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

1Otto Cerman and Petr Hušek, “Adaptive fuzzy sliding mode control for electro-hydraulic servo mechanism”, Expert Systems with Applications 39 (2012) ,pp.10269–10277. Search in Google Scholar

2R. 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. Search in Google Scholar

3Truong 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 Search in Google Scholar

4Dinh 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. Search in Google Scholar

5Zulfatman 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 Search in Google Scholar

6Jin 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. Search in Google Scholar

7T. 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. Search in Google Scholar

8Shengdun 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. Search in Google Scholar

9Huang 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 Search in Google Scholar

10Ergin 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. Search in Google Scholar

11M. 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. Search in Google Scholar

12IshigarniH, 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 Search in Google Scholar

13S.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 Search in Google Scholar

14K.K. Ahn, D.Q. Truong, “Online tuning fuzzy PID controller using robust extended Kalman filter”, Journal of Process Control 19 (2009), pp. 1011–1023. Search in Google Scholar

15D. 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 Search in Google Scholar

16Guofu 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. Search in Google Scholar

17Y.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. Search in Google Scholar

18Leszek 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. Search in Google Scholar

19G. 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.Search in Google Scholar

20Yang Geng, “ Motor and motion control systems”, Tsinghua University Press, 2006. Search in Google Scholar

21S.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 Search in Google Scholar

22Xu lina , “Neural Network Control” , Electronic Industry Press, 2009.Search in Google Scholar

23Wang Ling, “Intelligent optimization and its application”, Tsinghua University Press, 2001.Search in Google Scholar