2022 |
Victor, Stéphane; Mayoufi, Abir; Malti, Rachid; Chetoui, Manel; Aoun, Mohamed System identification of MISO fractional systems: Parameter and differentiation order estimation Article de journal Dans: Automatica, vol. 141, 2022, (Cited by: 10). Résumé | Liens | BibTeX | Étiquettes: Continous time, Continuous time systems, Fractional model, Fractional systems, Instrumental variables, Intelligent systems, Monte Carlo methods, Multiple input single output systems, Multiple inputs single outputs, Optimization, Optimization algorithms, Order estimation, Order optimizations, Parameter estimation, Religious buildings, System-identification @article{Victor2022b,This paper deals with continuous-time system identification of multiple-input single-output (MISO) fractional differentiation models. When differentiation orders are assumed to be known, coefficients are estimated using the simplified refined instrumental variable method for continuous-time fractional models extended to the MISO case. For unknown differentiation orders, a two-stage optimization algorithm is proposed with the developed instrumental variable for coefficient estimation and a gradient-based algorithm for differentiation order estimation. A new definition of structured-commensurability (or S-commensurability) is introduced to better cope with differentiation order estimation. Three variants of the algorithm are then proposed: (i) first, all differentiation orders are set as integer multiples of a global S-commensurate order, (ii) then, the differentiation orders are set as integer multiples of a local S-commensurate orders (one S-commensurate order for each subsystem), (iii) finally, all differentiation orders are estimated by releasing the S-commensurability constraint. The first variant has the smallest number of parameters and is used as a good initial hit for the second variant which in turn is used as a good initial hit for the third variant. Such a progressive increase of the number of parameters allows better performance of the optimization algorithm evaluated by Monte Carlo simulation analysis. © 2022 Elsevier Ltd |
Victor, Stéphane; Mayoufi, Abir; Malti, Rachid; Chetoui, Manel; Aoun, Mohamed System identification of MISO fractional systems: Parameter and differentiation order estimation Article de journal Dans: Automatica, vol. 141, 2022, (Cited by: 10). Résumé | Liens | BibTeX | Étiquettes: Continous time, Continuous time systems, Fractional model, Fractional systems, Instrumental variables, Intelligent systems, Monte Carlo methods, Multiple input single output systems, Multiple inputs single outputs, Optimization, Optimization algorithms, Order estimation, Order optimizations, Parameter estimation, Religious buildings, System-identification @article{Victor2022,This paper deals with continuous-time system identification of multiple-input single-output (MISO) fractional differentiation models. When differentiation orders are assumed to be known, coefficients are estimated using the simplified refined instrumental variable method for continuous-time fractional models extended to the MISO case. For unknown differentiation orders, a two-stage optimization algorithm is proposed with the developed instrumental variable for coefficient estimation and a gradient-based algorithm for differentiation order estimation. A new definition of structured-commensurability (or S-commensurability) is introduced to better cope with differentiation order estimation. Three variants of the algorithm are then proposed: (i) first, all differentiation orders are set as integer multiples of a global S-commensurate order, (ii) then, the differentiation orders are set as integer multiples of a local S-commensurate orders (one S-commensurate order for each subsystem), (iii) finally, all differentiation orders are estimated by releasing the S-commensurability constraint. The first variant has the smallest number of parameters and is used as a good initial hit for the second variant which in turn is used as a good initial hit for the third variant. Such a progressive increase of the number of parameters allows better performance of the optimization algorithm evaluated by Monte Carlo simulation analysis. © 2022 Elsevier Ltd |
2018 |
Lamouchi, Rihab; Raïssi, Tarek; Amairi, Messaoud; Aoun, Mohamed Interval Observer Design for Actuator Fault Estimation of Linear Parameter-Varying Systems Conférence vol. 51, no. 24, 2018, (Cited by: 5; All Open Access, Bronze Open Access, Green Open Access). Résumé | Liens | BibTeX | Étiquettes: Actuators, Discrete time linear parameter varying (LPV) system, External disturbances, Fault estimation, Interval observers, Linear parameter varying systems, Linear systems, Lower and upper bounds, LPV systems, Parameter estimation, Unknown input observer @conference{Lamouchi20181199b,This work is devoted to fault estimation of discrete-time Linear Parameter-Varying (LPV) systems subject to actuator additive faults and external disturbances. Under the assumption that the measurement noises and the disturbances are unknown but bounded, an interval observer is designed, based on decoupling the fault effect, to compute a lower and upper bounds for the unmeasured state and the faults. Stability conditions are expressed in terms of matrices inequalities. A case study is used to illustrate the effectiveness of the proposed approach. © 2018 |
