A study of the cornering power by use of the analytical tyre model

Kabe, Kazuyuki; Miyashita, N.

doi:10.1080/00423110500139999

Cited by 8 publications

(4 citation statements)

References 1 publication

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“…distribution among the suspension arms while varying (i) the suspension travel and (ii) the point of application of the tire loads during the maneuvers in racetrack driving. 61 Lastly, Table 4 highlights the greater accuracy of the NNs over the LMs, as it is demonstrated by half the NRMSE values and by m values closer to unity also in all circuits different from the training one. Alternatively stated, NNs have a greater generalization capability than LMs.…”

Section: The Nn Fis Performancementioning

confidence: 89%

“…The excellent performance of the NN FIS models can be observed, in that the obtained NRMSE values (Equation 6b) are lower than 0.3%, while the slopes m are close to unity and precisely between 0.994 and 1.002. Therefore, the NN FIS is capable of correctly estimating the wheel forces taking into account the dependency of the internal force distribution among the suspension arms while varying (i) the suspension travel and (ii) the point of application of the tire loads during the maneuvers in racetrack driving 61 . Lastly, Table 4 highlights the greater accuracy of the NNs over the LMs, as it is demonstrated by half the NRMSE values and by m values closer to unity also in all circuits different from the training one.…”

Section: Validation Of the Nns For Wheel Force Estimationmentioning

confidence: 99%

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Validation of machine learning approaches for estimating wheel fatigue loads at the front suspension of a race car during track driving

Cortivo

Campagnolo

Meneghetti

2022

Fatigue Fract Eng Mat Struct

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The wheel loads of a race car have been estimated in view of structural durability assessments. First, the front left double‐wishbone suspension of a rear‐wheel‐drive race vehicle has been instrumented; then, wheel loads have been estimated by means of four approaches: (i) a geometric matrix (GM) method, (ii) a feedforward neural network (FNN) approach applied to the fully instrumented suspension (FIS), (iii) a FNN approach involving a reduced number of sensors (the partially instrumented suspension (PIS)) and an inertial measurement unit (IMU), and (iv) a linear modeling approach (LM). After having trained the FNNs by using suspension signals acquired in a racetrack as inputs and related tire forces measured with the GM method as targets, the FNN‐based methods have been validated on three different racetracks by comparing the estimated loads with those estimated by means of the GM method. According to the results achieved, the FNN approaches are effective for the estimation of the wheel forces.

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Section: The Nn Fis Performancementioning

confidence: 89%

Section: Validation Of the Nns For Wheel Force Estimationmentioning

confidence: 99%

Validation of machine learning approaches for estimating wheel fatigue loads at the front suspension of a race car during track driving

Cortivo

Campagnolo

Meneghetti

2022

Fatigue Fract Eng Mat Struct

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show abstract

“…The observed values of the position coordinates are given to the Neural Network as instruction signal, and online training is performed so as to minimize the cost function (11). By training and estimating the modeling error due to the nonlinear characteristics online, the behavior of the vehicle can be accurately represented in situations such as acceleration and deceleration, large steering and road surface changes.…”

Section: Vehicle Model To Estimate Modeling Errormentioning

confidence: 99%

“…Hence, the accuracy may be deteriorated due to a modeling error between the actual vehicle and the vehicle model. In order to consider the nonlinear characteristics of the vehicle, vehicle models that includes model equations such as tires and suspensions in the vehicle model has also been proposed [11,12]. However, since these vehicle models include multiple models expressions in the vehicle model, the structure of the vehicle model is complicated.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Identification Method of Vehicle Model for Autonomous Driving Robust to Environmental Disturbances

Yamauchi¹,

Saito²

2020

Adv. sci. technol. eng. syst. j.

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Many recent studies on autonomous driving have focused on model-based control. A number of studies has addressed that simple models such as the Kinematic Bicycle Model are easier to design controls for autonomous driving systems. However, such a simple vehicle model has a weakness in that it is subject to modeling errors. This is because it does not take into account the nonlinear characteristics due to road conditions and driving conditions (environmental disturbances: road friction coefficient, large steering, acceleration, sideslip, etc.) Therefore, the purpose of this study is to identify vehicles with high accuracy and in real time, adapting to environmental disturbances. This study propose a vehicle model based on the Kinematic Bicycle Model. The nonlinear characteristics of the vehicle are represented by the deviation of the front wheel steering angle of the Kinematic Bicycle Model. This deviation is trained and estimated online using a three-layer Neural Network. In other words, the AI is adaptive learning of modeling errors caused by nonlinear characteristics of the vehicle. This paper presents an example of model-based control using model predictive control.

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A study of the cornering force by use of the analytical tyre model

Miyashita

Kabe

2005

Vehicle System Dynamics

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A study of the cornering power by use of the analytical tyre model

Cited by 8 publications

References 1 publication

Validation of machine learning approaches for estimating wheel fatigue loads at the front suspension of a race car during track driving

Validation of machine learning approaches for estimating wheel fatigue loads at the front suspension of a race car during track driving

Adaptive Identification Method of Vehicle Model for Autonomous Driving Robust to Environmental Disturbances

A study of the cornering force by use of the analytical tyre model

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