Parameter Identification for Dynamic Damping System Based on Genetic Algorithm

Parameter Identification for Dynamic Damping System Based on Genetic Algorithm

Yulu Zeng

Department of Mechanical & Electrical Engineering, Nanchang Institute of Technology, Nanchang 330099, China

Corresponding Author Email: 
zhuzhifang1984@163.com
Page: 
101-113
|
DOI: 
https://doi.org/10.18280/ama_c.720201
Received: 
5 May 2017
|
Accepted: 
13 June 2017
|
Published: 
30 June 2017
| Citation

OPEN ACCESS

Abstract: 

In order to obtain the damping coefficient and other parameters that influence the dynamic features of the valve, this paper employs the “LuGre friction model” to describe the precise dynamic and the static features, and presents a new one-step identification method for the parameter identification of LuGre friction model through the optimization by genetic algorithm. With the properly selected objective function, four static parameters and two dynamic parameters can be obtained simultaneously by the MATLAB programming language. The proposed method is proved effective through the verification of the identified parameters.

Keywords: 

Damping, Friction model, Genetic algorithm, Parameter identification.

1. Introduction
2. System Structure and Feature Implementation
3. Parameter Identification
4. Simulation Results and Analysis
Conclusion
Acknowledgements

This paper is finacially supported by the Youth Science Fund of Jiangxi Province office of education(GJJ161124) and Foundation of Jiangxi Province Key Laboratory of Precision Drive & Control (PLPDC-KFKT-201619).

  References

1. W. Yu, J.G. Ma, J.Y. Li, Friction parameter identification and friction compensation for precision servo turning table, 2011, Optics and Precision Engineering, vol. 19, no. 11, pp. 2737-2743.

2. C.C. De Wit, H. Olsson, K.J. Astrom, P. Lischinsky, Dynamic friction models and control design, 1999, the 14th World Congress of IFAC, Beijing, pp. 487-491.

3. E.J. Berger, Friction modeling for dynamic system simulation, 2002, ASME App Mech Rev, vol. 55, no. 6, pp. 535-577.

4. J.P. Han, Y.L. Sun, Y.Y. Wang, Parameter identification of LuGre tire model for the simplified motion dynamics of a quarter-vehicle model based on ant colony algorithm, 2007, IEEE Transactions on Automation and Logistics, vol. 8, pp. 18-21.

5. W.J. Zhang, Parameter identification of LuGre friction model in servo system based on improved particle swarm optimization algorithm, 2007, Proceedings of the 26th Chinese Control Conference, Beijing, China, vol. 7, pp. 135-139.

6. R.M. Hirschorn, G. Miller, Control of nonlinear systems with friction, 1999, IEEE Transactions on Control Systems Technology, vol. 7, no.5, pp. 588-595.

7. R.H.A. Hensen, M.J.G.V.D. Molengraft, M. Steinbuch, Frequency domain identification of dynamic friction model parameters, 2002, IEEE Transactions on Control systems Technology, vol. 10, no.2, pp. 191-196.

8. D.P. Liu, Research on parameter identification of friction model for servo systems based on genetic Algorithms, 2005, IEEE Transactions on the Fourth International Conference on Machine Learning and Cybernetics, vol. 8, no. 2, pp. 1116-1120.

9. S.W. Yang, M.Q. Zheng, Simulation of nonlinear friction with modeling methodology, 2002, System Simulation Science, vol. 14, no. 10, pp. 1365-1368.

10. M.S. Madi, K. Khayati, P. Bigras, Parameter estimation for the LuGre friction model using interval analysis and set inversion, 2004, IEEE Transactions on Systems, Man and Cybernetics, pp.428-433.

11. J. Swevers, F. Al-Bender, C.G. Ganseman, T. Projogo, An integrated friction model structure with improved presiding behavior for accurate friction compensation, 2000, IEEE Transactions on Automatic Control, vol. 45, no. 4, pp. 675-686.

12. C.L. Karr, Design of an adaptive fuzzy logic controller using genetic algorithm, 1991, Proceedings of fourth international conference on genetic algorithms, Los A1ts, CA. Morgan Kaufmann Publisher, pp. 450-457.

13. S. Hashimoto, K. Ohishi, T. Ishikawa, K. Kosaka, H. Kubota, T. Ohmi, On-line identification method of static friction for ultra-precision positioning, 2004, SICE, Annual Conference, Sapporo, Japan, vol. 4, no. 8, pp. 137-142.

14. U. Parlitz, A. Hornstein, D. Engster, F. Al-Bender, V. Lampaert, T. Tjahjowidodo, S.D, Fassois, D. Rizos, C.X. Wong, K. Worden, G. Manson, Identification of pre-sliding friction dynamics, 2004, American Institute of Physics, vol. 14, no. 2, pp. 420-430.

15. S. Chekroun, B. Abdelhadi, A. Benoudjit, Design optimization of induction motor using hybrid genetic algorithm "a critical analyze", 2016, Advances in Modelling and Analysis C, vol. 71, no. 1, pp. 1-23.

16. P.A.S. Dayal, G.S.N. Raju, S. Mishra, Pattern synthesis using accelerated particle swarm optimization, 2016, Modelling, Measurement and Control A, vol. 89, No. 1, pp. 58-76.

17. Z.X. Li, C. Li, Z. Jue, Multi-objective particle swarm optimization algorithm for recommender system, 2016, Advances in Modelling and Analysis B, vol. 59, no. 1, pp. 189-200.