A New Parameter Estimation Methodology Using Steady State Yaw Rate Measurements for Lateral Vehicle Dynamics

Zhihong Man; Mingcong Deng; Zenghui Wang; Qing-long Han

doi:10.1109/JAS.2025.125366

IEEE/CAA Journal of Automatica Sinica

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Z. Man, M. Deng, Z. Wang, and Q.-L. Han, “A new parameter estimation methodology using steady state yaw rate measurements for lateral vehicle dynamics,” IEEE/CAA J. Autom. Sinica, 2025. doi: 10.1109/JAS.2025.125366

Citation:

Z. Man, M. Deng, Z. Wang, and Q.-L. Han, “A new parameter estimation methodology using steady state yaw rate measurements for lateral vehicle dynamics,” IEEE/CAA J. Autom. Sinica, 2025. doi: 10.1109/JAS.2025.125366

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A New Parameter Estimation Methodology Using Steady State Yaw Rate Measurements for Lateral Vehicle Dynamics

doi: 10.1109/JAS.2025.125366

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Author Bio:
Zhihong Man (Member, IEEE) received his B.E. degree from Shanghai Jiaotong University, China, in 1982, M.Sc. degree from Chinese Academy of Sciences in 1987 and PhD degree from the University of Melbourne, Australia, in 1994. From 1994 to 1996, Zhihong was the Lecturer in the Department of Computer and Communication Engineering at Edith Cowan University, Australia. From 1996 to 2001, he was the Lecturer and then Senior Lecturer in the School of Engineering at The University of Tasmania, Australia. From 2002 to 2007, Zhihong worked at Nanyang Technological University, Singapore, as the Associate Professor of Computer Engineering. From 2007 to 2008, he was with Monash University Sunway Campus, served as the Professor and Head of Electrical and Computer Systems Engineering as well as the Chair of the Monash Sunway Campus Research Committee. Zhihong is currently the Professor of Robotics and Mechatronics in the School of Engineering at Swinburne University of Technology, Australia. Zhihong’s research interests are in nonlinear control, robotics, neural networks-based pattern classifications & learning systems, engineering optimization and vehicle dynamics & control. He has published more than 300 research papers in both referred international journals and international conferences. Since 1994, Zhihong has been involved in many international conferences in control, robotics, signal processing, neural networks and industrial electronics as the General Chair, Program Chair, Track Chair, Session Chair and the International Advisory Committees

Mingcong Deng (Fellow, IEEE) is with the Department of Electrical and Electronic Engineering at Tokyo University of Agriculture and Technology, serving as Professor and Chair. His research interests are in learning & operator based robust nonlinear control and applications. Prof. Deng has made significant contributions to learning & operator based nonlinear control technology, systems development and knowledge. His research results have been expounded in one monograph by IEEE Press Series on Systems Science and Engineering, as well as in more than 200 publications in prestigious journals. Prof. Deng is a Fellow of The Engineering Academy of Japan, a Fellow of IEEE, the Chair of Environmental Sensing, Networking, and Decision Making Technical Committee of IEEE SMCS and the Co-Chair of Agricultural Robotics and Automation Technical Committee of IEEE RAS. Prof. Deng has organized 13 times International Conference on Advanced Mechatronic Systems as the General Chair, technically sponsored by IEEE SMCS. In addition, Prof. Deng received the 2014 & 2019 Meritorious Services Awards from IEEE SMCS and 2020 IEEE RAS Most Active Technical Committee Award from IEEE RAS

Zenghui Wang (Member, IEEE) earned his Doctoral degree in Control Theory and Control Engineering from Nankai University, China, in 2007. After completing his doctorate, he worked as an Associate Professor in the Department of Automation at Shandong University of Science and Technology and as a Postdoctoral Fellow at the French South African Technical Institute in Electronics at Tshwane University of Technology, South Africa. In 2010, he joined the University of South Africa as an Associate Professor in the Department of Electrical and Smart Systems Engineering (formerly the Department of Electrical and Mining Engineering). Shortly thereafter, in July 2012, he was promoted to Professor in the same department. He is currently a Professor at the University of South Africa. His research interests include control theory and control engineering, engineering optimization, Industry 4.0, microgrids, and artificial intelligence

