Adaptive Fuzzy Sliding Mode Control Based on Pi-sigma Fuzzy Neutral Network for Hydraulic Hybrid Control System Using New Hydraulic Transformer

  • Wei ShenEmail author
  • Jiehao Wang


Control issue is the key for applying hydraulic hybrid system, especially for common pressure rail (CPR) system which has the huge potential to enhance efficiency. In the paper, the mathematical model of hydraulic cylinder speed control system using new hydraulic transformer is established. Then an adaptive fuzzy sliding mode controller based on Pi-sigma fuzzy neutral network is designed to solve the problem of parameter uncertainty and nonlinearity without establishing the precise model. Furthermore, compared to PID and conventional adaptive fuzzy system, the controller proposed can achieve good control performance and strong robustness in the presence of time-varying uncertainty.


Hydraulic hybrid system hydraulic transformer pi-sigma fuzzy neutral network sliding mode control 


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  1. [1]
    P. Achten, T. van den Brink, J. Potma, M. H. Schellekens, and G. Vael, “A four-quadrant hydraulic transformer for hybrid vehicles,” Proc. of 11th Scandinavian International Conference on Fluid Power, Linkôping, Sweden, 2009.Google Scholar
  2. [2]
    W. Shen, Y. Mai, X. Su, Z. Jinbao, and J. Jiang, “A new electric hydraulic actuator adopted the variable displacement pump,” Asian Journal of Control, vol. 18, no. 1, pp. 178–191, 2016.MathSciNetCrossRefzbMATHGoogle Scholar
  3. [3]
    G. Vael, P. Achten, and Z. Fu, “The innas hydraulic transformer the key to the hydrostatic common pressure rail,” SAE Technical Paper, 2000-01-2561, 2000.Google Scholar
  4. [4]
    W. Shen, H. Huang, Y. Pang, and X. Su, “Review of the energy saving hydraulic system based on common pressure rail,” IEEE Access, vol. 5, pp. 655–669, 2017.CrossRefGoogle Scholar
  5. [5]
    J. Jiang, H. Lu, R. Zhou, Q. Yu, and N. Guo, “Development of hydraulic transformer in constant pressure rail system,” Journal of Southeast University (Natural Science Edition), vol. 36, no. 5, pp. 869–874, 2006.Google Scholar
  6. [6]
    J. Jiang, A. Yu, and W. Shen, “The review of full hydraulic hybrid excavator based on common pressure rail network,” Chinese Hydraulics and Pneumatics, vol. 9, pp. 44–49, 2010.Google Scholar
  7. [7]
    P. Achten, Z. Fu, and G. Vael, “Transforming future hydraulics: a new design of a hydraulic transformer,” Proc. of The 5th Scandinavian International Conference on Fluid Power, pp. 97, 1997.Google Scholar
  8. [8]
    W. Shen, J. Jiang, X. Su, and H. R. Karimi, “Control strategy analysis of the hydraulic hybrid excavator,” Journal of the Franklin Institute, vol. 352, no. 2, pp. 541–561, 2015.CrossRefzbMATHGoogle Scholar
  9. [9]
    H. Lu, J. Jiang, and F. Wang, “Study on hydraulic transformer driving linear load system based on fuzzy tuning PID control strategy,” Machine Tool & Hydraulics, vol. 1, pp. 021, 2009.Google Scholar
  10. [10]
    Y. Chen, S. Liu, M. Miao, X. Zhou, and Y. Yao, “Research on control performance of valve plant of hydraulic transformer,” Machine Tool & Hydraulics, vol. 21, pp. 004, 2010.Google Scholar
  11. [11]
    S. Liu, D. Xie, and T. Shang, “Control strategy of the value plant of hydraulic transformer based on fractional order PID Controller,” Journal of Beijing University of Technology, vol. 10, pp. 004, 2013.Google Scholar
  12. [12]
    W. Shen, J. Zhang, Y. Sun, D. Zhang, and J. Jiang, “Effect of cavitation bubble collapse on hydraulic oil temperature” Journal of Central South University, vol. 23, no. 7, pp. 1657–1668, 2016.CrossRefGoogle Scholar
  13. [13]
    M. Mohamed, X. G. Yan, S. K. Spurgeon, and B. Jiang, “Robust sliding-mode observers for large-scale systems with application to a multimachine power system,” IET Control Theory & Applications, vol. 11, no. 8, pp. 1307–1315, 2016.MathSciNetCrossRefGoogle Scholar
