Identification of tire forces using Dual Unscented Kalman Filter algorithm

Davoodabadi, Iraj; Ramezani, Ali Asghar; Mahmoodi-k, Mehdi; Ahmadizadeh, Pouyan

doi:10.1007/s11071-014-1566-z

Cited by 36 publications

(13 citation statements)

References 19 publications

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“…The dual unscented Kalman filter (DUKF) based on the double track model and Dugoff tire was introduced to estimate vehicle sideslip angle and the key parameter of vehicle mass at CG [64]. DUKF, together with the double-track model and Pacejka tire model, was designed in order to simultaneously estimate the side slip angle, tire-road forces, and Pacejka tire parameters; then, the hybrid of the Levenberg–Marquardt and quasi Newton method was employed to identify the Pacejka tire coefficients [65].…”

Section: Model-based Vehicle State Estimationmentioning

confidence: 99%

Advanced Estimation Techniques for Vehicle System Dynamic State: A Survey

Jin

Yin

Chen

2019

Sensors

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In order to improve handling stability performance and active safety of a ground vehicle, a large number of advanced vehicle dynamics control systems—such as the direct yaw control system and active front steering system, and in particular the advanced driver assistance systems—towards connected and automated driving vehicles have recently been developed and applied. However, the practical effects and potential performance of vehicle active safety dynamics control systems heavily depend on real-time knowledge of fundamental vehicle state information, which is difficult to measure directly in a standard car because of both technical and economic reasons. This paper presents a comprehensive technical survey of the development and recent research advances in vehicle system dynamic state estimation. Different aspects of estimation strategies and methodologies in recent literature are classified into two main categories—the model-based estimation approach and the data-driven-based estimation approach. Each category is further divided into several sub-categories from the perspectives of estimation-oriented vehicle models, estimations, sensor configurations, and involved estimation techniques. The principal features of the most popular methodologies are summarized, and the pros and cons of these methodologies are also highlighted and discussed. Finally, future research directions in this field are provided.

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Section: Model-based Vehicle State Estimationmentioning

confidence: 99%

Advanced Estimation Techniques for Vehicle System Dynamic State: A Survey

Jin

Yin

Chen

2019

Sensors

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“…are the equations of section 2 assuming a non-linear tire model such as the Magic Formula of Pacejka [23].…”

Section: Extended Kalman Filtermentioning

confidence: 99%

“…The unscented Kalman filtering (UKF) is a powerful tool for the state estimate of nonlinear systems [21][22][23]. The UKF is able to achieve good performance if the complete information of measurement noise distribution is taken as known.…”

Section: Introductionmentioning

confidence: 99%

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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

122

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(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.

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“…The KF has been well established as a powerful tool for state estimation of linear systems. [24][25][26] Developed from the KF for linear systems, the EKF [27][28][29] and UKF [30][31][32] can be used in nonlinear systems, and the latter can specially perform well in the presence of severe system nonlinearities. Another successful nonlinear filter is the PF that can maintain high estimation accuracy even with colored noises.…”

Section: Introductionmentioning

confidence: 99%

Vehicle sideslip angle estimation for a four-wheel-independent-drive electric vehicle based on a hybrid estimator and a moving polynomial Kalman smoother

Wang

2018

Proceedings of the Institution of Mechanical Engineers, Part K:

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This paper presents a vehicle sideslip angle estimation scheme against noises and outliers in sensor measurements for a four-wheel-independent-drive electric vehicle. The proposed scheme combines a robust unscented Kalman filter estimator based on the 3-DOF vehicle dynamics model and an extended Kalman filter estimator based on the kinematic model to form a hybrid estimator through a weighting factor. The weighting factor can be dynamically adjusted in real time to optimize the overall estimation performance under different driving conditions. The main contributions of this study to the related literature lie in two aspects. Firstly, a robust unscented Kalman filter estimator was incorporated to improve the robustness of dynamics-based estimation to sensor measurement outliers. Secondly, a novel moving polynomial Kalman smoother was included to filter out the noises in sensor measurements. Co-simulations of Matlab/ Simulink and Carsim software were conducted under typical vehicle maneuvers and show that the proposed vehicle sideslip angle estimation scheme can obtain satisfied estimation results, with the moving polynomial Kalman smoother exhibiting better phase characteristics and filtering performance relative to commonly-used finite impulse response filters, and the robust unscented Kalman filter estimator being robust to sensor measurement outliers.

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Identification of tire forces using Dual Unscented Kalman Filter algorithm

Cited by 36 publications

References 19 publications

Advanced Estimation Techniques for Vehicle System Dynamic State: A Survey

Advanced Estimation Techniques for Vehicle System Dynamic State: A Survey

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

Vehicle sideslip angle estimation for a four-wheel-independent-drive electric vehicle based on a hybrid estimator and a moving polynomial Kalman smoother

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