论文标题
使用NARX模型对滞后的识别和非线性补偿
Identification and nonlinearity compensation of hysteresis using NARX models
论文作者
论文摘要
本文解决了两个问题：使用具有外源输入（NARX）的非线性多项式自回旋模型（NARX）的非线性多项式自回旋模型对滞后非线性的识别和补偿。首先，基于灰色框标识技术，提出了对NARX模型的结构和参数的一些约束，以确保确定的模型显示磁滞的键值。此外，开发了一个更一般的框架来解释这种模型中如何发生滞后。其次，提出了两种设计滞后补偿器的策略。在一个策略中，通过对确定模型进行的简单代数操作获得薪酬定律。已经发现，基于灰色框模型的补偿器优于使用黑框技术识别的模型的情况。在第二个策略中，从数据中直接确定薪酬法。提出了数值和实验结果，以说明所提出的程序的效率。
This paper deals with two problems: the identification and compensation of hysteresis nonlinearity in dynamical systems using nonlinear polynomial autoregressive models with exogenous inputs (NARX). First, based on gray-box identification techniques, some constraints on the structure and parameters of NARX models are proposed to ensure that the identified models display a key-feature of hysteresis. In addition, a more general framework is developed to explain how hysteresis occurs in such models. Second, two strategies to design hysteresis compensators are presented. In one strategy the compensation law is obtained through simple algebraic manipulations performed on the identified models. It has been found that the compensators based on gray-box models outperform the cases with models identified using black-box techniques. In the second strategy, the compensation law is directly identified from the data. Both numerical and experimental results are presented to illustrate the efficiency of the proposed procedures.
