Publications

@conference{Mayoufi20203701b,

title = {An optimal instrumental variable approach for continuous-time multiple input-single output fractional model identification},

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

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107518806\&doi=10.1016%2fj.ifacol.2020.12.2055\&partnerID=40\&md5=a45fe2670f212782b1c97c7adcd159c9},

doi = {10.1016/j.ifacol.2020.12.2055},

year  = {2020},

date = {2020-01-01},

journal = {IFAC-PapersOnLine},

volume = {53},

pages = {3701 \textendash 3706},

abstract = {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)},

note = {Cited by: 1; All Open Access, Bronze Open Access, Green Open Access},

keywords = {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},

pubstate = {published},

tppubtype = {conference}

}

@conference{Mayoufi20203701,

title = {An optimal instrumental variable approach for continuous-time multiple input-single output fractional model identification},

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

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107518806\&doi=10.1016%2fj.ifacol.2020.12.2055\&partnerID=40\&md5=a45fe2670f212782b1c97c7adcd159c9},

doi = {10.1016/j.ifacol.2020.12.2055},

year  = {2020},

date = {2020-01-01},

journal = {IFAC-PapersOnLine},

volume = {53},

pages = {3701 \textendash 3706},

abstract = {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)},

note = {Cited by: 1; All Open Access, Bronze Open Access, Green Open Access},

keywords = {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},

pubstate = {published},

tppubtype = {conference}

}

@conference{Chetoui201990b,

title = {Instrumental variables based methods for linear systems identification with fractional models in the EIV context},

author = {Manel Chetoui and Mohamed Aoun},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075640184\&doi=10.1109%2fSSD.2019.8893265\&partnerID=40\&md5=2f59e71847ca98f45a7d71b9b46a627d},

doi = {10.1109/SSD.2019.8893265},

year  = {2019},

date = {2019-01-01},

journal = {16th International Multi-Conference on Systems, Signals and Devices, SSD 2019},

pages = {90 \textendash 95},

abstract = {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.},

note = {Cited by: 5},

keywords = {Continuous time systems, Continuous-time, Fourth-order cumulants, Fractional differentiation, Higher order statistics, Image segmentation, Instrumental variables, Least Square, Linear systems, State-variable filters},

pubstate = {published},

tppubtype = {conference}

}

@conference{Ethabet2019406b,

title = {Set-Membership Fault Detection for Continuous-time Switched Linear Systems},

author = {Haifa Ethabet and Tarek Ra\"{i}ssi and Messaoud Amairi and Mohamed Aoun},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074402785\&doi=10.1109%2fASET.2019.8870992\&partnerID=40\&md5=d92dcb337b52e6a06fd5caf02b00c0c0},

doi = {10.1109/ASET.2019.8870992},

year  = {2019},

date = {2019-01-01},

journal = {Proceedings of International Conference on Advanced Systems and Emergent Technologies, IC_ASET 2019},

pages = {406 \textendash 411},

abstract = {The problem of Fault Detection (FD) for continuous-time switched linear systems subject to bounded disturbances is investigated in this paper. Based on cooperativity and stability properties, and fulfillment of an Average Dwell Time (ADT) constraint, guaranteed upper and lower bounds of the state are calculated using an interval observer. Under the assumption that disturbances and measurement noise are unknown but bounded with a priori known bounds, stability criteria is expressed in terms of Linear Matrix Inequalities (LMIs). Then, a fault detection methodology is developed to indicate the presence of faults. Finally, we demonstrate the proposed fault detection approach via an illustrative example. © 2019 IEEE.},

note = {Cited by: 7},

keywords = {Bounded disturbances, Continuous time systems, Continuous-time, Fault detection, Linear matrix inequalities, Linear systems, Set membership, Stability criteria, Stability properties, Switched linear system, Switched system, Unknown but bounded, Upper and lower bounds},

pubstate = {published},

tppubtype = {conference}

}

@conference{Salem2016640b,

title = {Instrumental variable based methods for continuous-time linear parameter varying system identification with fractional models},

author = {Thouraya Salem and Manel Chetoui and Mohamed Aoun},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84986183506\&doi=10.1109%2fMED.2016.7536043\&partnerID=40\&md5=42b2e04961aef2ea329989e32a842690},

