Open AccessParticle filtering‐based recursive identification for controlled auto‐regressive systems with quantised output
Author(s) -
Ding Jie,
Chen Jiazhong,
Lin Jinxing,
Jiang Guoping
Publication year - 2019
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2019.0028
Subject(s) - convergence (economics) , algorithm , control theory (sociology) , set (abstract data type) , computer science , probability density function , identification (biology) , function (biology) , sampling (signal processing) , mathematics , filter (signal processing) , artificial intelligence , control (management) , statistics , botany , evolutionary biology , economics , computer vision , biology , programming language , economic growth
Recursive prediction error method is one of the main tools for analysis of controlled auto‐regressive systems with quantised output. In this study, a recursive identification algorithm is proposed based on the auxiliary model principle by modifying the standard stochastic gradient algorithm. To improve the convergence performance of the algorithm, a particle filtering technique, which approximates the posterior probability density function with a weighted set of discrete random sampling points is utilised to correct the linear output estimates. It can exclude those invalid particles according to their corresponding weights. The performance of the particle filtering technique‐based algorithm is much better than that of the auxiliary model‐based one. Finally, results are verified by examples from simulation and engineering.