Volume 2, Issue 4, July 2017, Page: 42-47
Design of Control System for Vehicle Dynamics and Mass Estimation
Paulinus Chinaenye Eze, Department of Electrical and Electronic Engineering, Federal University of Technology, Owerri, Nigeri
Chinonso Francis Ubaonu, Department of Electrical and Electronic Engineering, Federal University of Technology, Owerri, Nigeri
Bonaventure Onyeka Ekengwu, Department of Electrical and Electronic Engineering, Chukwuemeka Odimegwu Ojukwu University, Uli, Nigeria
Chidiebere Alison Ugoh, Department of Electrical and Electronic Engineering, Federal University of Technology, Owerri, Nigeri
Inaibo Dein Samuel, Department of Electrical and Electronic Engineering, Federal University of Technology, Owerri, Nigeri
Received: Jun. 29, 2017;       Accepted: Jul. 12, 2017;       Published: Oct. 31, 2017
DOI: 10.11648/j.ijimse.20170204.12      View  2745      Downloads  158
Abstract
This paper has presented a control system for vehicle dynamics and mass estimation. The objective of this paper is to use a single-tyre model of slip control integrated with extended Kalman filter (EKF) to estimate the states of a vehicle such as the forward velocity, wheel slip, coefficient of friction of the road surface and the mass that cannot be measured directly. In order to do this, the dynamics of a vehicle moving with a forward velocity were obtained using a single-tyre model. The dynamic equations in continuous time were transformed into their equivalent discrete time form. A two degree of freedom proportional integral and derivative (2DOFPID) control algorithm was implemented for the control loop. An estimator was designed using the extended Kalman filter algorithm to carry out the estimation based on noisy measurement of wheel rotational speed. The entire system was modeled using Matlab/Simulink blocks. Simulations were performed to determine the effectiveness of the estimator. The simulation results showed that the extended Kalman filter effectively estimated the states of a single-tyre model of a vehicle represented by a slip control system. Though the results obtained seemed promising but will be improved if the covariance matrices are calculated with adequate information and are better tuned.
Keywords
Control System, Vehicle Dynamics, Mass, Extended Kalman Filter, Estimation
To cite this article
Paulinus Chinaenye Eze, Chinonso Francis Ubaonu, Bonaventure Onyeka Ekengwu, Chidiebere Alison Ugoh, Inaibo Dein Samuel, Design of Control System for Vehicle Dynamics and Mass Estimation, International Journal of Industrial and Manufacturing Systems Engineering. Vol. 2, No. 4, 2017, pp. 42-47. doi: 10.11648/j.ijimse.20170204.12
Copyright
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Reference
[1]
Alleyne A., “Improved Vehicle Performance Using Combined Suspension and Braking Forces, Vehicle Systems Dynamics,” International Journal of Vehicles and Mobility, Vol. 27, No. 4, 1997, pp. 235-265.
[2]
Beatriz L. Boada, Daniel Garcia-Pozuelo, Maria Jesus L. Boada, and Vicente Diaz, “A constrained Dual Kalman Filter Based on pdf Truncation for Estimation of Vehicle Parameters and Road Bank angle: Analysis and Experimental Validation,” IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 4, Aprill 2017. Pp. 1006-1016. https://doi.org/10.1109/TITS.2016.2594217.
[3]
Erik Jonsson Holm, “Vehicle Mass and Road Grade Estimation Using Kalman Filter,” Institutionen for Systemteknik, Department of Electrical Engineering, 16 August, 2011.
[4]
John S. and J. O. Pedro, “Hybrid Feedback Linearization Slip Control for Anti-lock Braking System”, Acta Polytechnica Hungarica, Vol. 10, No. 1, 2013, pp. 84-95.
[5]
Jingang Guo, Xiaoping Jian and Guangyu Lin, “Performance Evaluation of an Anti-Lock Braking System for Electric Vehicles with a Fuzzy Sliding Mode Controller,” Energies 2014, 7, 6459-6476; doi: 10.3390/en7106459.
[6]
Kidambi, N., Harne, R., Fujii, Y., Pietron, G. et al., "Methods in Vehicle Mass and Road Grade Estimation," SAE Int. J. Passeng. Cars - Mech. Syst. 7(3): 2014, doi: 10.4271/2014-01-0111.
[7]
Gyoungeun Kim, Jaewoo Yoon and Byeongwoo Kim, “Estimation of the Steering Angle Based on Extended Kalman Filter,” International Journal of Multimedia and Ubiquitous Engineering, Vol. 11, No. 12 (2016), pp. 295-308. http://dx.doi.org/10.14257/ijmue.2016.11.12.27.
[8]
Matilde Paiano, Guilo Reina, and Jose-Luis Blanco, “Vehicle Parameter Estimation Using a Model-Based Estimator,” Preprint submitted to Mechanical Systems and Signal Processing, February 8, 2016. Published version: http://www.sciencedirect.com/science/article/pii/S0888327016302205.
[9]
Sagar R. Burkul, Prashant R. Pawar, Kirankumar R. Jagtap, “Estimation of Vehicle Parameters using Kalman Filter: Review,” International Journal of Current Engineering and Technology, E-ISSN 2277 – 4106, P-ISSN 2347 – 5161 ©2014 INPRESSCO®, All Rights Reserved. Available at http://inpressco.com/category/ijcet.
[10]
Savaresi, S. M. and Tanelli, M., “Active Braking Systems Design for Vehicles, Springer, http://www.springer.com/978-1-84996-349-7nternational Conference on Machine Learning and Cybernetics, Guangzhou, 2010, pp. 591-595.
[11]
Wenzel T. A., Burnham K. J., Blundell M. V., and Williams R. A. (2006): “Dual Extended Kalman Filter for Vehicle State and Parameter Estimation,” Vehicle System Dynamics: International Journal of Vehicle Mechanics and Mobility, 44: 2, 153-171. http://dx.doi.org/10.1080/00423110500385949.
[12]
Wragge - Morley, R. T., Herrmann, G., Barber, P., & Burgess, S. C. (2015). Gradient and Mass Estimation from CAN Based Data for a Light Passenger Car. SAE International Journal of Passenger Cars- Mechanical Systems, 8(1), 137-145. DOI: 10.4271/2015-01-0201.
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