Decomposition-based recursive least squares identification methods for multivariate pseudo-linear systems using the multi-innovation

Ping Ma,Feng Ding,Quanmin Zhu

doi:10.1080/00207721.2018.1433247

Abstract

ABSTRACTThis paper studies the parameter estimation algorithms of multivariate pseudo-linear autoregressive systems. A decomposition-based recursive generalised least squares algorithm is deduced for estimating the system parameters by decomposing the multivariate pseudo-linear autoregressive system into two subsystems. In order to further improve the parameter accuracy, a decomposition based multi-innovation recursive generalised least squares algorithm is developed by means of the multi-innovation theory. The simulation results confirm that these two algorithms are effective.

Highlights

System modeling and identification have extensive applications in industrial control [1, 2, 3]
12], the innovation is the useful information that can improve the accuracy of the parameter estimation or the state estimation [13, 14]
The basic idea is that deriving the mathematical model of multivariate systems firstly, and using the negative gradient search principle, combing with the multiinnovation identification theory or data filtering technique, obtain corresponding algorithms, to overcome the influence of colored noise

Summary

Introduction

System modeling and identification have extensive applications in industrial control [1, 2, 3]. 12], the innovation is the useful information that can improve the accuracy of the parameter estimation or the state estimation [13, 14] In this aspect, Mao and Ding presented a multi-innovation stochastic gradient algorithm for Hammerstein nonlinear systems [15]; Jin et al proposed a multi-innovation least squares identification algorithm for multivariable output-error systems with scarce measurements [16]; Wang and Zhu presented a multi-innovation stochastic gradient algorithm for a class of linear-inparameter systems [17]. Shen and Ding presented a hierarchical multi-innovation extended stochastic gradient algorithm for input nonlinear multivariable output-error moving average (OEMA) systems by the key-term separation principle [18]. The basic idea is that deriving the mathematical model of multivariate systems firstly, and using the negative gradient search principle, combing with the multiinnovation identification theory or data filtering technique, obtain corresponding algorithms, to overcome the influence of colored noise.

System description and identification model

The M-GSG algorithm

The M-MI-GSG algorithm

The F-M-GSG algorithm

Examples

Conclusions