Volume 15, Issue 1 (Journal of Control, V.15, N.1 Spring 2021)
JoC 2021, 15(1): 67-78 |
Back to browse issues page
1- Sahand University of Technology
Abstract: (6494 Views)
In designing the anti-lock braking system (ABS), some states and parameters of vehicle system such as road friction of coefficient and wheel slip should be estimated due to lack of cost effective and reliable sensors for direct measurement. Because of nonlinear characteristics of vehicle dynamics and tire forces, development of a nonlinear estimation algorithm is necessary. However, consideration of physical constraints can enhance the accuracy and reliability of estimation algorithm in real situations. In this paper, the extended Kalman filter (EKF) is applied for the ABS and its algorithm is modified in way that the physical limitations of road friction and wheel slip could be considered. The performance of the modified EKF in the constrained case is compared with the conventional EKF. At the rest of paper, a nonlinear predictive-based controller is analytically designed for the ABS and combined with the proposed constrained estimation algorithm. In order to decrease the effect of estimation errors on tracking performance, the integral feedback technique is combined with the control strategy. The simulation results indicate that not only the proposed algorithm improves the tracking accuracy in the presence of uncertainties, but also the control signal oscillations with high frequency will be prevented.
Keywords: Anti-lock braking system (ABS), Constrained nonlinear estimation, Road friction of coefficient, predictive control, Integral feedback.
Type of Article: Research paper |
Subject:
Special
Received: 2019/07/19 | Accepted: 2020/06/5 | ePublished ahead of print: 2020/07/26 | Published: 2021/05/22
Received: 2019/07/19 | Accepted: 2020/06/5 | ePublished ahead of print: 2020/07/26 | Published: 2021/05/22
References
1. [1] Aghasizade, S., Mirzaei, M., & Rafatnia, S. (2018). "Novel constrained control of active suspension system integrated with anti-lock braking system based on 14-degree of freedom vehicle model". Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics, vol. 232, no. 4, pp. 501-520. [DOI:10.1177/1464419317752612]
2. [2] Aghasizade, S., Mirzaei, M., & Rafatnia, S. (2018). "The effect of road quality on integrated control of active suspension and anti-lock braking systems". AUT Journal of Mechanical Engineering, vol. 3, no. 1, pp. 123-135.
3. [3] Mirzaeinejad, H., & Mirzaei, M. (2010). "A novel method for non-linear control of wheel slip in anti-lock braking systems". Control Eng. Pract., vol. 18, no. 8, pp. 918-926. [DOI:10.1016/j.conengprac.2010.03.015]
4. [4] Mirzaei, M., & Mirzaeinejad, H. (2017). "Fuzzy scheduled optimal control of integrated vehicle braking and steering systems". IEEE/ASME Trans. Mechatron., vol. 22, no. 5, pp. 2369-2379. [DOI:10.1109/TMECH.2017.2749002]
5. [5] Mirzaeinejad, H., Mirzaei, M., & Rafatnia, S. (2018). "A novel technique for optimal integration of active steering and differential braking with estimation to improve vehicle directional stability". ISA Trans., vol. 80, pp. 513-527. [DOI:10.1016/j.isatra.2018.05.019]
6. [6] Reina, G., & Messina, A. (2019). "Vehicle dynamics estimation via augmented Extended Kalman Filtering". Measurement, vol. 133, pp. 383-395. [DOI:10.1016/j.measurement.2018.10.030]
7. [7] Guo, H., Chen, H., Xu, F., Wang, F., & Lu, G. (2013). "Implementation of EKF for vehicle velocities estimation on FPGA". IEEE Trans. Ind. Electron., vol. 60, no. 9, pp. 3823-3835. [DOI:10.1109/TIE.2012.2208436]
8. [8] Patra, N., & Sadhu, S. (2015, December). "Adaptive Extended Kalman Filter for the state estimation of Anti-Lock Braking System". In 2015 Annual IEEE India Conference (INDICON)(pp. 1-6). IEEE. [DOI:10.1109/INDICON.2015.7443199]
9. [9] Antonov, S., Fehn, A., & Kugi, A. (2011). "Unscented Kalman filter for vehicle state estimation". Veh. Sys. Dyn., vol. 49, no.9, pp. 1497-1520. [DOI:10.1080/00423114.2010.527994]
10. [10] Rajendran, S., Spurgeon, S. K., Tsampardoukas, G., & Hampson, R. (2019). "Estimation of road frictional force and wheel slip for effective antilock braking system (ABS) control". Int. J. Robust. Nonlin. Control, vol. 29, no.3, pp. 736-765. [DOI:10.1002/rnc.4366]
11. [11] Zhang, W., Wang, Z., Zou, C., Drugge, L., & Nybacka, M. (2019). "Advanced Vehicle State Monitoring: Evaluating Moving Horizon Estimators and Unscented Kalman Filter". IEEE Trans. Veh. Technol., vol. 68, no. 6, pp. 5430-5442. [DOI:10.1109/TVT.2019.2909590]
12. [12] Sun, F., Huang, X., Rudolph, J., & Lolenko, K. (2015). "Vehicle state estimation for anti-lock control with nonlinear observer". Control Eng. Prac., vol. 43, pp. 69-84. [DOI:10.1016/j.conengprac.2015.07.003]
13. [13] Laowanitwattana, J., & Uatrongjit, S. (2016, June). "Estimation of induction motor states and parameters based on extended kalman filter considering parameter constraints". In 2016 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM) (pp. 755-760). IEEE. [DOI:10.1109/SPEEDAM.2016.7525829]
14. [14] Lu, F., Ju, H., & Huang, J. (2016). "An improved extended Kalman filter with inequality constraints for gas turbine engine health monitoring". Aerosp. Sci. Technol., vol. 58, pp. 36-47. [DOI:10.1016/j.ast.2016.08.008]
15. [15] Joukov, V., Bonnet, V., Venture, G., & Kulić, D. (2015, September). "Constrained dynamic parameter estimation using the extended Kalman filter". In 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 3654-3659). IEEE. [DOI:10.1109/IROS.2015.7353888]
16. [16] Pacejka, H. Tire and vehicle dynamics. Elsevier, 2005.
17. [17] Rajamani, R. Vehicle dynamics and control. Springer Science & Business Media, 2011. [DOI:10.1007/978-1-4614-1433-9_2]
18. [18] Simon, D. Optimal state estimation: Kalman, H infinity, and nonlinear approaches. John Wiley & Sons, 2006. [DOI:10.1002/0470045345]
19. [19] Simon, D., & Chia, T. L. (2002). "Kalman filtering with state equality constraints".IEEE Trans. Aerosp. Electron. Syst., vol. 38, no. 1, pp. 128-136. [DOI:10.1109/7.993234]
20. [20] Chia, T. L. (1985). Parameter identification and state estimation of constrained systems (Doctoral dissertation, Case Western Reserve University).
21. [21] Chen, W. H., Ballance, D. J., & Gawthrop, P. J. (2003). "Optimal control of nonlinear systems: a predictive control approach". Automatica, vol. 39, no. 4, pp. 633-641. [DOI:10.1016/S0005-1098(02)00272-8]
22. [22] Khalil, H. K. Nonlinear control (pp. 33-45). New York: Pearson, 2015.
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |