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 |
2020 |
Mayoufi, Abir; Victor, Stéphane; Malti, Rachid; Chetoui, Manel; Aoun, Mohamed vol. 53, 2020, (Cited by: 1; All Open Access, Bronze Open Access, Green Open Access). Résumé | Liens | BibTeX | Étiquettes: Continuous time systems, Continuous-time, Fractional model, Fractional model identification, Instrumental variables, Monte Carlo methods, Multiple input single outputs, Order estimation, Refined instrumental variables, Single input single output @conference{Mayoufi20203701b,This paper proposes an instrumental variable approach for continuous-time system identification using fractional models with multiple input single output context. This work is an extension of the simplified refined instrumental variable approach (srivcf) developed for single input-single output fractional model identification (Malti et al. (2008a); Victor et al. (2013)) to the multiple input-single output case. Monte Carlo simulation analysis is used to demonstrate the performance of the proposed approach. A study is then provided to motivate differentiation order estimation, and more specifically, commensurate order estimation. Copyright © 2020 The Authors. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0) |
Mayoufi, Abir; Chetoui, Manel; Victor, Stephans; Aoun, Mohamed; Malti, Rachid A comparison between two methods for MISO fractional models estimation Conférence 2020, (Cited by: 0). Résumé | Liens | BibTeX | Étiquettes: Comparative studies, Fractional model, Fractional order, Instrumental variables, Linear coefficients, Monte Carlo methods, Multiple input single output systems, Numerical methods, Output errors @conference{Mayoufi2020446b,This paper proposes two new methods for multiple input-single output system identification with fractional models: The instrumental variable based method and the output-error based method. The fractional orders are supposed known and the linear coefficients are estimated. A comparative study between the developed methods is illustrated via a numerical example. Monte Carlo simulations are used to demonstrate the efficiency of the two methods. © 2020 IEEE. |
Mayoufi, Abir; Victor, Stéphane; Malti, Rachid; Chetoui, Manel; Aoun, Mohamed vol. 53, 2020, (Cited by: 1; All Open Access, Bronze Open Access, Green Open Access). Résumé | Liens | BibTeX | Étiquettes: Continuous time systems, Continuous-time, Fractional model, Fractional model identification, Instrumental variables, Monte Carlo methods, Multiple input single outputs, Order estimation, Refined instrumental variables, Single input single output @conference{Mayoufi20203701,This paper proposes an instrumental variable approach for continuous-time system identification using fractional models with multiple input single output context. This work is an extension of the simplified refined instrumental variable approach (srivcf) developed for single input-single output fractional model identification (Malti et al. (2008a); Victor et al. (2013)) to the multiple input-single output case. Monte Carlo simulation analysis is used to demonstrate the performance of the proposed approach. A study is then provided to motivate differentiation order estimation, and more specifically, commensurate order estimation. Copyright © 2020 The Authors. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0) |
2019 |
Chetoui, Manel; Aoun, Mohamed 2019, (Cited by: 5). Résumé | Liens | BibTeX | Étiquettes: Continuous time systems, Continuous-time, Fourth-order cumulants, Fractional differentiation, Higher order statistics, Image segmentation, Instrumental variables, Least Square, Linear systems, State-variable filters @conference{Chetoui201990b,In this paper a new instrumental variables methods based on the Higher-Order-Statistics (fourth order cumulants) are developed for continuous-time system identification with fractional models in the errors in variables context. The fractional orders are supposed known a priori and only the linear coefficients are estimated. The developed algorithms are compared to a fractional fourth order cumulants based least squares algorithm. Their performances are tested through a numerical example in two cases: white and colored noises affecting the input and the output measurements. © 2019 IEEE. |
Yakoub, Zaineb; Amairi, Messaoud; Aoun, Mohamed; Chetoui, Manel On the fractional closed-loop linear parameter varying system identification under noise corrupted scheduling and output signal measurements Article de journal Dans: Transactions of the Institute of Measurement and Control, vol. 41, no. 10, p. 2909 – 2921, 2019, (Cited by: 3). Résumé | Liens | BibTeX | Étiquettes: Accident prevention, Calculations, Continuous time systems, Differentiation (calculus), Fractional calculus, Instrumental variables, Least Square, Linear parameters, Linear systems, Non-linear optimization, Nonlinear programming, Scheduling @article{Yakoub20192909b,It is well known that, in some industrial process identification situations, measurements can be collected from closed-loop experiments for several reasons such as stability, safety, and performance constraints. In this paper, we investigate the problem of identifying continuous-time fractional closed-loop linear parameter varying systems. The simplified refined instrumental variable method is developed to estimate both coefficients and differentiation orders. This method is established to provide consistent estimates when the output and the scheduling variable are contaminated by additive measurements noise. The proposed scheme is evaluated in comparison with other approaches in terms of a simulation example. © The Author(s) 2019. |
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. |
2015 |
Yakoub, Z.; Amairi, M.; Chetoui, M.; Aoun, M. On the Closed-Loop System Identification with Fractional Models Article de journal Dans: Circuits, Systems, and Signal Processing, vol. 34, no. 12, p. 3833 – 3860, 2015, (Cited by: 20). Résumé | Liens | BibTeX | Étiquettes: Algorithms, Closed loop systems, Closed loop transfer function, Closed loops, Fractional model, Identification (control systems), Information criterion, Instrumental variable methods, Instrumental variables, Intelligent systems, Least Square, Monte Carlo methods, Non-linear optimization algorithms, Nonlinear programming, Optimization, Religious buildings @article{Yakoub20153833b,In this paper, the fractional closed-loop system identification problem is addressed. Using the indirect approach, which supposes the knowledge of the controller, both coefficients and fractional orders of the process are estimated. The optimal instrumental variable method combined with a nonlinear optimization algorithm is handled to identify the fractional closed-loop transfer function. Also, two techniques are used for model selection the Akaike’s information criterion and the $$R_T^2$$RT2 criterion. The performances of the proposed scheme are illustrated by a numerical example via Monte Carlo simulation and by real electronic system identification. © 2015, Springer Science+Business Media New York. |
Publications
2022 |
System identification of MISO fractional systems: Parameter and differentiation order estimation Article de journal Dans: Automatica, vol. 141, 2022, (Cited by: 10). |
2020 |
vol. 53, 2020, (Cited by: 1; All Open Access, Bronze Open Access, Green Open Access). |
A comparison between two methods for MISO fractional models estimation Conférence 2020, (Cited by: 0). |
vol. 53, 2020, (Cited by: 1; All Open Access, Bronze Open Access, Green Open Access). |
2019 |
2019, (Cited by: 5). |
On the fractional closed-loop linear parameter varying system identification under noise corrupted scheduling and output signal measurements Article de journal Dans: Transactions of the Institute of Measurement and Control, vol. 41, no. 10, p. 2909 – 2921, 2019, (Cited by: 3). |
2016 |
2016, (Cited by: 9). |
2015 |
On the Closed-Loop System Identification with Fractional Models Article de journal Dans: Circuits, Systems, and Signal Processing, vol. 34, no. 12, p. 3833 – 3860, 2015, (Cited by: 20). |