The Research on Intelligent Control Method for EHP System

Abstract:

Optimized machine learning algorithm is applied to control modeling of high-speed electric-hydraulic proportional system of high nonlinear in this paper, a identification model of high-speed electric-hydraulic proportional system is built based on support vector machines, fusion intelligent method of dynamic self-adaptive internal model control and predictive control is realized for high-speed electric-hydraulic proportional control system. Internal model and inverse controller model are online adjusted together. Simulation shows the satisfactory tracking effect by intelligent technology of dynamic self-adaptive internal control and predictive control based on the support vector machine, the dynamic characteristic is greatly improved by the intelligent control strategy for high-speed electric-hydraulic proportional control system, good tracking and control effect is reached in condition of high frequency response. It provides a new intelligent control method for high-speed electric-hydraulic proportional system.

• Periodical: Advanced Materials Research (Volume 580)
• Pages: 12-15
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.580.12
• Online since: October 2012
• Authors: Yi Wan, Qi Bo Cai, Huan Wang
• Keywords: EHP System, Intelligent Method, Predictive Control, Self-Adaptive Internal Control, Support Vector Machine (SVM)

References

[1] Hong Yu, Zheng-jin Feng and Xu-yong Wang: Journal of Zhejiang University - Science A Vol. 5(2009), pp.1413-1417.

[2] Miao-Lei Zhou, Yan-tao Tian, Wei Gao and Zhi-gang Yang: Journal of Central South University of Technology Vol. 14(2007), pp.832-837.

[3] Wei-Jie Mao and Yuan-Yuan Zhang: Lecture Notes in Computer Science, Intelligent Robotics and Applications Vol. 5314(2008), pp.401-409.

[4] Jun Gu, James Taylor and Derek Seward: Journal of Control Theory and Applications Vol. 5(2009), pp.391-396.

[5] Qing-hua He, Peng Hao and Da-qing Zhang: Journal of Central South University of Technology, Vol. 15(2008), pp.382-386.

[6] Kasprzyczak, Leszek; Macha, Ewald: Mechanical Systems and Signal Processing, Vol. 22(2009), pp.1274-1288.

[7] ShengWei Mei, XiaoYang Gui, Chen Shen and Qiang Lu: Science in China Series E: Technological Sciences, Vol. 50(2007), pp.618-635.

[8] Truong, Dinh Quang: 2008 International Conference on Control, Automation and Systems, ICCAS( 2008), pp.2622-2627.

[9] Yi. He: The Identification Modeling and Control of Nonlinear System Based on SVM (Tianjin University, Tianjin2006).

[10] Zhong Liang JI: Control Method on VCM Production Process Based on SVM (Dalian University of Technology, Dalin 2005).