中文摘要：

为辨识压电执行器中的速率相关迟滞特性,基于扩展输入空间法建立迟滞的神经网络动态模型。提出动态迟滞算子来描述速率相关迟滞的变化趋势和动态特性并基于该动态迟滞算子构建扩展输入空间。在这个扩展输入空间上,迟滞的多值映射能够转化成一一映射,同时可以利用动态迟滞算子来提取迟滞的速率相关性。用神经网络来逼近这个一一映射实现速率相关迟滞的动态建模。所提出的神经网络动态模型可以描述迟滞的速率相关性,结构简单,打破了常规迟滞模型基于算子加权叠加的建模框架,并且能够在线调整参数以适应不同条件下的迟滞建模。最后应用该方法对压电执行器中的迟滞特性进行动态建模,试验结果证明了这种建模方法的有效性。

英文摘要：

In order to identify the rate-dependent hysteresis in piezoelectric actuator,a neural dynamic model based on expanded input space method is built.In this method,a novel hysteretic operator is proposed to describe the change tendency and the dynamic property of rate-dependent hysteresis.Thus,an expanded input space is constructed with the introduction of such hysteretic operator.The multi-valued mapping of the rate-dependent hysteresis can be transformed into a one-to-one mapping in this expanded input space.Furthermore,the rate-dependent property of hysteresis can be extracted by the hysteretic operator.Then neural networks can be used to approximate the one-to-one mapping and the dynamic model is constructed for the rate-dependent hysteresis.The proposed neural model can describe the rate-dependent hysteresis and has a simple architecture which is totally different from the modeling framework of the hysteresis model based on the weighted superposition of operators.Moreover,it is convenient to adjust the parameters on-line to adapt to the variation of the operating environment.Finally,this method is applied to dynamic modeling of the hysteresis in piezoelectric actuator.The experimental results prove the validity of this modeling technique.