使用NARX模型识别和补偿滞后性
Identification and nonlinearity compensation of hysteresis using NARX models
AI总结：
本文提出使用NARX模型识别并补偿滞后性，通过灰盒和数据驱动策略提升补偿性能。
摘要：
本文解决两个问题：使用非线性多项式自回归模型（NARX）对动态系统中的滞后非线性进行识别和补偿。首先，基于灰盒识别技术，提出了一些关于NARX模型结构和参数的约束，以确保识别出的模型表现出滞后性的一个关键特征。此外，还开发了一个更一般的框架，解释了此类模型中滞后性如何发生。第二，提出了两种设计滞后补偿器的策略。一种策略通过简单的代数操作获得补偿法则。研究发现，基于灰盒模型的补偿器在性能上优于使用黑盒技术识别的模型。另一种策略则是直接从数据中识别补偿法则。数值和实验结果均被展示以说明所提出方法的有效性。
英文摘要：
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.
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