Research on Modeling and Parameter Identification of Electro-Hydraulic Servo Loading System

Abstract:

In order to improve the mathematical models accuracy of the electro-hydraulic servo loading system from the high-speed motorized spindle reliability test bench. This paper establishes the mathematical model based on the dynamic characteristics of the test bench, then establishes discrete mathematical model of the system based on the Z-transform, and finally uses particle swarm optimization (PSO) algorithm to identify the parameters of the discrete model. Additionally, the least square method is applied to identify the parameters of the model for measuring the PSO algorithm parameter identification capability in our paper. The experimental results show that the mathematical model, identified by the PSO algorithm, can simulate the loading process very well under the strong interference signals, and the result is better than that gotten by the least square method, which proves that the PSO algorithm has high identification accuracy and better capability in parameters identification .

• Periodical: Advanced Materials Research (Volumes 889-890)
• Pages: 970-977
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.889-890.970
• Online since: February 2014
• Authors: Song Wang, Chuan Gui Yang, Fei Chen*, Zhao Jun Yang, Zhuang Tan, Guang Zhu
• Keywords: Electro-Hydraulic Servoloading System, Mathematical Modeling, Parameter Identification, PSO Algorithm, Reliability

References

[1] Zhaojun Yang, Chuanhai Chen, Fei Chen, Guofa Li. Progress in the Research of Reliability Technology of Machine Tools. Journal of Mechanical Engineering, Vol. 6 (2013), pp.89-98.

[2] Jiwei Wang, Zhenshun Wu. Control Engineering Fundamentals. Beijing: Higher Education Press, pp.10-71, (2011).

[3] Guangyu Wu. System identification and adaptive. Harbin: Harbin Institute of Technology Press (1987).

[4] Shiyong Li. Fuzzy contro Neural networks and Intelligent Control. Harbin: Harbin Institute of Technology Press (1998).

[5] Zhengrui Yang, Keqin Hua, Yi Xu. Electro-hydraulic proportional and servo control. Beijing: Metallurgical Industry Press (2009).

[6] Yu, Ding Li. Diagnosing simulated faults for an industrial furnace based on bilinear model. Control Systems Technology, Vol. 8 (2000), pp.435-442.

DOI: 10.1109/87.845874

[7] Fuyi Li. Hydraulics and Hydraulic Servo System. Harbin: Harbin Engineering University Press (1992), pp.128-192.

[8] Kennedy J, Eberhart R C. Particle swarm optimization. International Conference on Neural Networks. Perth Australia (1995).

[9] Chuangui Yang, Zhaojun Yang, Fei Chen. et al. The optimization of the PID controller using the least squares method and the improved lbest PSO algorithm. Applied Mechanics and Materials, Vol. 397-400 (2013), pp.1296-1303.

DOI: 10.4028/www.scientific.net/amm.397-400.1296

[10] Xiaoping Xu, Fucai Qian, Ding Liu, Feng Wang. Method of System Identification Based on PSO Algorithm. Journal of System Simulation, Vol. 7 (2008), pp.3525-3528.

[11] Dawei Zhou, Xiang Gao, Guohai Liu. Randomization in particle swarm optimization for global search ability. Expert Systems with Applications, Vol. 6 (2011), pp.15356-15364.

DOI: 10.1016/j.eswa.2011.06.029

[12] Wei Zhang, Hua Li, Qinghua Zhao, Huakui Wang. Guidelines for Parameter Selection in Particle Swarm Optimization According to Control Theory. Fifth International Conference on Natural Computation (2009).

DOI: 10.1109/icnc.2009.270

[13] Zhaojun Yang, Chuangui Yang, Fei Chen. et al. The optimization of the electro-hydraulic servo loading based on the least squares algorithm and the SVDUKF algorithm. Journal of Jilin University, Vol. 2 (2014), in press.