  14. [14]
    R. Xu and M. Zhou, “A self-adaption compensation control for hysteresis nonlinearity in piezo-actuated stages based on Pi-sigma fuzzy neural network,” Smart Materials and Structures, vol. 27, no. 4, pp. 045002, 2018.CrossRefGoogle Scholar
  15. [15]
    S. Dai, K. Kobayashi, and Y. Yamashita, “A new electric hydraulic actuator adopted the variable displacement pump,” MPC-based Co-design of Control and Routing for Wireless Sensor and Actuator Networks, vol. 16, no. 3, pp. 953–960, 2018.Google Scholar
  16. [16]
    M. Van, “An enhanced robust fault tolerant control based on an adaptive fuzzy PID-nonsingular fast terminal sliding mode control for uncertain nonlinear systems,” IEEE/ASME Transactions on Mechatronics, 2018.Google Scholar
  17. [17]
    M. R. Soltanpour, M. H. Khooban, and M. R. Khalghani, “An optimal and intelligent control strategy for a class of nonlinear systems: adaptive fuzzy sliding mode,” Journal of Vibration and Control, vol. 22, no. 1, pp. 159–175, 2016.MathSciNetCrossRefzbMATHGoogle Scholar
  18. [18]
    M. Liu, L. Zhang, P. Shi, and Y. Zhao, “Sliding mode control of continuous-time Markovian jump systems with digital data transmission,” Automatica, vol. 80, pp. 200–209, 2017.MathSciNetCrossRefzbMATHGoogle Scholar
  19. [19]
    J. Ghabi and H. Dhouibi, “Discrete time sliding mode controller using a disturbance compensator for nonlinear uncertain systems,” International Journal of Control Automation & Systems, vol. 13, no. 3, pp. 1156–1164, 2018.CrossRefGoogle Scholar
  20. [20]
    Y. Zhu, Z. Zhong, M. V. Basin, and D. Zhou, “A descriptor system approach to stability and stabilization of discrete-time switched PWA systems,” IEEE Transactions on Automatic Control, vol. 63, no. 10, pp. 3456–3463, 2018.MathSciNetCrossRefzbMATHGoogle Scholar
  21. [21]
    S. Tong and H. X. Li, “Fuzzy adaptive sliding-mode control for MIMO nonlinear systems,” IEEE Transactions on Fuzzy Systems, vol. 11, no. 3, pp. 354–360, 2013.CrossRefGoogle Scholar
  22. [22]
    M. Liu, L. Zhang, and W. Zheng, “Fault reconstruction for stochastic hybrid systems with adaptive discontinuous observer and non-homogeneous differentiator,” Automatica, vol. 85, pp.339–348, 2017.MathSciNetCrossRefzbMATHGoogle Scholar
  23. [23]
    Y. Wang, Y. Gao, H. R. Karimi, H. Shen, and Z. Fang, “Sliding mode control of fuzzy singularly perturbed systems with application to electric circuit,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 48, no. 10, pp. 1667–1675, 2017.CrossRefGoogle Scholar
  24. [24]
    M. Liu, L. Zhang, P. Shi, and Y. Zhao, “Fault estimation sliding mode observer with digital communication constraints,” IEEE Transactions on Automatic Control, vol. 63, no. 10, pp. 3434–3441, 2018.MathSciNetCrossRefzbMATHGoogle Scholar
  25. [25]
    A. Errachdi and M. Benrejeb, “Performance comparison of neural network training approaches in Indirect adaptive control,” International Journal of Control Automation & Systems, vol. 16, no. 3, pp. 1448–1458, 2018.CrossRefGoogle Scholar
  26. [26]
    N. Sun, T. Yang, Y. Fang, Y. Wu, and H. Chen, “Transportation control of double-pendulum cranes with a nonlinear quasi-PID scheme: design and experiments,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2018. DOI: 10.1109/TSMC.2018.2871627Google Scholar

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© ICROS, KIEE and Springer 2019

Authors and Affiliations

  1. 1.department of Mechatronics EngineeringUniversity of Shanghai for Science and TechnologyShanghaiChina
  2. 2.State Key Laboratory of Fluid Power and Mechatronic SystemsZhejiang UniversityHangzhouChina

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