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

Abstract

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

CLC Number:

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.

References

[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.

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

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