Qing-Long Han (Fellow, IEEE) received the B.Sc. degree in Mathematics from Shandong Normal University, Jinan, China, in 1983, and the M.Sc. and Ph.D. degrees in Control Engineering from East China University of Science and Technology, Shanghai, China, in 1992 and 1997, respectively. Professor Han is Pro Vice-Chancellor (Research Quality) and a Distinguished Professor at Swinburne University of Technology, Melbourne, Australia. He held various academic and management positions at Griffith University and Central Queensland University, Australia. His research interests include networked control systems, multi-agent systems, time-delay systems, smart grids, unmanned surface vehicles, and neural networks. Professor Han was awarded the 2024 IEEE Dr.-Ing. Eugene Mittelmann Achievement Award (the Highest Award in industrial electronics), the 2021 Norbert Wiener Award (the Highest Award in systems science and engineering, and cybernetics) and the 2021 M. A. Sargent Medal (the Highest Award of the Electrical College Board of Engineers Australia). He was the recipient of the IEEE Systems, Man, and Cybernetics Society Andrew P. Sage Best Transactions Paper Award in 2019, 2020, and 2022, respectively, the IEEE/CAA Journal of Automatica Sinica Norbert Wiener Review Award in 2020, and the IEEE Transactions on Industrial Informatics Outstanding Paper Award in 2020. Professor Han is a Member of the Academia Europaea (The Academy of Europe). He is a Fellow of the International Federation of Automatic Control (FIFAC), an Honorary Fellow of the Institution of Engineers Australia (HonFIEAust), and a Fellow of the Chinese Association of Automation (FCAA). He is a Highly Cited Researcher in both Engineering and Computer Science (Clarivate). He has served as an AdCom Member of IEEE Industrial Electronics Society (IES), a Member of IEEE IES Fellows Committee, a Member of IEEE IES Publications Committee, Chair of IEEE IES Technical Committee on Network-Based Control Systems and Applications, and the Co-Editor-in-Chief of IEEE Transactions on Industrial Informatics. He is currently the President-Elect, an Executive Board Member, and a Steering Committee Member of Asian Control Association (ACA). He is currently the Editor-in-Chief of IEEE/CAA Journal of Automatica Sinica and the Co-Editor of Australian Journal of Electrical and Electronic Engineering
Corresponding author: Zhihong Man, e-mail: zman@swin.edu.au
Received Date: 2024-08-27
Revised Date: 2025-01-15
Accepted Date: 2025-02-24

Available Online: 2025-05-29

Abstract

Abstract

In this paper, the lateral dynamics of road vehicles (LDRV) is further studied from the viewpoint of vehicle informatics. It is seen that LDRV is first decoupled and the vehicle slip angle is proved to be observable from the yaw rate measurements. A new methodology of parameter estimation using steady-state yaw rate measurements (PESYRM) is then developed to accurately estimate the parameters of LDRV. The important characteristics of PESYRM comprise four parts: ( i ) The steering angle input to LDRV is chosen as the linear combination of sinusoids; ( ii ) Only the steady state information of yaw rate in any fundamental period is required to accurately estimate the unknown parameters of LDRV; ( iii ) Unlike many existing parameter estimation methods, the time consuming computing of the inverse of high-dimensional data matrix is avoided by making full use of the orthogonal properties of trigonometric base functions; ( iv ) All of system information of LDRV is embedded in the measurements of the steady state yaw rate in any fundamental period. A simulation example is carried out to show the advantages and effectiveness of the new research findings for LDRV.
- Lateral dynamics,
- parameter estimation,
- road vehicle,
- steady-state,
- vehicle informatics

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References(41)

