PARAMETRIC COMPLEXITY REDUCTION OF DISCRETE-TIME LINEAR SYSTEMS HAVING A SLOW INITIAL ONSET OR DELAY

Author:

Krifa Abdelkader1,Bouzrara Kais1

Affiliation:

1. University of Monastir

Abstract

This paper is concerned with an optimal expansion of linear discrete time systems on Meixner functions. Many orthogonal functions have been widely used to reduce the model parameter number such as Laguerre functions, Kautz functions and orthogonal basis functions. However, when the system has a slow initial onset or delay, Meixner functions, which have a slow start, are more suitable in terms of providing a more accurate approximation to the system. The optimal approximation of Meixner model is ensured once the pole characterizing the Meixner functions is set to its optimal value. In this paper, a new recursive representation of Meixner model is proposed. Further we propose, from input/output measurements, an iterative pole optimization algorithm of the Meixner pole functions. The method consists in applying the Newton-Raphson’s technique in which their elements are expressed analytically by using the derivative of the Meixner functions. Simulation results show the effectiveness of the proposed optimal modeling method.

Publisher

Vilnius Gediminas Technical University

Subject

Modelling and Simulation,Analysis

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Pole optimization of NARX-Laguerre model using Gradient method;2024 International Conference on Control, Automation and Diagnosis (ICCAD);2024-05-15

2. Robust predictive control based on the Meixner-like model;Journal of the Franklin Institute;2019-08

3. Analytical solution of optimal Meixner-like pole;Journal of Difference Equations and Applications;2019-01-02

4. New methods of Laguerre pole optimization for the ARX model expansion on Laguerre bases;ISA Transactions;2017-09

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