基于ODE参数辨识的液压伺服系统灰箱建模

doi:10.7527/S1000-6893.2013.0022

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基于ODE参数辨识的液压伺服系统灰箱建模

赵盼^1,2, 王少萍^1,2

1. 北京航空航天大学自动化科学与电气工程学院, 北京 100191;
2. 北京航空航天大学飞行器控制一体化技术国防科技重点实验室, 北京 100191

收稿日期:2012-03-05 修回日期:2012-07-17 出版日期:2013-01-25 发布日期:2013-01-19
通讯作者: 王少萍,Tel.:010-82338933,E-mail:shaopingwang@vip.sina.com E-mail:shaopingwang@vip.sina.com
作者简介:赵盼,男,硕士研究生。主要研究方向:液压伺服系统的建模、辨识与控制。,E-mail:zhaopan0558@yahoo.com.cn;王少萍,女,博士,教授,博士生导师。主要研究方向:机电系统控制与仿真,机电系统故障诊断、容错与可靠性。Tel:010-82338933,E-mail:shaopingwang@vip.sina.com
基金资助:
国家自然科学基金(51175014)

Grey-box Modeling of Hydraulic Servo Systems Based on ODE Parameter Identification

ZHAO Pan^1,2, WANG Shaoping^1,2

1. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China;
2. Science and Technology on Aircraft Control Laboratory, Beihang University, Beijing 100191, China

Received:2012-03-05 Revised:2012-07-17 Online:2013-01-25 Published:2013-01-19
Supported by:
National Natural Science Foundation of China (51175014)

摘要/Abstract

摘要：

针对液压伺服系统常规"白箱"建模由于参数无法精确获得导致所得模型精度不高及"黑箱"建模所得模型内部结构未知的问题,本文提出基于ODE参数辨识的液压伺服系统"灰箱"建模。首先,建立了工程上实用的系统状态空间模型, 根据系统特征确定了待辨识参数,将模型辨识问题转化为常微分方程(ODE)参数辨识问题;然后,采用正弦扫频信号作为激励信号和基于带边界约束的信赖域优化算法的初值问题方法进行参数辨识;为了和ODE参数辨识结果进行对比,本文同时采用系统的频率响应数据和最小二乘法辨识得到系统的"黑箱"传递函数模型;最后,通过大量实验验证了辨识模型的精确度。实验结果表明,本文提出的基于信赖域算法的液压伺服系统模型辨识方法可以有效处理参数的边界问题,使辨识模型既具有实际的物理意义,又与实际系统高度符合。

关键词: 液压伺服系统, 灰箱建模, 常微分方程, 参数辨识, 信赖域, 边界约束

Abstract:

In view of the fact that white-box modeling cannot supply an accurate model due to the unavailability of accurate parameters and that black-box model structure is unknown, this paper tries to implement a grey-box modeling of hydraulic servo systems utilizing ODE parameter identification. A practical state space model of the system is first constructed, and parameters that need to be estimated are then defined. Parameter identification is carried out utilizing sine sweep data and the initial value problem approach (IVPA) based on trust region method (TRM) which is able to handle the bound constraints on the parameters. Black-box transfer function models are also acquired utilizing frequency response data to compare with the results of ODE parameter identification. Finally, extensive experiments are performed to testify the quality of the identified models. Experiment results show that the proposed method for identifying hydraulic servo systems based on TRM is able to handle parameter bounds effectively, which results in a model that is not only physically reasonable, but also corresponds highly with the practical system.

Key words: hydraulic servo system, grey-box modeling, ordinary differential equations, parameter identification, trust region, bound constraint

中图分类号:

赵盼, 王少萍. 基于ODE参数辨识的液压伺服系统灰箱建模[J]. 航空学报, 2013, 34(1): 187-196.

ZHAO Pan, WANG Shaoping. Grey-box Modeling of Hydraulic Servo Systems Based on ODE Parameter Identification[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2013, 34(1): 187-196.

