Abstract
Vehicle yaw rate is a key parameter required for various active stability control systems. Accurate yaw rate information may be obtained from the fusion of some on-vehicle sensors and GPS data. In this study, the closed-form expression of the yaw rate–written as a function of front wheel rolling speeds and steering angle–was derived via kinematic analysis of a planar four-wheel vehicle on the assumption of no longitudinal slip at the both front tires. The obtained analytical solution was primarily verified by computational simulation. In terms of implementation, the 1:10th scaled rear-wheel-drive vehicle was modified so that the front wheel rolling speeds and the steering angle could be measured. An inertial measurement unit was also installed to provide the directly measured yaw rate used for validation. Preliminary experiment was done on some extremely random sideslip maneuvers beneath the global positioning using four recording cameras. Comparing with the vision-based and the gyro-based references, the vehicle yaw rate could be well approximated at any slip condition without requiring integration or vehicle and tire models. The proposed cost-effective estimation strategy using only on-vehicle sensors could be used as an alternative way to enhance performance of the GPS-based yaw rate estimation system while the GPS signal is unavailable.
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Abbreviations
- ABS:
-
anti-lock braking system
- GPS:
-
global positioning system
- IMU:
-
inertial measurement unit
- MEMs:
-
micro-electro-mechanical system
- RWD:
-
rear wheel drive
- CG:
-
center of gravity
- ICZV:
-
instantaneous center zero velocity
- CW:
-
clockwise direction
- CCW:
-
counterclockwise direction
References
Carlson, C. R., Gerdes, J. C. and Powell, D. (2002). Practical position and yaw rate estimation with GPS and differential wheelspeeds. Proc. 6th Int. Symp. Advanced Vehicle Control.
Chaichaowarat, R. and Wannasuphoprasit, W. (2013a). Dynamics and simulation of RWD vehicle drifting at steady state using BNP-MNC tire model. SAE Int. J. Transportation Safety 1, 1, 134–144.
Chaichaowarat, R. and Wannasuphoprasit, W. (2013b). Optimal control for steady state drifting of RWD vehicle. Proc. 7th IFAC Symp. Advances in Automotive Control, 814–820.
Chee, W. (2005). Yaw rate estimation using two 1-axis accelerometers. Proc. American Control Conf., 423–428.
Cherouat, H., Braci, M. and Diop, S. (2005). Vehicle velocity, side slip angles and yaw rate estimation. Proc. IEEE Int. Symp. Industrial Electronics, Dubrovnik, Croatia.
Emirler, M. T., Kahraman, K., Senturk, M., Guvenc, B. A., Guvenc, L. and Efendioglu, B. (2013). Vehicle yaw rate estimation using a virtual sensor. Int. J. Vehicular Technology, 2013, 1–13.
Ghoneim, Y. A. and Chin, Y. K. (2001). Active Brake Control Having Yaw Rate Estimation. US Patent 6 169 951 B1.
Gustafsson, F., Ahlqvist, S., Forssell, U. and Persson, N. (2001). Sensor fusion for accurate computation of yaw rate and absolute velocity. SAE Paper No. 2001-01- 1064.
Gwak, M., Jo, K. and Sunwoo, M. (2013). Neural-network multiple models filter (NMM)-based position estimation system for autonomous vehicles. Int. J. Automotive Technology 14, 2, 265–274.
Hac, A. and Simpson, M. D. (2000). Estimation of vehicle side slip angle and yaw rate. SAE Paper No. 2000-01- 0696.
Novara, C., Ruiz, F. and Milanese, M. (2011). Direct identification of optimal SM-LPV filters and application to vehicle yaw rate estimation. IEEE Trans. Control Systems Technol. 19, 1, 5–17.
Rogers, R. M. (1997). Land vehicle navigation filtering for GPS/dead-reckoning system. Proc. Institute of Navigation National Technology Meeting, 703–708.
Rogers, R. M. (1999). Improved heading using dual speed sensors for angular rate and odometry in land navigation. Proc. Institute of Navigation National Technology Meeting, 353–361.
Shimada, K., Nakamura, Y., Horikoshi, S., Sugawara, H. and Monji, T. (1993). Angular Rate Detection Apparatus of Moving Body. US-Patent 5247466.
Shivashankar, S. N. and Ulsoy, A. G. (1998). Yaw rate estimation for vehicle control applications. ASME J. Dynamic Systems, Measurement and Control 120, 2, 267–274.
Tchamna, R. and Youn, I. (2013). Yaw rate and side-slip control considering vehicle longitudinal dynamics. Int. J. Automotive Technology 14, 1, 53–60.
Venhovens, P. J. T. and Naab, K. (1999). Vehicle dynamics estimation using Kalman filters. Int. J. Vehicle Mechanics and Mobility 32, 2, 171–184.
Zaremba, A. and Stuntz, R. M. (1994). Cost Effective Yaw Rate Sensing. Technical Memorandum No. SRM-94-16, Ford Research Laboratory, Dearborn, MI, USA.
Zenhai, G. (2003). Soft sensor application yaw rate measurement based on Kalman filter and vehicle dynamics. Proc. IEEE Intelligent Transportation Systems.
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Chaichaowarat, R., Wannasuphoprasit, W. Full-slip kinematics based estimation of vehicle yaw rate from differential wheel speeds. Int.J Automot. Technol. 17, 83–90 (2016). https://doi.org/10.1007/s12239-016-0007-z
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DOI: https://doi.org/10.1007/s12239-016-0007-z