Publications

@article{Victor2022b,

title = {System identification of MISO fractional systems: Parameter and differentiation order estimation},

author = {St\'{e}phane Victor and Abir Mayoufi and Rachid Malti and Manel Chetoui and Mohamed Aoun},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127368402\&doi=10.1016%2fj.automatica.2022.110268\&partnerID=40\&md5=94d17d7d182d7bb2c6bffb4406265369},

doi = {10.1016/j.automatica.2022.110268},

year  = {2022},

date = {2022-01-01},

journal = {Automatica},

volume = {141},

abstract = {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},

note = {Cited by: 10},

keywords = {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},

pubstate = {published},

tppubtype = {article}

}

@article{Victor2022,

title = {System identification of MISO fractional systems: Parameter and differentiation order estimation},

author = {St\'{e}phane Victor and Abir Mayoufi and Rachid Malti and Manel Chetoui and Mohamed Aoun},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127368402\&doi=10.1016%2fj.automatica.2022.110268\&partnerID=40\&md5=94d17d7d182d7bb2c6bffb4406265369},

doi = {10.1016/j.automatica.2022.110268},

year  = {2022},

date = {2022-01-01},

journal = {Automatica},

volume = {141},

abstract = {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},

note = {Cited by: 10},

keywords = {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},

pubstate = {published},

tppubtype = {article}

}

@article{Hamdi20184185b,

title = {Recursive set-membership parameter estimation of fractional systems using orthotopic approach},

author = {Saif Eddine Hamdi and Messaoud Amairi and Mohamed Aoun},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045302403\&doi=10.1177%2f0142331217744853\&partnerID=40\&md5=40ffcf990575c0015868048fce18b27a},

doi = {10.1177/0142331217744853},

year  = {2018},

date = {2018-01-01},

journal = {Transactions of the Institute of Measurement and Control},

volume = {40},

number = {15},

pages = {4185 \textendash 4197},

abstract = {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.},

note = {Cited by: 5},

keywords = {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},

pubstate = {published},

tppubtype = {article}

}