doi = {10.1109/MED.2016.7536043},

year  = {2016},

date = {2016-01-01},

journal = {24th Mediterranean Conference on Control and Automation, MED 2016},

pages = {640 \textendash 645},

abstract = {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.},

note = {Cited by: 9},

keywords = {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},

pubstate = {published},

tppubtype = {conference}

}

@conference{Yakoub2015e,

title = {A direct fractional order bias eliminated least squares method for the fractional closed-loop system identification},

author = {Z. Yakoub and M. Chetoui and M. Amairi and M. Aoun},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962688308\&doi=10.1109%2fSSD.2015.7348219\&partnerID=40\&md5=1523b25860bd1afff62b4ced5a164cdd},

doi = {10.1109/SSD.2015.7348219},

year  = {2015},

date = {2015-01-01},

journal = {12th International Multi-Conference on Systems, Signals and Devices, SSD 2015},

abstract = {The paper deals with the continuous-time fractional closed-loop system identification in a noisy output context. Both coefficients and fractional orders of the process are estimated using the direct approach. The proposed method is based on the least squares technique and the state variable filter. It is an extension of the bias eliminated least squares method to the fractional systems. It is combined to a nonlinear optimization algorithm in order to estimate both coefficients and fractional orders of the fractional process. A numerical example is presented to illustrate the performances of the proposed methods. © 2015 IEEE.},

note = {Cited by: 2},

keywords = {Algorithms, Closed loop systems, Closed loops, Continuous time systems, Continuous-time, Direct approach, Fractional differentiation, Identification (control systems), Least Square, Least squares approximations, Nonlinear programming, Numerical methods, Optimization, Religious buildings, State-variable filters},

pubstate = {published},

tppubtype = {conference}

}

@conference{Chetoui2014b,

title = {Fourth order cumulants in EIV system identification using fractional models. An electronic application},

author = {Manel Chetoui and Magalie Thomassin and Rachid Malti and Mohamed Aoun and Mohamed Naceur Abdelkrim},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84918524787\&doi=10.1109%2fICFDA.2014.6967436\&partnerID=40\&md5=4864e15da0b3922a4215fa5c5ac8366c},

doi = {10.1109/ICFDA.2014.6967436},

year  = {2014},

date = {2014-01-01},

journal = {2014 International Conference on Fractional Differentiation and Its Applications, ICFDA 2014},

abstract = {This paper considers the problem of identifying continuous-time fractional systems from noisy input/output measurements. Firstly, the differentiation orders are fixed and the differential equation coefficients are estimated using an estimator based on Higher-Order Statistics: fractional fourth-order cumulants based least squares (ffocls). Then, the commensurate order is estimated along with the differential equation coefficients using a non linear optimization technique combined to the ffocls algorithm (co-ffocls). Under some assumptions on the distributional properties of additive noises and the noise-free input signals, the developed estimators give consistent results. Hence, the noise-free input signal is assumed to be non gaussian, whereas the additive noises are assumed to be gaussian. The performances of the developed algorithms are assessed through a practical application for modeling a real electronic system. © 2014 IEEE.},

note = {Cited by: 0},

keywords = {Additive noise, Additives, commensurate order, Continuous time systems, Continuous-time, Differential equations, Electronic systems, Errors in variables, Fourth-order cumulants, Fractional differentiation, Higher order statistics, Identification (control systems), Nonlinear programming, Religious buildings, Signal processing},

pubstate = {published},

tppubtype = {conference}

}

@conference{Yakoub2014128b,

title = {A bias-eliminated least squares method for continuous-time fractional closed-loop system identification},

author = {Z. Yakoub and M. Amairi and M. Chetoui and M. Aoun},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84920520477\&doi=10.1109%2fCCA.2014.6981340\&partnerID=40\&md5=1e18e1e8dcd7b25d35f5589188cfe8a6},

doi = {10.1109/CCA.2014.6981340},

year  = {2014},

date = {2014-01-01},

journal = {2014 IEEE Conference on Control Applications, CCA. Part of 2014 IEEE Multi-conference on Systems and Control, MSC 2014},

