Abstract
In this paper, in order to solve the problem that the controlled object is complex or nonlinear in many times, a weighted recursive least square scheme is used to estimate the parameters of the nonlinear Wiener-Hammerstein systems with the dead zone. First of all, we make the unknown dead zone linear by the switching operator and the intermediate function, and construct the parameter identification model of Wiener-Hammerstein system. Secondly, we obtain the parametric regression model of the concerned systems for parameter identification using the key-term separation principle. Thirdly, we build a fictitious auxiliary model to replace the immeasurable intermediate variable. And then, we estimate the parameters of the obtained model with the fictitious auxiliary model using the weighted recursive least square. Finally, we verify the feasibility of the algorithm by MATLAB simulation.
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References
Xiao, S., Li, Y.: Optimal design, fabrication, and control of an XY micropositioning stage driven by electromagnetic actuators. IEEE Trans. Industr. Electron. 60(10), 4613–4626 (2013)
Shin, J., Kwak, D.J., Lee, Y.: Adaptive path-following control for an unmanned surface vessel using an identified dynamic model. IEEE/ASME Trans. Mechatron. 22(3), 1143–1153 (2017)
Lee, K.S., Park, B.H., il Lee, H., et al.: Phase frequency detectors for fast frequency acquisition in zero-dead-zone CPPLLs for mobile communication systems. In: ESSCIRC 2004–29th European Solid-State Circuits Conference (IEEE Cat. No.03EX705), pp. 525–528 (2003)
Sun, Y.-J., Hsieh, Y.-C., Hsieh, J.-G.: A unifying control scheme of linear circuits with saturating or dead-zone actuator. J. Franklin Inst. 334(3), 427–430 (1997)
Ding, J., Cao, Z., Chen, J., et al.: Weighted parameter estimation for Hammerstein nonlinear ARX systems. Circ. Syst. Sig. Process. 39(4), 2178–2192 (2020)
Liu, Y., Ding, F., Shi, Y.: An efficient hierarchical identification method for general dual-rate sampled-data systems. Automatica 50(3), 962–970 (2014)
Ding, J., Ding, F., Liu, X.P., et al.: Hierarchical least squares identification for linear SISO systems with dual-rate sampled-data. IEEE Trans. Autom. Control 56(11), 2677–2683 (2011)
Vörös, J.: Parameter identification of discontinuous hammerstein systems. Automatica 33(6), 1141–1146 (1997)
Vörös, J.: Recursive identification of Hammerstein systems with discontinuous nonlinearities containing dead-zones. IEEE Trans. Autom. Control 48(12), 2203–2206 (2003)
Vörös, J.: Modeling and identification of systems with backlash. Automatica 46(2), 369–374 (2010)
Vörös, J.: Identification of nonlinear dynamic systems using extended Hammerstein and wiener models. Control Theor. Adv. Technol. 10(4), 1203–1212 (1995)
Ding, F., Xu, L., Meng, D., et al.: Gradient estimation algorithms for the parameter identification of bilinear systems using the auxiliary model. J. Comput. Appl. Math. 369, 112575 (2020)
Ji, Y., Zhang, C., Kang, Z., et al.: Parameter estimation for block-oriented nonlinear systems using the key term separation[J]. International Journal of Robust and Nonlinear Control 30(9), 3727–3752 (2020)
Ding, F.: Multivariable system identification. Tsinghua University, Beijing (1990)
Ding, F., Chen, T.: Combined parameter and output estimation of dual-rate systems using an auxiliary model. Automatica 40(10), 1739–1748 (2004)
Ding, F., Chen, T.: Parameter estimation of dual-rate stochastic systems by using an output error method. IEEE Trans. Autom. Control 50(9), 1436–1441 (2005)
Acknowledgments
Our work is supported by Grant No.61973036, 61433003, from National Natural Science Foundation (NNSF) of China.
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Lan, R., Ren, X., Li, L. (2022). Weighted Recursive Least Square for Parameter Identification of Nonlinear Wiener–Hammerstein Systems. In: Jia, Y., Zhang, W., Fu, Y., Yu, Z., Zheng, S. (eds) Proceedings of 2021 Chinese Intelligent Systems Conference. Lecture Notes in Electrical Engineering, vol 803. Springer, Singapore. https://doi.org/10.1007/978-981-16-6328-4_47
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DOI: https://doi.org/10.1007/978-981-16-6328-4_47
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