Construction of Expanded Input Space for Modeling Hysteretic Systems

Yonghong Tan, Xinlong Zhao
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引用次数: 1

Abstract

A neural model for hysteresis based on expanded input space is proposed in this article. In this method, the behavior of hysteresis is considered as a dynamic system that can be described by a nonlinear state space equation containing hysteretic state. In order to transfer the multi-valued mapping of hysteresis into a one-to-one mapping, an expanded input space involving the original input variable and a so-called Duhem operator is constructed. Thus, the neural networks can be employed to approximate the relation between the hysteretic state and the output of the system that is also the output of hysteresis. The proposed model has a simple architecture that can be easily implemented for on-line adaptation for the model in case of the unexpected change of operating environment. Furthermore, the dynamic performance of the model is improved because of the existence of Duhem operator. Finally, the method is used to the modeling of hysteresis in a piezoelectric actuator.
迟滞系统建模扩展输入空间的构造
提出了一种基于扩展输入空间的迟滞神经网络模型。在该方法中,迟滞行为被视为一个动态系统,可以用包含迟滞状态的非线性状态空间方程来描述。为了将迟滞的多值映射转换为一对一映射,构造了一个包含原始输入变量和所谓Duhem算子的扩展输入空间。因此,可以使用神经网络来近似系统的滞后状态与输出之间的关系,系统的输出也是滞后的输出。该模型具有简单的体系结构,易于实现,在操作环境发生意外变化时对模型进行在线调整。此外,由于Duhem算子的存在,模型的动态性能得到了改善。最后,将该方法应用于压电作动器的滞回建模。
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