Long-memory recursive prediction error method for identification of continuous-time fractional models

This paper deals with recursive continuous-time system identification using fractional-order models. Long-memory recursive prediction error method is proposed for recursive estimation of all parameters of fractional-order models. When differentiation orders are assumed known, least squares and predi...

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Bibliographic Details
Published in:Nonlinear dynamics Vol. 110; no. 1; pp. 635 - 648
Main Authors: Victor, Stéphane, Duhé, Jean-François, Melchior, Pierre, Abdelmounen, Youssef, Roubertie, François
Format: Journal Article
Language:English
Published: Dordrecht Springer Netherlands 01.09.2022
Springer Nature B.V
Springer Verlag
Subjects:
Algorithms
Approximation
Automotive Engineering
Classical Mechanics
Computer simulation
Continuous time systems
Control
Differentiation
Dynamical Systems
Engineering
Engineering Sciences
Errors
Identification
Lungs
Mathematical models
Mechanical Engineering
Original Paper
Parameter estimation
Parameters
Regularization methods
System identification
Vibration
Prediction error method
Fractional-order model
Least squares
Real-time system identification
Recursive identification
System identification
Long-memory prediction error method
Continuous-time models
Fractional calculus
ISSN:0924-090X, 1573-269X
Online Access:Get full text
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Summary:This paper deals with recursive continuous-time system identification using fractional-order models. Long-memory recursive prediction error method is proposed for recursive estimation of all parameters of fractional-order models. When differentiation orders are assumed known, least squares and prediction error methods, being direct extensions to fractional-order models of the classic methods used for integer-order models, are compared to our new method, the long-memory recursive prediction error method. Given the long-memory property of fractional models, Monte Carlo simulations prove the efficiency of our proposed algorithm. Then, when the differentiation orders are unknown, two-stage algorithms are necessary for both parameter and differentiation-order estimation. The performances of the new proposed recursive algorithm are studied through Monte Carlo simulations. Finally, the proposed algorithm is validated on a biological example where heat transfers in lungs are modeled by using thermal two-port network formalism with fractional models.
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ISSN:0924-090X
1573-269X
DOI:10.1007/s11071-022-07628-8