Hamdi, Saif Eddine; Amairi, Messaoud; Aoun, Mohamed Recursive set-membership parameter estimation of fractional systems using orthotopic approach Article de journal Dans: Transactions of the Institute of Measurement and Control, vol. 40, no. 15, p. 4185 – 4197, 2018, (Cited by: 5). Résumé | Liens | BibTeX | Étiquettes: Bounded error context, Bounded errors, Errors, Fractional systems, Fractional-order systems, Iterative algorithm, Iterative methods, Monte Carlo methods, Order estimation, Parameter estimation, Set membership approach, Unknown but bounded @article{Hamdi20184185b,In this paper, set-membership parameter estimation of linear fractional-order systems is addressed for the case of unknown-but-bounded equation error. In such bounded-error context with a-priori known noise bounds, the main goal is to characterize the set of all feasible parameters. This characterization is performed using an orthotopic strategy adapted for fractional system parameter estimation. In the case of a fractional commensurate system, an iterative algorithm is proposed to deal with commensurate-order estimation. The performances of the proposed algorithm are illustrated by a numerical example via a Monte Carlo simulation. © The Author(s) 2018. |
2016 |
Salem, Thouraya; Chetoui, Manel; Aoun, Mohamed 2016, (Cited by: 9). Résumé | Liens | BibTeX | Étiquettes: Continuous time systems, Continuous-time, Differential equations, Estimation, Fractional differential equations, Fractional differentiation, Identification (control systems), Instrumental variables, Intelligent systems, Linear parameter varying models, Linear parameter varying systems, Linear systems, LPV systems, Monte Carlo methods, Parameter estimation, Refined instrumental variables, Religious buildings @conference{Salem2016640b,This paper deals with continuous-time linear parameter varying (LPV) system identification with fractional models. Two variants of instrumental variables based techniques are proposed to estimate continuous-time parameters of a fractional differential equation linear parameter varying model when all fractional orders are assumed known a priori: the first one is the instrumental variables estimator based in an auxiliary model. The second one is the simplified refined instrumental variables estimator. A comparison study between the developed estimators is done via a numerical example. A Monte Carlo simulation analysis results are presented to illustrate the performances of the proposed methods in the presence of an additive output noise. © 2016 IEEE. |
Salem, Thouraya; Chetoui, Manel; Aoun, Mohamed 2016, (Cited by: 9). Résumé | Liens | BibTeX | Étiquettes: Continuous time systems, Continuous-time, Differential equations, Estimation, Fractional differential equations, Fractional differentiation, Identification (control systems), Instrumental variables, Intelligent systems, Linear parameter varying models, Linear parameter varying systems, Linear systems, LPV systems, Monte Carlo methods, Parameter estimation, Refined instrumental variables, Religious buildings @conference{Salem2016640,This paper deals with continuous-time linear parameter varying (LPV) system identification with fractional models. Two variants of instrumental variables based techniques are proposed to estimate continuous-time parameters of a fractional differential equation linear parameter varying model when all fractional orders are assumed known a priori: the first one is the instrumental variables estimator based in an auxiliary model. The second one is the simplified refined instrumental variables estimator. A comparison study between the developed estimators is done via a numerical example. A Monte Carlo simulation analysis results are presented to illustrate the performances of the proposed methods in the presence of an additive output noise. © 2016 IEEE. |
2012 |
Amairi, Messaoud; Aoun, Mohamed; Najar, Slaheddine; Abdelkrim, Mohamed Naceur Set membership parameter estimation of linear fractional systems using parallelotopes Conférence 2012, (Cited by: 11). Résumé | Liens | BibTeX | Étiquettes: Algorithms, Fractional model, Fractional systems, Parameter estimation, Set-membership, Time domain @conference{Amairi2012c,The paper deals with set-membership parameter estimation of fractional models in the time-domain. In such a context, the noise is supposed to be unknown-but-bounded with a priori known bounds. The proposed algorithm computes the set of all feasible parameters represented by a parallelotop. Simulation results and performance comparaison with the ellipsoidal approach are also given. © 2012 IEEE. |
Amairi, Messaoud; Aoun, Mohamed; Najar, Slaheddine; Abdelkrim, Mohamed Naceur Guaranteed frequency-domain identification of fractional order systems: Application to a real system Article de journal Dans: International Journal of Modelling, Identification and Control, vol. 17, no. 1, p. 32 – 42, 2012, (Cited by: 20). Résumé | Liens | BibTeX | Étiquettes: Algebra, Constrained optimization, Fractional differentiation, Fractional systems, Frequency domain analysis, Frequency domains, Global optimisation, Global optimization, Identification (control systems), Parameter estimation, Real intervals, Satisfaction problem @article{Amairi201232b,This paper presents a new guaranteed approach for frequency-domain identification of fractional order systems. Estimated parameters (coefficients and differential orders) are expressed as intervals. Then, an interval-based global optimisation algorithm is used to estimate the set of all feasible parameters. It combines the Hansen’s algorithm with forward-backward contractor. The approach is applied to a numerical example as well as to a real electronic system. Copyright © 2012 Inderscience Enterprises Ltd. |
