Have a personal or library account? Click to login

Intelligent Neural Network Control Strategy of Hydraulic System Driven by Servo Motor

By:
Ma Yu  
Open Access
|Jun 2015

References

  1. Otto Cerman and Petr Hušek, “Adaptive fuzzy sliding mode control for electro-hydraulic servo mechanism”, Expert Systems with Applications 39 (2012) ,pp.10269–10277.
  2. R. Ahmed, O. Abdul, C. Marcel, dSPACE DSP-based rapid prototyping of fuzzy PID controls for high performance brushless servo drives, in: Proceedings of IEEE International Conference, USA, 2006, pp. 1360–1364.
  3. Truong DQ, Ahn KK, Soo KJ, Soo YH. Application of fuzzy-PID controller in hydraulic load simulator. In: Proceedings of the IEEE international conference on mechatronics and automation, 2007. pp. 3338–43.10.1109/ICMA.2007.4304098
  4. Dinh Quang Truong and Kyoung Kwan Ahn, “Force control for hydraulic load simulator using self-tuning grey predictor – fuzzy PID”, Mechatronics 19 (2009), pp .233–246.
  5. Zulfatman and M. F. Rahmat, “ Application of self-tuning Fuzzy PID Controller on Industrial Hydraulic Actuator using System Identification Approach”, International Journal on Smart Sensing and Intelligent Systems, Vol. 2, No. 2, pp 246-261,June 2009.10.21307/ijssis-2017-349
  6. Jin lisheng, Zhao Dingxuan and Huang haidong, “Study on Energy-saving Intelligent PID Expert Control System of Hydraulic Excavators”, Natural Science Journal of Jilin University of Technology, Vol.31, No.4, 2001, pp.12-16.
  7. T. Radpukdee and P. Jirawattana, “Uncertainty learning and compensation: An application to pressure tracking of an electro-hydraulic proportional relief valve”, Control Engineering Practice 17 (2009), pp. 291–301.
  8. Shengdun Zhao, Ji Wang, Lihong Wang, Chunjian Hua and Yupeng He, “Iterative learning control of electro-hydraulic proportional feeding system in slotting machine for metal bar cropping”, International Journal of Machine Tools& Manufacture 45 (2005), pp.923–931.
  9. Huang X, Liting S. Simulation on a fuzzy-PID position controller on the CNC servo system. In: Proceedings of the IEEE 6th international conference on intelligent systems design and applications; 2006. p. 305–9.10.1109/ISDA.2006.237
  10. Ergin Kilic, Melik Dolen, Hakan Caliskan, Ahmet Bugra Koku and Tuna Balkan, “Pressure prediction on a variable-speed pump controlled hydraulic system using structured recurrent neural networks”, Control Engineering Practice 26 (2014),pp. 51–71.
  11. M. Hasebe, Y. Nagayama, “Reservoir operation using the neural network and fuzzy systems for dam control and operation support”, Advances in Engineering Software 33 (2002),pp.245-260.
  12. IshigarniH, FukudaT, ShibataT, etal, “Struture optimization of fuzzy Neural network by genetic algorithm”, Fuzzy Sets and Systems, Vol. 71, No. 3, 1995, pp. 257-264.10.1016/0165-0114(94)00283-D
  13. S.C.Mukhopadhyay, T.Ohji, M.Iwahara and S.Yamada, “Design, Analysis and Control of a New Repulsive Type Magnetic Bearing”, IEE proceeding on Electric Power Applications, vol. 146, no. 1, pp. 33-40, January 1999.10.1049/ip-epa:19990203
  14. K.K. Ahn, D.Q. Truong, “Online tuning fuzzy PID controller using robust extended Kalman filter”, Journal of Process Control 19 (2009), pp. 1011–1023.
  15. D. Josephine Selvarani Ruth, S Sunjai Nakshatharan and K Dhanalakshmi, “Auto- Adaptive Control of a One-Joint Arm Direct Driven by Antagonistic Shape Memory alloy”, International Journal on Smart Sensing and Intelligent Systems, Vol. 6, No. 3, pp833-849, JUNE 2013.10.21307/ijssis-2017-568
  16. Guofu Tian, Shuhui Sun and Zhongwei Ren, “Application of Neural-Fuzzy System in Twin-Turbo Hydraulic Torque Converter’s Performance Testing”, Procedia Engineering 15 (2011),pp. 1282 – 1287.
  17. Y.M. Zhao, W.F. Xie, X.W. Tu, “Performance-based parameter tuning method of model- driven PID control systems”, ISA Transactions 51 (2012),pp. 393–399.
  18. Leszek Kasprzyczak, Ewald Macha, “Selection of settings of the PID controller by automatic tuning at the control system of the hydraulic fatigue stand”, Mechanical Systems and Signal Processing 22 (2008),pp. 1274–1288.
  19. G. Sen Gupta, S.C. Mukhopadhyay, S. Demidenko and C.H. Messom, “Master-slave Control of a Teleoperated Anthropomorphic Robotic Arm with Gripping Force Sensing”, IEEE Transactions on Instrumentation and Measurement, Vol. 55, No. 6, pp. 2136-2145, December 2006.
  20. Yang Geng, “ Motor and motion control systems”, Tsinghua University Press, 2006.
  21. S.C.Mukhopadhyay, T.Ohji, M.Iwahara and S.Yamada, “Modeling and Control of a New Horizontal Shaft Hybrid Type Magnetic Bearing”, IEEE Transactions on Industrial Electronics, Vol. 47, No. 1, pp. 100-108, February 2000.10.1109/41.824131
  22. Xu lina , “Neural Network Control” , Electronic Industry Press, 2009.
  23. Wang Ling, “Intelligent optimization and its application”, Tsinghua University Press, 2001.
Language: English
Page range: 1406 - 1423
Submitted on: Feb 21, 2015
Accepted on: May 5, 2015
Published on: Jun 1, 2015
Published by: Professor Subhas Chandra Mukhopadhyay
In partnership with: Paradigm Publishing Services
Publication frequency: 1 times per year

© 2015 Ma Yu, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.