Advertisement

International Journal of Automotive Technology

, Volume 19, Issue 5, pp 825–836 | Cite as

Novel Bench-Based Inspection Approach for Automobile Anti-Lock Braking System

  • Xiangmo Zhao
  • Ruru Hao
  • Zhou Zhou
  • Amira Ashour
  • Nilanjan Dey
Article
  • 9 Downloads

Abstract

Bench inspection approach for automobile Anti-lock Braking System (ABS) has gained research interests recently due to its high efficiency, small site occupation and insusceptibility to environment influences. The current work proposed a novel systematic bench inspection approach for ABS. In order to dynamically simulate various road adhesion coefficients, torque controllers are used for loading different torques to the drums. Furthermore, flywheels are adopted to simulate the translational inertia of the vehicle braking on road for compensating the inertial energy of ABS road experiment on the bench. The principal component analysis (PCA) is applied for accurate and efficient data analysis. The automatic evaluation of ABS is achieved by using the processed PCA data as an input to the back-propagation (BP) neural network classifier. The experiments established that the new approach can accurately simulate various road braking conditions. It can be carried out for the inspection of ABS installed in the car.

Key words

Anti-lock braking system Bench inspection Road adhesion coefficient simulation Principal component analysis 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aksjonov, A., Vodovozov, V. and Petlenkov, E. (2016). Design and experimentation of fuzzy logic control for an anti-lock braking system. Proc. IEEE 15th Biennial Blatic Electronics Conf. (Bec), Tallinn, Estonia.Google Scholar
  2. Branciforte, M., Meli, A., Muscato, G. and Porto, D. (2011). ANN and non-integer order modeling of ABS solenoid valves. IEEE Trans. Control Systems Technology 19, 3, 628–635.CrossRefGoogle Scholar
  3. Ciupe, V., Mărgineanu, D. and Lovasz, E.-C. (2017). Scaled test stand simulation for studying the behavior of anti-lock brake systems on bumpy roads. New Advances in Mechanisms, Mechanical Transmissions and Robotics, 46, 197–205.CrossRefGoogle Scholar
  4. Erkin, D., Bilin, A. G. and Tankut, A. (2014). Extremumseeking control of ABS braking in road vehicles with lateral force improvement. IEEE Trans. Control System Technology 22, 1, 230–237.CrossRefGoogle Scholar
  5. Fu, T., Zhao, J. B. and Liu, W. J. (2012). Multi-objective optimization of cutting parameters in high-speed milling based on grey relational analysis coupled with principal component analysis. Frontiers of Mechanical Engineering 7, 4, 445–452.CrossRefGoogle Scholar
  6. Hoang, T. B., Pasillas-Lépine, W., De Bernardinis, A. and Netto, M. (2014). Extended braking stiffness estimation based on a switched observer, with an application to wheel-acceleration control. IEEE Trans. Control System Technology 22, 6, 2384–2392.CrossRefGoogle Scholar
  7. Ko, S., Song, C. and Kim, H. (2016). Cooperative control of the motor and the electric booster brake to improve the stability of an in-wheel electric vehicle. Int. J. Automotive Technology 17, 3, 447–456.CrossRefGoogle Scholar
  8. Koylu, H. and Cinar, A. (2012). Experimental design of control strategy based on brake pressure changes on wet and slippery surfaces of rough road for variable damper setting during braking with activated anti-lock brake system. Proc. Institution of Mechanical Engineers, Part D: J. Automobile Engineering 226, 10, 1303–1324.Google Scholar
  9. Lee, Y. and Zak, S. H. (2002). Designing a genetic neural fuzzy antilock-brake-system controller. IEEE Trans. Evolutionary Computation 6, 2, 198–211.CrossRefGoogle Scholar
  10. Lin, W.-C., Lin, C.-L., Hsu, P.-M. and Wu, M.-T. (2014). Realization of anti-lock braking strategy for electric scooters. IEEE Trans. Industrial Electronics 61, 6, 2826–2833.CrossRefGoogle Scholar
  11. Palladino, A., Fiengo, G. and Lanzo, D. (2012). A portable hardware-in-the-loop (HIL) device for automotive diagnostic control systems. ISA Trans. 51, 1, 229–236.CrossRefGoogle Scholar
