基于辨识的NARX多项式模型的非线性补偿
Nonlinearity Compensation Based on Identified NARX Polynomials Models
摘要：
本文讨论了用带外生输入的非线性多项式自回归模型（NARX）补偿动力系统中的非线性。的补偿方法是制定静态和动态的背景下，以及其适应滞后系统。在所有这些场景中，使用已识别的NARX模型。其核心思想是将模型重写为代数多项式，其根是潜在的补偿输入。提出了一个程序，以选择最适当的根，在情况下，有可能不止一个。数值和实验结果来说明该方法。在实验的情况下，该方法与其他方法进行了比较。结果表明，所提出的方法可以提供补偿输入信号，实际线性化的研究系统，使用简单的和有代表性的模型不超过五项。
英文摘要：
This paper deals with the compensation of nonlinearities in dynamical systems using nonlinear polynomial autoregressive models with exogenous inputs (NARX). The compensation approach is formulated for static and dynamical contexts, as well as its adaptation to hysteretic systems. In all of these scenarios, identified NARX models are used. The core idea is to rewrite the model as an algebraic polynomial whose roots are potential compensation inputs. A procedure is put forward to choose the most adequate root, in cases where more than one is possible. Both numerical and experimental results are presented to illustrate the method. In the experimental case the method is compared to other approaches. The results show that the proposed methodology can provide compensation input signals that practically linearize the studied systems using simple and representative models with no more than five terms.
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