Robust Gradient Estimation Algorithm for a Stochastic System with Colored Noise

This paper studies the parameter estimation algorithms of a finite impulse response system with colored noise. To suppress the negative effects of the colored noises, a novel gradient-based algorithm is developed by means of the cost function of the continuous mixed p -norm (CMPN). It combines the p...

Full description

Saved in:
Bibliographic Details
Published in:International journal of control, automation, and systems automation, and systems, 2023-02, Vol.21 (2), p.553-562
Main Authors: Liu, Wentao, Xiong, Weili
Format: Article
Language:English
Subjects:
Algorithms
Continuity (mathematics)
Control
Cost function
Engineering
Impulse response
Innovations
Mechatronics
Norms
Parameter estimation
Regular Papers
Robotics
Stochastic systems
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper studies the parameter estimation algorithms of a finite impulse response system with colored noise. To suppress the negative effects of the colored noises, a novel gradient-based algorithm is developed by means of the cost function of the continuous mixed p -norm (CMPN). It combines the p -norms for 1 ⩽ p ⩽ 2, which control the proportions of the error norms and generate an adjustable gain to adapt the data quality. Moreover, to improve the convergence rate, a CMPN multi-innovation gradient recursive algorithm is derived through expanding the innovation scalar to the innovation vector. Finally, two examples are given to demonstrate the validity of the proposed algorithms.
ISSN:1598-6446
2005-4092
DOI:10.1007/s12555-021-0572-4