RBF neural networks hysteresis modelling for piezoceramic actuator using hybrid model

Xuanju Dang; Yonghong Tan

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RBF neural networks hysteresis modelling for piezoceramic actuator using hybrid model

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摘要

An radial basis function (RBF) neural networks rate-dependent hysteresis hybrid model for piezoceramic actuator is proposed. The piezoceramic actuator cannot be described by neural networks like the back propagation (BP) static neural networks because of its multi-valued hysteresis non-linearity. The proposed hybrid hysteresis model consists of hysteresis-like non-linearity in series with a dynamic RBF neural networks used for implementing non-linear transformations of the phase lag and non-linear magnitude. The hysteresis-like non-linearity model, which is composed of the previous output of piezoceramic actuator and input signal, differs from the hysteresis behaviour of piezoceramic actuator in only ways of their phase and magnitude, and it is used to describe the non-smooth behaviour of piezoceramic actuator. The results of both simulation and experiment show that the new modelling approach is very effective and of higher precision under a decayed input signal with the varying frequency.

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《Mechanical Systems & Signal Processing》 |2006年第1期|430-440|共11页
作者
Xuanju Dang; Yonghong Tan;
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正文语种英语
中图分类机械制造工艺;
关键词
Piezoceramic actuator; Hysteresis behaviour; Hybrid hysteresis model; Dynamic RBF neural networks;

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RBF neural networks hysteresis modelling for piezoceramic actuator using hybrid model

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