References

[1]	Z. Zhou, Y. Wang, G. Zhou, X. Liu, M. Wu, and K. Dai, “Vehicle lateral dynamics-inspired hybrid model using neural network for parameter identification and error characterization,” IEEE Trans. Veh. Technol., vol. 73, no. 11, pp. 16173–16186, Nov. 2024. doi: 10.1109/TVT.2024.3416317
[2]	D. Jeong, G. Ko, and S. B. Choi, “Estimation of sideslip angle and cornering stiffness of an articulated vehicle using a constrained lateral dynamics model,” Mechatronics, vol. 85, p. 102810, Aug. 2022. doi: 10.1016/j.mechatronics.2022.102810
[3]	L. Bascetta and G. Ferretti, “LFT-based identification of lateral vehicle dynamics,” IEEE Trans. Veh. Technol., vol. 71, no. 2, pp. 1349–1362, Feb. 2022. doi: 10.1109/TVT.2021.3134591
[4]	B. A. Vicente, S. S. James, and S. R. Anderson, “Linear system identification versus physical modeling of lateral-longitudinal vehicle dynamics,” IEEE Trans. Control Syst. Technol., vol. 29, no. 3, pp. 1380–1387, May 2021. doi: 10.1109/TCST.2020.2994120
[5]	G. Reina and A. Messina, “Vehicle dynamics estimation via augmented Extended Kalman Filtering,” Measurement, vol. 133, pp. 383–395, Feb. 2019. doi: 10.1016/j.measurement.2018.10.030
[6]	R. Rajamani, Vehicle Dynamics and Control. 2nd ed. New York, USA: Springer, 2012.
[7]	D. Chindamo, B. Lenzo, and M. Gadola, “On the vehicle sideslip angle estimation: A literature review of methods, models, and innovations,” Appl. Sci., vol. 8, no. 3, p. 355, Mar. 2018. doi: 10.3390/app8030355
[8]	F. Lai, X. Huang, and X. Ye, “Analysis of vehicle driving stability based on longitudinal-lateral and vertical unified dynamics model,” Int. J. Automot. Technol., vol. 23, no. 1, pp. 73–87, Feb. 2022. doi: 10.1007/s12239-022-0006-1
[9]	X. Jin, G. Yin, and N. Chen, “Advanced estimation techniques for vehicle system dynamic state: A survey,” Sensors, vol. 19, no. 19, p. 4289, Oct. 2019. doi: 10.3390/s19194289
[10]	W. J. Manning and D. A. Crolla, “A review of yaw rate and sideslip controllers for passenger vehicles,” Trans. Inst. Meas. Control, vol. 29, no. 2, pp. 117–135, Jun. 2007. doi: 10.1177/0142331207072989
[11]	B. Lenzo, A. Sorniotti, P. Gruber, and K. Sannen, “On the experimental analysis of single input single output control of yaw rate and sideslip angle,” Int. J. Automot. Technol., vol. 18, no. 5, pp. 799–811, Oct. 2017. doi: 10.1007/s12239-017-0079-4
[12]	K. Nam, S. Oh, H. Fujimoto, and Y. Hori, “Estimation of sideslip and roll angles of electric vehicles using lateral tire force sensors through RLS and Kalman filter approaches,” IEEE Trans. Ind. Electron., vol. 60, no. 3, pp. 988–1000, Mar. 2013. doi: 10.1109/TIE.2012.2188874
[13]	F. Cheli, E. Sabbioni, M. Pesce, and S. Melzi, “A methodology for vehicle sideslip angle identification: Comparison with experimental data,” Veh. Syst. Dyn., vol. 45, no. 6, pp. 549–563, May 2007. doi: 10.1080/00423110601059112
[14]	A. Y. Ungoren, H. Peng, and H. E. Tseng, “A study on lateral speed estimation methods,” Int. J. Veh. Auton. Syst., vol. 2, no. 1-2, pp. 126–144, May 2004.