参考文献

[1] Wang Z L. Hydraulic servo control. Beijing: Press of Beijing Institute of Aeronautics, 1987: 1. (in Chinese) 王占林. 液压伺服控制. 北京: 北京航空学院出版社, 1987:1.

[2] Wang Z L. Modern electro-hydraulic servo control. Beijing: Beihang University Press, 2005: 1-3. (in Chinese) 王占林. 近代电气液压伺服控制. 北京: 北京航空航天大学出版社, 2005: 1-3.

[3] Sjöberg J, Zhang Q, Ljung L, et al. Nonlinear black-box modeling in system identification: a unified overview. Automatica, 1995, 31(12): 1691-1724.

[4] Zhao P. Research on nonlinear model identification of hydraulic servo system. Beijing: School of Automation Science and Electrical Engineering, Beihang University, 2011. (in Chinese) 赵盼. 液压伺服系统非线性模型辨识方法研究. 北京: 北京航空航天大学自动化科学与电气工程学院, 2011.

[5] Wang X J, Shao J P, Jiang J H, et al. System identification and control of the electro-hydraulic servo system of a continuous rotary motor. Journal of Harbin Engineering University, 2011, 32(8): 1045-1051. (in Chinese) 王晓晶, 邵俊鹏, 姜继海, 等. 连续回转马达电液伺服系统辨识及控制. 哈尔滨工程大学学报, 2011, 32(8): 1045-1051.

[6] Lohmann T, Bock H G, Schloeder J P. Numerical methods for parameter estimation and optimal experiment design in chemical reaction systems. Industrial & Engineering Chemistry Research, 1992, 31(1): 54-57.

[7] Müller T G, Noykova N, Gyllenberg M, et al. Parameter identification in dynamical models of anaerobic waste water treatment. Mathematical Biosciences, 177-178: 147-160.

[8] Bock H G. Recent advances in parameter identification techniques for O.D.E. Numerical Treatment of Inverse Problems in Differential and Integral Equations: Proceedings of an Internation Workshop, 1983,2: 95-121.

[9] Ramsay J O, Hooker G, Campbell D, et al. Parameter estimation for differential equations: a generalized smoothing approach. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2007, 69(5): 741-796.

[10] Li Z, Osborne M R, Prvan T. Parameter estimation of ordinary differential equations. IMA Journal of Numerical Analysis, 2005, 25(2): 264-285.

[11] Ardenghi J I, Maciel M C, Verdiell A B. A trust-region-approach for solving a parameter estimation problem from the biotechnology area. Applied Numerical Mathematics, 2003, 47(3-4): 281-294.

[12] Coleman T F, Li Y. An interior trust region approach for nonlinear minimization subject to bounds. SIAM Journal on Optimization, 1996, 6(2): 418-445.

[13] Byrd R H, Schnabel R B, Shultz G A. Approximate solution of the trust region problem by minimization over two-dimensional subspaces. Mathematical Programming, 1988, 40(1): 247-263.

[14] Coleman T F, Li Y. A reflective Newton method for minimizing a quadratic function subject to bounds on some of the variables. SIAM Journal on Optimization, 1996, 6(4): 1040-1058.

[15] Coleman T F, Hempel C. Computing a trust region step for a penalty function. SIAM Journal on Scientific and Statistical Computing, 1990, 11(1): 180-201.

[16] Conn A R, Gould N I M, Toint P L. Global convergence of a class of trust region algorithms for optimization with simple bounds. SIAM Journal on Numerical Analysis, 1988, 25(2): 433-460.

[17] Fang C Z, Xiao D Y. Process identification. Beijing: Tsinghua University Press, 1988:100-110. (in Chinese) 方崇智, 萧德云. 过程辨识. 北京: 清华大学出版社, 1988:100-110.

E-mail：hkxb@buaa.edu.cn

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主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

基于ODE参数辨识的液压伺服系统灰箱建模

Grey-box Modeling of Hydraulic Servo Systems Based on ODE Parameter Identification

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