2011 |
Aoun, M.; Amairi, M.; Lassoued, Z.; Najar, S.; Abdelkrim, M. N. An ellipsoidal set-membership parameter estimation of fractional orders systems Conférence 2011, (Cited by: 4). Résumé | Liens | BibTeX | Étiquettes: Algorithms, Fogel-Huang algorithm, Fractional differentiation, Fractional systems, Identification (control systems), Numerical methods, OBE, Parameter estimation, Probability distributions, Set-membership, system identification @conference{Aoun2011e,This paper presents a new ellipsoidal set-membership method for the identification of linear fractional orders systems. It use the Optimal Bounding Ellipsoid (OBE) algorithm. When the probability distribution of the disturbances is unknown but bounded and when the differentiation orders are known, the proposed method can estimate all the feasible parameters. A numerical example shows the effectiveness of the proposed method. © 2011 IEEE. |
2006 |
Sabatier, Jocelyn; Aoun, Mohamed; Oustaloup, Alain; Grégoire, Gilles; Ragot, Franck; Roy, Patrick Fractional system identification for lead acid battery state of charge estimation Article de journal Dans: Signal Processing, vol. 86, no. 10, p. 2645 – 2657, 2006, (Cited by: 269). Résumé | Liens | BibTeX | Étiquettes: Fractional order dynamical systems, Fractional system identification method, Identification (control systems), Lead acid batteries, Mathematical models, Parameter estimation, State of charge estimation, Thermal effects @article{Sabatier20062645b,This paper deals with the application of fractional system identification to lead acid battery state of charge estimation. Fractional behavior of lead acid batteries is justified. A fractional system identification method recently developed is presented and a new fractional model of the battery is proposed. Based on parameter variations of this model, a state of charge estimation method is presented. Validation tests of this method on unknown state of charge are carried out. These validation tests highlights that the proposed estimation method gives a state of charge estimation with an error close to 5% whatever the operating temperature. © 2006 Elsevier B.V. All rights reserved. |
Sabatier, Jocelyn; Aoun, Mohamed; Oustaloup, Alain; Grégoire, Gilles; Ragot, Franck; Roy, Patrick Fractional system identification for lead acid battery state of charge estimation Article de journal Dans: Signal Processing, vol. 86, no. 10, p. 2645 – 2657, 2006, (Cited by: 269). Résumé | Liens | BibTeX | Étiquettes: Fractional order dynamical systems, Fractional system identification method, Identification (control systems), Lead acid batteries, Mathematical models, Parameter estimation, State of charge estimation, Thermal effects @article{Sabatier20062645,This paper deals with the application of fractional system identification to lead acid battery state of charge estimation. Fractional behavior of lead acid batteries is justified. A fractional system identification method recently developed is presented and a new fractional model of the battery is proposed. Based on parameter variations of this model, a state of charge estimation method is presented. Validation tests of this method on unknown state of charge are carried out. These validation tests highlights that the proposed estimation method gives a state of charge estimation with an error close to 5% whatever the operating temperature. © 2006 Elsevier B.V. All rights reserved. |
Publications
2022 |
System identification of MISO fractional systems: Parameter and differentiation order estimation Article de journal Dans: Automatica, vol. 141, 2022, (Cited by: 10). |
System identification of MISO fractional systems: Parameter and differentiation order estimation Article de journal Dans: Automatica, vol. 141, 2022, (Cited by: 10). |
2018 |
Interval Observer Design for Actuator Fault Estimation of Linear Parameter-Varying Systems Conférence vol. 51, no. 24, 2018, (Cited by: 5; All Open Access, Bronze Open Access, Green Open Access). |
Recursive set-membership parameter estimation of fractional systems using orthotopic approach Article de journal Dans: Transactions of the Institute of Measurement and Control, vol. 40, no. 15, p. 4185 – 4197, 2018, (Cited by: 5). |
2016 |
2016, (Cited by: 9). |
2016, (Cited by: 9). |
2012 |
Set membership parameter estimation of linear fractional systems using parallelotopes Conférence 2012, (Cited by: 11). |
Guaranteed frequency-domain identification of fractional order systems: Application to a real system Article de journal Dans: International Journal of Modelling, Identification and Control, vol. 17, no. 1, p. 32 – 42, 2012, (Cited by: 20). |
2011 |
An ellipsoidal set-membership parameter estimation of fractional orders systems Conférence 2011, (Cited by: 4). |
2006 |
Fractional system identification for lead acid battery state of charge estimation Article de journal Dans: Signal Processing, vol. 86, no. 10, p. 2645 – 2657, 2006, (Cited by: 269). |
Fractional system identification for lead acid battery state of charge estimation Article de journal Dans: Signal Processing, vol. 86, no. 10, p. 2645 – 2657, 2006, (Cited by: 269). |