[15]	K. T. Leung, J. F. Whidborne, D. Purdy, and A. Dunoyer, “A review of ground vehicle dynamic state estimations utilising GPS/INS,” Veh. Syst. Dyn., vol. 49, no. 1-2, pp. 29–58, Feb. 2011. doi: 10.1080/00423110903406649
[16]	D. Piyabongkarn, R. Rajamani, J. A. Grogg, and J. Y. Lew, “Development and experimental evaluation of a slip angle estimator for vehicle stability control,” IEEE Trans. Control Syst. Technol., vol. 17, no. 1, pp. 78–88, Jan. 2009. doi: 10.1109/TCST.2008.922503
[17]	J. Ryu and J. C. Gerdes, “Integrating inertial sensors with global positioning system (GPS) for vehicle dynamics control,” J. Dyn. Syst. Meas. Control, vol. 126, no. 2, pp. 243–254, Jun. 2004. doi: 10.1115/1.1766026
[18]	S. Lee, K. Nakano, and M. Ohori, “On-board identification of tyre cornering stiffness using dual Kalman filter and GPS,” Veh. Syst. Dyn., vol. 53, no. 4, pp. 437–448, Jan. 2015. doi: 10.1080/00423114.2014.999800
[19]	M. Gadola, D. Chindamo, M. Romano, and F. Padula, “Development and validation of a Kalman filter-based model for vehicle slip angle estimation,” Veh. Syst. Dyn., vol. 52, no. 1, pp. 68–84, Jan. 2014. doi: 10.1080/00423114.2013.859281
[20]	J. Dakhlallah, S. Glaser, S. Mammar, and Y. Sebsadji, “Tire-road forces estimation using extended Kalman filter and sideslip angle evaluation,” in Proc. American Control Conf., Seattle, USA, 2008, pp. 4597–4602.
[21]	M. Doumiati, A. Charara, A. Victorino, and D. Lechner, Vehicle Dynamics Estimation Using Kalman Filtering: Experimental Validation. London, UK: ISTE Ltd., 2013.
[22]	M. Gadola, D. Chindamo, M. Romano, and F. Padula, “Development and validation of a Kalman filter-based model for vehicle slip angle estimation,” Veh. Syst. Dyn., vol. 52, no. 1, pp. 68–84, Dec. 2014. doi: 10.1080/00423114.2013.859281
[23]	B.-C. Chen and F.-C. Hsieh, “Sideslip angle estimation using extended Kalman filter,” Veh. Syst. Dyn., vol. 46, no. s1, pp. 353–364, Jan. 2008.
[24]	K. Jiang, A. C. Victorino, and A. Charara, “Adaptive estimation of vehicle dynamics through RLS and Kalman filter approaches,” in Proc. 18th Int. Conf. Intelligent Transportation Systems, Gran Canaria, Spain, 2015, pp. 1741–1746.
[25]	S. Antonov, A. Fehn, and A. Kugi, “Unscented Kalman filter for vehicle state estimation,” Veh. Syst. Dyn., vol. 49, no. 9, pp. 1497–1520, Mar. 2011. doi: 10.1080/00423114.2010.527994
[26]	P. J. T. Venhovens and K. Naab, “Vehicle dynamics estimation using Kalman filters,” Veh. Syst. Dyn., vol. 32, no. 2–3, pp. 171–184, Aug. 1999. doi: 10.1076/vesd.32.2.171.2088
[27]	B. L. Boada, M. J. L. Boada, and V. Diaz, “Vehicle sideslip angle measurement based on sensor data fusion using an integrated ANFIS and an unscented Kalman filter algorithm,” Mech. Syst. Signal Process., vol. 72-73, pp. 832–845, May 2016. doi: 10.1016/j.ymssp.2015.11.003
[28]	C.-K. Chen and A.-T. Le, “Vehicle side-slip angle and lateral force estimator based on extended Kalman filtering algorithm,” in AETA 2015: Recent Advances in Electrical Engineering and Related Sciences, V. H. Duy, T. T. Dao, I. Zelinka, H.-S. Choi, and M. Chadli, Eds. Cham, Germany: Springer, 2016, pp. 377–388.
