relevant research publications, has allowed to allocate the most important fuzzy application cases for each domain. In particular, it concerns driver identification, humanmachine interface, recognition of road conditions, and controllers of vehicle chassis and powertrain systems. It is found out that fuzzy methods have the primary use most of all for tasks requiring identification and forecasting procedures, especially in conditions of limited informational space. Additional observation that can be also derived from the presented survey points to reasonable integration of fuzzy technique with other control engineering methods to improve the performance of automotive control systems. Conclusions In the aggregate the performed review indicates that fuzzy computing can be considered as a versatile tool for automotive engineering applications of different nature.

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Section: Conventional Fuzzy Methods Methods / Tools Integrated With Fmentioning

confidence: 99%

A review of fuzzy methods in automotive engineering applications

Ivanov

2015

Eur. Transp. Res. Rev.

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“…en, the identification problem is solved as an optimization problem, and the search technique is a critical factor in determining the performance of the optimization scheme. Many search techniques are used in identifying characteristics of the TSTmodel, for example, the trial-and-error method [11], least-square method [17], particle swarm optimization [15,16], and genetic algorithm [13,14]. In this paper, a large number of possible solutions to the parameters of the linear TST model suggest that the GA is effective and accurate [18,19].…”

Section: Introductionmentioning

confidence: 99%

Parameter Identification of Tractor‐Semitrailer Model under Steering and Braking

Shi

Qiu

2019

Mathematical Problems in Engineering

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This paper describes a valuable linear yaw-roll tractor-semitrailer (TST) model with five-degree-of-freedom (DOFs) for control algorithm development when steering and braking. The key parameters, roll stiffness, axle cornering stiffness, and fifth-wheel stiffness, are identified by the genetic algorithm (GA) and multistage genetic algorithm (MGA) based on TruckSim outputs to increase the accuracy of the model. Thus, the key parameters of the simplified model can be modified according to the real-time vehicle states by online lookup table and interpolation. The TruckSim vehicle model is built referring to the real tractor (JAC-HFC4251P1K7E33ZTF6×2) and semitrailer (Luyue LHX9406) used in the field test later. The validation of the linear yaw-roll model of a tractor-semitrailer using field test data is presented in this paper. The field test in the performance testing ground is detailed, and the test data of roll angle, roll rate, and yaw rate are compared with the outputs of the model with maps of the key parameters. The results indicate that the error of the tractor’s roll angle and semitrailer’s roll angle between model data and test data is 1.13% and 1.24%, respectively. The roll rate and yaw rate of the tractor and semitrailer are also in good agreement.

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“…Nevertheless, wheel speeds could become unrelated to the vehicle velocity when driving in slippery conditions. In order to compensate the errors caused by wheel slip, GPS signal is used to correct the data of wheel speed sensor as a fusion of data [4][5][6][7]9]. Besides the longitudinal speed, sideslip angle is also an important parameter to describe vehicle dynamics.…”

Section: Introductionmentioning

confidence: 99%

Real-time estimation of vehicle's lateral dynamics at inclined road employing extended Kalman filter

Jiang

Victorino

Charara

2016

2016 IEEE 11th Conference on Industrial Electronics and Applications (ICIEA)

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In order to enhance road safety, this article presents a robust methodology for estimation of vehicle's lateral dynamics states, including velocities, sideslip angles, accelerations and tire forces. Awareness of vehicle dynamics states is essential for most intelligent vehicle control systems. However some of these states are not available by direct sensor measurement for technical and economical reasons. Even those available sensors are always coupled with noises or errors. In order to become reliable, many intelligent systems use very expensive sensors, which are not available for ordinary passenger cars. Therefore, it is quite interesting to have an accurate estimation of all dynamics states based on low-cost sensors. The main contribution of this article is providing a reliable estimator of vehicle dynamics states in presence of sensor noises and irregular roads. A best-wheel selection approach is applied in order to have a better understanding of vehicle's driving situation and enable us to select the best models for estimating dynamics states. The proposed estimator is based on the algorithm of extended Kalman Filter, which could minimize the variance of estimation errors. The estimator is implemented in our experimental vehicles and tested at banked track. Experimental results validate and prove the feasibility of this approach.

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Vehicle dynamic estimation with road bank angle consideration for rollover detection: theoretical and experimental studies

Cited by 41 publications

References 16 publications

A review of fuzzy methods in automotive engineering applications

A review of fuzzy methods in automotive engineering applications

Parameter Identification of Tractor‐Semitrailer Model under Steering and Braking

Real-time estimation of vehicle's lateral dynamics at inclined road employing extended Kalman filter

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