Robust intelligent modeling for giant magnetostrictive actuators with rate-dependent hysteresis

Purpose – This paper proposes a robust modeling method of a giant magnetostrictive actuator which has a rate‐dependent nonlinear property.Design/methodology/approach – It is known in statistics that the Least Wilcoxon learning method developed using Wilcoxon norm is robust against outliers. Thus, it is used in the paper to determine the consequence parameters of the fuzzy rules to reduce the sensitiveness to the outliers in the input‐output data. The proposed method partitions the input space adaptively according to the distribution of samples and the partition is irrelative to the dimension of the input data set.Findings – The proposed modeling method can effectively construct a unique dynamic model that describes the rate‐dependent hysteresis in a given frequency range with respect to different single‐frequency and multi‐frequency input signals no matter whether there exist outliers in the training set or not. Simulation results demonstrate that the proposed method is effective and insensitive against t...

[1]  D. Jiles,et al.  Ferromagnetic hysteresis , 1983 .

[2]  Alison B. Flatau,et al.  Modeling of a Terfenol-D ultrasonic transducer , 2000, Smart Structures.

[3]  Klaus Kuhnen,et al.  Modeling, Identification and Compensation of Complex Hysteretic Nonlinearities: A Modified Prandtl - Ishlinskii Approach , 2003, Eur. J. Control.

[4]  Li Chuntao,et al.  A neural networks model for hysteresis nonlinearity , 2004 .

[5]  J. Mao,et al.  Fuzzy-tree model and its applications to complex system modeling , 1999 .

[6]  Chuen-Tsai Sun,et al.  Constructing hysteretic memory in neural networks , 2000, IEEE Trans. Syst. Man Cybern. Part B.

[7]  Dennis S. Bernstein,et al.  Piecewise Linear Identification for the Rate-Independent and Rate-Dependent Duhem Hysteresis Models , 2007, IEEE Transactions on Automatic Control.

[8]  Huibin Xu,et al.  Giant magnetostrictive actuators for active vibration control , 2004 .

[9]  Cheng-Han Tsai,et al.  Nonlinear Function Approximation Based on Least Wilcoxon Takagi-Sugeno Fuzzy Model , 2008, 2008 Eighth International Conference on Intelligent Systems Design and Applications.

[10]  Ganapati Panda,et al.  Robust identification of nonlinear complex systems using low complexity ANN and particle swarm optimization technique , 2011, Expert Syst. Appl..

[11]  Shuying Cao,et al.  Modeling of magnetomechanical effect behaviors in a giant magnetostrictive device under compressive stress , 2008 .

[12]  Jiangang Zhang,et al.  Adaptive-tree-structure-based fuzzy inference system , 2005, IEEE Trans. Fuzzy Syst..

[13]  Krzysztof Chwastek,et al.  Identification of a hysteresis model parameters with genetic algorithms , 2006, Math. Comput. Simul..

[14]  Yih-Lon Lin,et al.  Preliminary Study on Wilcoxon Learning Machines , 2008, IEEE Transactions on Neural Networks.

[15]  Amr A. Adly,et al.  Using neural networks in the identification of Preisach-type hysteresis models , 1998 .

[16]  Leon O. Chua,et al.  A Generalized Hysteresis Model , 1972 .

[17]  Isaak D. Mayergoyz,et al.  Dynamic Preisach models of hysteresis , 1988 .

[18]  JianQin Mao,et al.  Intelligent modeling and control for nonlinear systems with rate-dependent hysteresis , 2009, Science in China Series F: Information Sciences.

[19]  Bernard Mulgrew,et al.  Robust identification and prediction using Wilcoxon norm and particle swarm optimization , 2009, 2009 17th European Signal Processing Conference.

[20]  Nelson Sadowski,et al.  Minor loops modelling with a modified Jiles–Atherton model and comparison with the Preisach model , 2008 .

[21]  Ding Hai,et al.  Indirect Adaptive Fuzzy Control Based on Fuzzy Tree Model , 2008 .