[29]	C. Zong, D. Hu, X. Yang, Z. Pan, and Y. Xu, “Vehicle driving state estimation based on extended Kalman filter,” J. Jilin Univ. (Eng. Technol. Ed.), vol. 39, no. 1, pp. 7–11, Jan. 2009.
[30]	Y. Chen, Y. Ji, and K. Guo, “A reduced-order nonlinear sliding mode observer for vehicle slip angle and tyre forces,” Veh. Syst. Dyn., vol. 52, no. 12, pp. 1716–1728, Sep. 2014. doi: 10.1080/00423114.2014.960430
[31]	B. Subudhi and S. S. Ge, “Sliding-mode-observer-based adaptive slip ratio control for electric and hybrid vehicles,” IEEE Trans. Intell. Transport. Syst., vol. 13, no. 4, pp. 1617–1626, Dec. 2012. doi: 10.1109/TITS.2012.2196796
[32]	N. Patel, C. Edwards, and S. K. Spurgeon, “Optimal braking and estimation of tyre friction in automotive vehicles using sliding modes,” Int. J. Syst. Sci., vol. 38, no. 11, pp. 901–912, Oct. 2007. doi: 10.1080/00207720701409637
[33]	M. Tanelli, A. Ferrara, and P. Giani, “Combined vehicle velocity and tire-road friction estimation via sliding mode observers,” in Proc. IEEE Int. Conf. Control Applications, Dubrovnik, Croatia, 2012, pp. 130–135.
[34]	N. Mśirdi, A. Rabhi, L. Fridman, J. Davila, and Y. Delanne, “Second order sliding mode observer for estimation of velocities, wheel sleep, radius and stiffness,” in Proc. American Control Conf., Minneapolis, USA, 2006, pp. 3316–3321.
[35]	O. Khemoudj, H. Imine, and M. Djemaï, “Heavy duty vehicle tyre forces estimation using variable gain sliding mode observer,” Int. J. Veh. Des., vol. 62, no. 2-4, pp. 274–288, Jan. 2013.
[36]	Y. Chen, Y. Ji, and K. Guo, “A reduced-order nonlinear sliding mode observer for vehicle slip angle and tyre forces,” Veh. Syst. Dyn., vol. 52, no. 12, pp. 1716–1728, Sep. 2014. doi: 10.1080/00423114.2014.960430
[37]	J. J. Rath, K. C. Veluvolu, M. Defoort, and Y. C. Soh, “Higher-order sliding mode observer for estimation of tyre friction in ground vehicles,” IET Control Theory Appl., vol. 8, no. 6, pp. 399–408, Apr. 2014. doi: 10.1049/iet-cta.2013.0593
[38]	J. Chen, J. Song, L. Li, G. Jia, X. Ran, and C. Yang, “UKF-based adaptive variable structure observer for vehicle sideslip with dynamic correction,” IET Control Theory Appl., vol. 10, no. 14, pp. 1641–1652, Sep. 2016. doi: 10.1049/iet-cta.2015.1030
[39]	X. Huang and J. Wang, “Robust sideslip angle estimation for lightweight vehicles using smooth variable structure filter,” in Proc. ASME Dynamic Systems and Control Conf., Palo Alto, USA, 2013, pp. 21–23.
[40]	J. C. Limroth and T. Kurfess, “Real-time vehicle parameter estimation and equivalent moment electronic stability control,” Int. J. Veh. Des., vol. 68, no. 1-3, pp. 221–244, Aug. 2015.
[41]	C. L. Phillips, J. M. Parr, and E. A. Riskin, Signals, Systems, and Transforms. 3rd ed. Upper Saddle River, USA: Prentice Hall, 2003.

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A New Parameter Estimation Methodology Using Steady State Yaw Rate Measurements for Lateral Vehicle Dynamics

doi: 10.1109/JAS.2025.125366

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