On the vehicle sideslip angle estimation through neural networks: Numerical and experimental results

Melzi, Stefano; Sabbioni, Edoardo

doi:10.1016/j.ymssp.2010.10.015

Cited by 106 publications

(59 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Two tire models are considered to prove the effectiveness of the proposed observer: the lineal tire model and the nonlinear tire model such as the Magic Formula of Pacejka [29].…”

Section: Vehicle Dynamic Modelmentioning

confidence: 99%

See 1 more Smart Citation

Vehicle sideslip angle measurement based on sensor data fusion using an integrated ANFIS and an Unscented Kalman Filter algorithm

Boada

Díaz

2016

Mechanical Systems and Signal Processing

123

View full text Add to dashboard Cite

(2016). Vehicle sideslip angle measurement based on sensor data fusion using an integrated ANFIS and an Unscented Kalman Filter algorithm. Mechanical Systems and Signal Processing,[72][73] ABSTRACTMost existing ESC (Electronic Stability Control) systems rely on the measurement of both yaw rate and sideslip angle. However, one of the main issues is that the sideslip angle cannot be measured directly because the sensors are too expensive. For this reason, sideslip angle estimation has been widely discussed in the relevant literature. The modeling of sideslip angle is complex due to the non-linear dynamics of the vehicle. In this paper, we propose a novel observer based on ANFIS, combined with Kalman Filters in order to estimate the sideslip angle, which in turn is used to control the vehicle dynamics and improve its behavior. For this reason, low-cost sensor measurements which are integrated into the actual vehicle and executed in real time have to be used. The ANFIS system estimates a "pseudo-sideslip angle" through parameters which are easily measured, using sensors equipped in actual vehicles (inertial sensors and steering wheel sensors); this value is introduced in UKF in order to filter noise and to minimize the variance of the estimation mean square error. The estimator has been validated by comparing the observed proposal with the values provided by the CARSIM model, which is a piece of experimentally validated software. The advantage of this estimation is the modeling of the non-linear dynamics of the vehicle, by means of signals which are directly measured from vehicle sensors. The results show the effectiveness of the proposed ANFIS+UKF-based sideslip angle estimator.

show abstract

“…Two tire models are considered to prove the effectiveness of the proposed observer: the lineal tire model and the nonlinear tire model such as the Magic Formula of Pacejka [29].…”

Section: Vehicle Dynamic Modelmentioning

confidence: 99%

“…AI-based algorithms have been proved to be appropriated in order to avoid issues associated with the identification and adaptation of reference model parameters. In [28][29][30], AI-based algorithms are used to estimate the sideslip angle based on fuzzy, Neural Network (NN) and ANFIS (Adaptive Neuro-Fuzzy Inference System), respectively.…”

Section: Introductionmentioning

confidence: 99%

Vehicle sideslip angle measurement based on sensor data fusion using an integrated ANFIS and an Unscented Kalman Filter algorithm

Boada

Díaz

2016

Mechanical Systems and Signal Processing

123

View full text Add to dashboard Cite

show abstract

“…These data should contain all of the required representative features. In this case, different maneuvers are selected in order to characterize the linear and non-linear vehicle behavior [24].…”

Section: Training Datamentioning

confidence: 99%

“…Some authors use algorithms based on artificial intelligence to estimate the sideslip angle such as Fuzzy [23] and Neural Networks [24] to avoid issues associated with the identification and adaptation of reference model parameters. Artificial intelligence has also been used in the field of vehicles obtaining satisfactory results [25,26,27].…”

Section: Introductionmentioning

confidence: 99%

Sideslip angle estimator based on ANFIS for vehicle handling and stability

Boada

Gauchía

et al. 2015

J Mech Sci Technol

View full text Add to dashboard Cite

show abstract

“…Current estimation algorithm mainly include linear kalman filter (KF) [3], extended kalman filtering (EKF) [4][5] and Unscented kalman filter (UKF) [6][7], neural network [8][9], the state observer [10] and fuzzy logic [11] and so on. These methods are to estimate and forecast the key control variables of vehicle control system.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Axles Disturbing Forces Estimation Based on Ant Colony Optimization Unscented Kalman Filter Algorithm

Wang¹,

Bei²,

Zhang³

et al. 2017

IJCA

View full text Add to dashboard Cite

show abstract

On the vehicle sideslip angle estimation through neural networks: Numerical and experimental results

Cited by 106 publications

References 12 publications

Vehicle sideslip angle measurement based on sensor data fusion using an integrated ANFIS and an Unscented Kalman Filter algorithm

Vehicle sideslip angle measurement based on sensor data fusion using an integrated ANFIS and an Unscented Kalman Filter algorithm

Sideslip angle estimator based on ANFIS for vehicle handling and stability

Vehicle Axles Disturbing Forces Estimation Based on Ant Colony Optimization Unscented Kalman Filter Algorithm

Contact Info

Product

Resources

About