Highly computationally efficient parameter estimation algorithms for a class of nonlinear multivariable systems by utilizing the state estimates

This paper investigates the parameter estimation issue for an input nonlinear multivariable state-space system. First, the canonical form of the input nonlinear multivariable state-space system is obtained through the linear transformation and the over-parameterization identification model of the co...

Full description

Saved in:
Bibliographic Details
Published in:Nonlinear dynamics Vol. 111; no. 9; pp. 8477 - 8496
Main Authors: Cui, Ting, Ding, Feng
Format: Journal Article
Language:English
Published: Dordrecht Springer Netherlands 01.05.2023
Springer Nature B.V
Subjects:
Algorithms
Automotive Engineering
Canonical forms
Classical Mechanics
Computational efficiency
Computing time
Control
Dynamical Systems
Efficiency
Engineering
Estimates
Identification
Identification methods
Least squares
Linear transformations
Mechanical Engineering
Methods
Nonlinear systems
Original Paper
Parameter estimation
Parameter identification
Parameterization
Process controls
Simulation
State estimation
Vibration
Input nonlinear model
Parameter estimation
Coupling identification
Multivariable system
Over-parameterization
ISSN:0924-090X, 1573-269X
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper investigates the parameter estimation issue for an input nonlinear multivariable state-space system. First, the canonical form of the input nonlinear multivariable state-space system is obtained through the linear transformation and the over-parameterization identification model of the considered system is derived. Second, by cutting down the redundant parameter estimates and extracting the unique parameter estimates from the parameter estimation vector in the least-squares identification method, we present an over-parameterization-based partially coupled average recursive extended least-squares parameter estimation algorithm to estimate the parameters. As for the unknown states in the parameter estimation algorithm, a new state estimator is designed to generate the state estimates. Third, in order to improve the computational efficiency of the parameter estimation algorithm, an over-parameterization-based multi-stage partially coupled average recursive extended least-squares algorithm is proposed. Finally, the computational efficiency analysis and the simulation examples are given to verify the effectiveness of the proposed algorithms.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0924-090X
1573-269X
DOI:10.1007/s11071-023-08259-3