  12. Park, J., Wang, B., Jeon, J. and Hwang, S.-H. (2011). Hardware in-the-loop simulation for ABS using 32–bit embedded system. Proc. IEEE 11th Int. Conf. Control, Automation and Systems, Gyeonggi, Korea.Google Scholar
  13. Patra, N. and Datta, K. (2012). Sliding mode controller for wheel-slip control of anti-lock braking system. Proc. IEEE Int. Conf. Advanced Communication Control and Computing Technologies (ICACCCT), Ramanathapuram, India.Google Scholar
  14. Peric, S. L., Antic, D., Milovanovic, M. B., Mitić, D. B., Milojković, M. T. and Nikolić, S. S. (2016). Quasisliding mode control with orthogonal endocrine neural network-based estimator applied in anti-lock braking system. IEEE-ASME Trans. Mechatronics 21, 2, 754–764.CrossRefGoogle Scholar
  15. Reza, Y. and Mojtaba, M. (2015). Design of robust speed and slip controllers for a hybrid electromagnetic brake system. IET Electric Power Applications 9, 4, 307–318.CrossRefGoogle Scholar
  16. Savitski, D., Ivanov, V., Augsburg, K., Shyrokau, B., Wragge-Morley, R., Pütz, T. and Barber, P. (2016). The new paradigm of an anti-lock braking system for a full electric vehicle: Experimental investigation and benchmarking. Proc. Institution of Mechanical Engineers, Part D: J. Automobile Engineering 230, 10, 1364–1377.Google Scholar
  17. Velimir, C., Dragan, A. and Dušan, S. (2013). Longitudinal wheel slip control using dynamic neural networks. Mechatronics 23, 1, 135–146.CrossRefGoogle Scholar
  18. Wang, R. G., Wang, B. and Sun, H. (2010). Development of a single wheel test bench for anti-lock brake system. Proc. IEEE Int. Conf. Optoelectronics and Image Processing, Haikou, China.Google Scholar
  19. William, P. L., Antonio, L. and Mathieu, G. (2012). Design and experimental validation of a nonlinear wheel slip control algorithm. Automatica 48, 8, 1852–1859.MathSciNetCrossRefzbMATHGoogle Scholar
  20. Woo, J. W. and Lee, S. B. (2011). Test-bed design for evaluation of intelligent transportation systems and intelligent vehicle systems. Proc. IEEE 13th Int. Conf. Advanced Communication Technology (ICACT), Seoul, Korea.Google Scholar
  21. Wu, C., Duan, J. M. and Yu, Y. (2010). A hardware in loop test system for pneumatic anti-lock brake system. Int. Conf. Measuring Technology and Mechatronics Automation (ICMTMA), Changsha, China.Google Scholar
  22. Xu, C., Gao, S. M. and Li, M. (2017). A novel PCA-based microstructure descriptor for heterogeneous material design. Computational Materials Science, 130, 39–49.CrossRefGoogle Scholar
  23. Yousefi, F., Mohammadiyan, S. and Karimi, H. (2016). Application of artificial neural network and PCA to predict the thermal conductivities of nanofluids. Heat and Mass Transfer 52, 10, 2141–2154.CrossRefGoogle Scholar
  24. Yun, D. S., Kim, H. S. and Boo, K. S. (2011). Brake performance evaluation of ABS with sliding mode controller on a split road with driver model. Int. J. Precision Engineering and Manufacturing 12, 1, 31–38.CrossRefGoogle Scholar
  25. Zeng, H., Zhan, Y., Kang, X. and Lin, X. (2017). Image splicing localization using PCA-based noise level estimation. Multimedia Tools and Applications 76, 4, 4783–4799.CrossRefGoogle Scholar
  26. Zhang, R., Li, K., Yu, F., He, Z. and Yu, Z. (2017). Novel Electronic braking system design for EVs based on constrained nonlinear hierarchical control. Int. J. Automotive Technology 18, 4, 707–718.CrossRefGoogle Scholar
  27. Zhang, W., Ding, N., Chen, M., Yu, G. and Xu, X. (2011). Development of a low-cost hardware-in-the-loop simulation system as a test bench for anti-lock braking system. Chinese J. Mechanical Engineering 24, 1, 98–104.CrossRefGoogle Scholar

Copyright information

© The Korean Society of Automotive Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Xiangmo Zhao
    • 1
  • Ruru Hao
    • 1
  • Zhou Zhou
    • 1
  • Amira Ashour
    • 2
  • Nilanjan Dey
    • 3
  1. 1.School of Information EngineeringChang’an UniversityXi’anChina
  2. 2.Department of Electronics and Electrical Communication EngineeringTanta UniversityTantaEgypt
  3. 3.Department of Information TechnologyTechno India College of TechnologyWest BengalIndia

Personalised recommendations