This content is not included in your SAE MOBILUS subscription, or you are not logged in.
Braking Force Identification of EMB Using Recursive Least-squares Method and Disturbance Observer Iteratively
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 03, 2018 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
An identification method using recursive least-squares method with moving data window and reduced-order disturbance observer iteratively is proposed in this paper to identify fast time-varying braking force in the electronic mechanical braking system (EMB). For the type of EMB which generates braking force by balls screw and motor mounted beside wheel, the actuator will go rapidly to eliminate clearance at beginning of braking process by means of raising the braking response speed, and at the same time, increasing the motor output torque which might be far larger than required. The proposed identification method is able to identify the point of contact between the brake pads and the disk in time by identifying the change of break force, and the torque of motor will be changed in time to reduce the braking force overshoot so that brake locking is avoided. Because of the existence of clearance in EMB system, when braking happens, braking force will change from zero to a big value at arbitrary moment, which enhances difficulty of online identification. The dynamics model of braking process is built in this paper, and the braking force is linear transformed to load torque of motor. RLS is used to identify the rotational inertia and resistance coefficient which is transformed to damping coefficient of the system. Then the identification parameters is used in reduced-order disturbance observer to identify the load torque, and then the RLS uses the load torque to identify parameters again. The above process is repeated iteratively. The data window is used to avoid the ‘data saturation’ and avoid the accumulation of error in the observer which can improve accuracy of identification result of time-varying parameters.
CitationYu, L., Zheng, S., Chang, J., and Liu, X., "Braking Force Identification of EMB Using Recursive Least-squares Method and Disturbance Observer Iteratively," SAE Technical Paper 2018-01-1381, 2018, https://doi.org/10.4271/2018-01-1381.
Data Sets - Support Documents
|[Unnamed Dataset 1]|
|[Unnamed Dataset 2]|
- Yu, L., Liu, X., Xie, Z., and Chen, Y., “Review of Brake-by-Wire System Used in Modern Passenger Car,” ASME Technical Paper DETC2016-59279, 2016, doi:10.1115/DETC2016-59279.
- Putz, M.H., Wunsch, C., Schiffer, M. et al., “Test Results of A Sensor-Less, Highly Nonlinear Electro-Mechanical Brake,” SAE Technical Paper 012541, 2014, doi:10.4271/2014-01-2541.
- Schwarz, R., Isermann, R., Böhm, J. et al., “Modeling and Control of an Electromechanical Disk Brake,” SAE Technical Paper 980600, 1998, doi:10.4271/980600.
- Schwarz, R., Isermann, R., Böhm, J. et al., “Clamping Force Estimation for a Brake-by-Wire Actuator,” SAE Technical Papers 1999-01-0482, 1999, doi:10.4271/1999-01-0482.
- Hoseinnezhad, R., Saric, S., and Bab-Hadiashar, A., “Estimation of Clamp Force in Brake-by-Wire Systems: A Step-by-Step Identification Approach,” SAE Technical Paper 2006-01-1154, 2006, doi:10.4271/2006-01-1154.
- Saric, S., Bab-Hadiashar, A., and Hoseinnezhad, R., “Clamp-Force Estimation for a Brake-by-Wire System: A Sensor-Fusion Approach,” IEEE Transactions on Vehicular Technology 57(2):778-786, 2008, doi:10.1109/TVT.2007.905251.
- Wei Z, Xu J, Halim D. “Clamping Force Control of Sensor-Less Electro-Mechanical Brake Actuator,” presented at IEEE International Conference on Mechatronics and Automation, IEEE, China, 2017, Aug. 6-9, 2017.
- Lozano, R., Dimogianopoulos, D., and Mahony, R., “Identification of Linear Time-Varying Systems Using a Modified Least-Squares Algorithm,” Automatica 36(7):1009-1015, 2000, doi:10.1016/S0005-1098(00)00010-8.
- Liu, Y.Q., Shen, Y.X., and Zhi-Cheng, J.I., “Identification of Induction Motors Based on Improved Least Square Algorithm,” Electric Machines & Control Application, 13-17, Dec., 2008.
- Chen Z, Yang L, Zhang Y, et al. “A Control Method of PMSM Current-Loop Based on On-line Parameter Identification,” presented at Industrial Electronics and Applications. IEEE, July 18-20, 2012, doi: 10.1109/ICIEA.2012.6360763.
- Wang, Z., Wang, C., Qi, X. et al., “Study on Load Torque Identification On-line Based on Vector Control of Saliency PMSMs,” Procedia Engineering 23:89-94, 2011, doi:10.1016/j.proeng.2011.11.2470.
- Teng, F., Hongsheng, L.I., Zhang, J. et al., “Research on Inertia Identification Based on Landau Discrete-time Recursive Algorithm,” Micromotors 16-19, Jan., 2012.
- Ding, X.Z., Zhang, C.R., Hu-Xiu, L.I. et al., “Identification of inertia and state estimation for PMSM,” Journal of Shandong University70-82, 2012.
- Zhibin, L.I., Zhao, J., and Liu, Y., “Inertia Identification Based on High Precision Landau Discrete time Recursive Algorithm,” Electric Drive58-60, 2013.
- Liang, J., “Research on Inertia Identification of PMSM,” Nanjing University of Aeronautics and Astronautics, 2011, doi:10.7666/d.d166821.
- Xu D, Gao Y. “An Approach to Torque Ripple Compensation for High Performance PMSM Servo System,” presented at Power Electronics Specialists Conference, 2004. Pesc 04. 2004 IEEE. IEEE, 5, 3256-3259, 2005.
- Yan S, Xu D, Wang G, et al. “Low Speed Control of PMAC Servo System Based on Reduced-order Observer,” presented at IEEE/RSI International Conference on Intelligent Robots and Systems, IROS 2006, Beijing, China, October 9-15, 2006. DBLP, 2006, 4886-4889.
- Zheng, Z., Li, Y., Xiao, X. et al., “Load Torque Observer of Permanent Magnet Synchronous Motor,” Transactions of China Electrotechnical Society30-36, 2010.
- Zhao S, Cui L, Liu G, et al. “An Improved Torque Feed-Forward Control with Observer-Based Inertia Identification in PMSM Drives,” presented at International Conference on Electrical Machines and Systems, IEEE, China, Oct. 22-25, 2012.
- Chen, Z.F., Zhong, Y.R., and Jie, L.I., “Comparison of Three Intelligent Optimization Algorithms for Parameter Identification of Induction Motors,” Electric Machines & Control7-12, 2010, doi:10.15938/j.emc.2010.11.003.
- Wang, S., Wan, S., Zhou, L., and Huang, H., “Identification of PMSM Servo System's Load Torque and Moment of Inertia by Ant Colony Algorithm,” Transactions of China Electrotechnical Society18-25, 2011.
- Liu, Z., Li, X., Zhou, S. et al., “Comprehensive Learning Particle Swarm Optimization Algorithm based on Immune Mechanism for Permanent Magnet Synchronous Motor Parameter Identification,” Transactions of China Electrotechnical Society118-126, 2014.
- C.Yang, Sun, D., C.Sun. “System Identification and Self-adaptive Control,” (Chongqing University Press, 2003), ISBN: 7562428174.
- Qi Wu. “Principles of Automatic Control, Second Edition,” (Beijing, Tsinghua university Press, 2006), ISBN: 978-7-302-13227-1.