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Nonlinear modeling and identification of the electro-hydraulic control system of an excavator arm using BONL model

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Abstract

Electro-hydraulic control systems are nonlinear in nature and their mathematic models have unknown parameters. Existing research of modeling and identification of the electro-hydraulic control system is mainly based on theoretical state space model, and the parameters identification is hard due to its demand on internal states measurement. Moreover, there are also some hard-to-model nonlinearities in theoretical model, which needs to be overcome. Modeling and identification of the electro-hydraulic control system of an excavator arm based on block-oriented nonlinear(BONL) models is investigated. The nonlinear state space model of the system is built first, and field tests are carried out to reveal the nonlinear characteristics of the system. Based on the physic insight into the system, three BONL models are adopted to describe the highly nonlinear system. The Hammerstein model is composed of a two-segment polynomial nonlinearity followed by a linear dynamic subsystem. The Hammerstein-Wiener(H-W) model is represented by the Hammerstein model in cascade with another single polynomial nonlinearity. A novel Pseudo-Hammerstein-Wiener(P-H-W) model is developed by replacing the single polynomial of the H-W model by a non-smooth backlash function. The key term separation principle is applied to simplify the BONL models into linear-in-parameters structures. Then, a modified recursive least square algorithm(MRLSA) with iterative estimation of internal variables is developed to identify the all the parameters simultaneously. The identification results demonstrate that the BONL models with two-segment polynomial nonlinearities are able to capture the system behavior, and the P-H-W model has the best prediction accuracy. Comparison experiments show that the velocity prediction error of the P-H-W model is reduced by 14%, 30% and 75% to the H-W model, Hammerstein model, and extended auto-regressive (ARX) model, respectively. This research is helpful in controller design, system monitoring and diagnosis.

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Authors and Affiliations

  1. College of Field Engineering, PLA University of Science and Technology, Nanjing, 210007, China

    Jun Yan, Bo Li, Gang Guo, Yonghua Zeng & Meijun Zhang

Authors
  1. Jun Yan
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  2. Bo Li
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  3. Gang Guo
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  4. Yonghua Zeng
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  5. Meijun Zhang
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Corresponding author

Correspondence to Bo Li.

Additional information

This project is supported by National Natural Science Foundation of China(Grant No. 51175511)

YAN Jun, born in 1962, is currently a professor and a PhD candidate supervisor at College of Field Engineering, PLA University of Science and Technology, China. His main research interests include Mechatronics, Military Equipment management and maintenance.

LI Bo, born in 1985, is currently a PhD candidate at College of Field Engineering, PLA University of Science and Technology, China. He received his bachelor degree from Northeastern University, China, in 2008. His research direction is hydraulic control system and filed robotics. He has published over 20 papers.

GUO Gang, born in 1965, is currently an associate professor at College of Field Engineering, PLA University of Science and Technology, China. His research interests include mechachonics engineering and fault diagnosis.

ZENG Yonghua, born in 1977, is currently a lecturer at College of Field Engineering, PLA University of Science and Technology, China. His research interests include Military Equipment management and maintenance.

ZHANG Meijun, born in 1958, is currently an associate professor at College of Field Engineering, PLA University of Science and Technology, China. Her research interests include mechanical dynamic systems and fault diagnosis.

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Yan, J., Li, B., Guo, G. et al. Nonlinear modeling and identification of the electro-hydraulic control system of an excavator arm using BONL model. Chin. J. Mech. Eng. 26, 1212–1221 (2013). https://doi.org/10.3901/CJME.2013.06.1212

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  • DOI: https://doi.org/10.3901/CJME.2013.06.1212

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