Estimation of road vehicle yaw rate using a virtual sensor and an observer

Emirler, Mümin Tolga; Kahraman, Kerim; Güvenç, Bilin Aksun; Güvenç, Levent; Efendioglu, Baris

doi:10.23919/ecc.2009.7075122

Cited by 1 publication

(4 citation statements)

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“…Under sudden acceleration condition, slip ratio of the wheels is defined as = − , < , 0 < < 1. for use in the kinematic estimation algorithm. FR and FL in (A.20) can be obtained by utilizing (16) and (17) [11].…”

Section: Discussionmentioning

confidence: 99%

“…Basically, yaw rate is estimated for the vehicle geometry seen in Figure 2 from rear wheel angular speeds by using (1) and from the front wheel angular speeds by using (2) [10][11][12]. Consider the following:…”

Section: Kinematic Estimationmentioning

confidence: 99%

“…where is the current value of time, Δ is the calculation time interval, and the subscript of shows the th wheel of the vehicle. The derivation details of (9)- (11) are given in the appendix. Slip ratio of each wheel is computed utilizing slip ratio definitions ((3) and (4)) and longitudinal speeds of wheels ((5)-(8)).…”

Section: Kinematic Estimationmentioning

confidence: 99%

“…In the dynamic estimation part, different types of observers can be used. Observers including Kalman filters for filtering sensor noise have been used in estimation of vehicle parameters [2][3][4][5][6][8][9][10][11][12][13][14][15] before, and this approach is also used in this paper.…”

Section: Introductionmentioning

confidence: 99%

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Vehicle Yaw Rate Estimation Using a Virtual Sensor

Emirler

Kahraman

Şentürk

et al. 2013

International Journal of Vehicular Technology

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Road vehicle yaw stability control systems like electronic stability program (ESP) are important active safety systems used for maintaining lateral stability of the vehicle. Vehicle yaw rate is the key parameter that needs to be known by a yaw stability control system. In this paper, yaw rate is estimated using a virtual sensor which contains kinematic relations and a velocity-scheduled Kalman filter. Kinematic estimation is carried out using wheel speeds, dynamic tire radius, and front wheel steering angle. In addition, a velocity-scheduled Kalman filter utilizing the linearized single-track model of the road vehicle is used in the dynamic estimation part of the virtual sensor. The designed virtual sensor is successfully tested offline using a validated, high degrees of freedom, and high fidelity vehicle model and using hardware-in-the-loop simulations. Moreover, actual road testing is carried out and the estimated yaw rate from the virtual sensor is compared with the actual yaw rate obtained from the commercial yaw rate sensor to demonstrate the effectiveness of the virtual yaw rate sensor in practical use.

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Section: Discussionmentioning

confidence: 99%

Section: Kinematic Estimationmentioning

confidence: 99%

Section: Kinematic Estimationmentioning

confidence: 99%