A neural networks based model for rate-dependent hysteresis for piezoceramic actuators

摘要

A method for the identification of the rate-dependent hysteresis in piezoceramic actuators is proposed. In this approach, both a so-called generalized gradient of the output with respect to the input of the hysteresis and the derivative of the input that represents the frequency change of the input are introduced into the input space. Then an expanded input space is established. Thus, the multi-valued mapping of the rate-dependent hysteresis can be transformed into a one-to-one mapping based on the expanded of the input space. In this case, the neural network method can be applied to the modeling of the rate-dependent hysteresis. Finally, the experimental results are presented to illustrate the performance of the proposed approach. (c) 2008 Elsevier B.V. All rights reserved.