Nonlinearity Compensation Based on Identified NARX Polynomials Models

TL;DR

This paper presents a method for nonlinear system compensation using identified NARX polynomial models, rewriting them as algebraic polynomials to select roots that linearize the system effectively in static, dynamic, and hysteretic cases.

Contribution

It introduces a novel polynomial root-based approach for nonlinear compensation using NARX models, applicable to various system types and validated through numerical and experimental results.

Findings

Method effectively linearizes systems with simple polynomial models

Experimental results outperform other nonlinear compensation approaches

Achieves near-linear behavior with models having no more than five terms

Abstract

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.