pages = {128 \textendash 133},

abstract = {This paper deals with continuous-time fractional closed-loop system identification in a noisy output context. A bias correction method called the bias-eliminated least squares is extended for indirect approach identification of closed-loop system with fractional models. This method is based on the least squares method combined with the state variable filter and assumes that the regulator order can not be lower than the process order. The performances of the proposed method are assessed through a numerical example. © 2014 IEEE.},

note = {Cited by: 5},

keywords = {Closed loop systems, Closed loops, Continuous time systems, Continuous-time, Fractional differentiation, Identification (control systems), Least Square, Least squares approximations, Numerical methods, Religious buildings, State-variable filters},

pubstate = {published},

tppubtype = {conference}

}

@conference{Chetoui2014,

title = {Fourth order cumulants in EIV system identification using fractional models. An electronic application},

author = {Manel Chetoui and Magalie Thomassin and Rachid Malti and Mohamed Aoun and Mohamed Naceur Abdelkrim},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84918524787\&doi=10.1109%2fICFDA.2014.6967436\&partnerID=40\&md5=4864e15da0b3922a4215fa5c5ac8366c},

doi = {10.1109/ICFDA.2014.6967436},

year  = {2014},

date = {2014-01-01},

journal = {2014 International Conference on Fractional Differentiation and Its Applications, ICFDA 2014},

abstract = {This paper considers the problem of identifying continuous-time fractional systems from noisy input/output measurements. Firstly, the differentiation orders are fixed and the differential equation coefficients are estimated using an estimator based on Higher-Order Statistics: fractional fourth-order cumulants based least squares (ffocls). Then, the commensurate order is estimated along with the differential equation coefficients using a non linear optimization technique combined to the ffocls algorithm (co-ffocls). Under some assumptions on the distributional properties of additive noises and the noise-free input signals, the developed estimators give consistent results. Hence, the noise-free input signal is assumed to be non gaussian, whereas the additive noises are assumed to be gaussian. The performances of the developed algorithms are assessed through a practical application for modeling a real electronic system. © 2014 IEEE.},

note = {Cited by: 0},

keywords = {Additive noise, Additives, commensurate order, Continuous time systems, Continuous-time, Differential equations, Electronic systems, Errors in variables, Fourth-order cumulants, Fractional differentiation, Higher order statistics, Identification (control systems), Nonlinear programming, Religious buildings, Signal processing},

pubstate = {published},

tppubtype = {conference}

}

@conference{Chetoui2013c,

title = {Fourth-order cumulants based method for continuous-time EIV fractional model identification},

author = {Manel Chetoui and Rachid Malti and Magalie Thomassin and Slaheddine Najar and Mohamed Aoun and Mohamed Naceur Abdelkrim and Alain Oustaloup},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84883101214\&doi=10.1109%2fSSD.2013.6564119\&partnerID=40\&md5=ea605860656fc371483bf9eefe2534d0},

doi = {10.1109/SSD.2013.6564119},

year  = {2013},

date = {2013-01-01},

journal = {2013 10th International Multi-Conference on Systems, Signals and Devices, SSD 2013},

abstract = {The errors-in-variables (EIV) system identification problem concerns the dynamic systems whose discrete input and output are corrupted by additive noises, that can be white, colored and/or mutually correlated. In this paper, a new estimator based on Higher-Order Statistics (fourth-order cumulants) is proposed for continuous-time system identification with fractional models. Under some assumptions on the distributional properties of the noise and noise-free signals, the fractional fourth-order cumulants based least squares (ffocls) estimator gives consistent results. A numerical example illustrates the performance of the proposed method. © 2013 IEEE.},

note = {Cited by: 4},

keywords = {Continuous time systems, Continuous-time, Continuous-time system identification, Distributional property, Errors, Errors in variables, Fourth-order cumulants, Fractional differentiation, Fractional model identification, Higher order statistics, Identification (control systems), System identification problems},

pubstate = {published},

tppubtype = {conference}

}

@conference{Chetoui2013,

title = {Fourth-order cumulants based method for continuous-time EIV fractional model identification},

author = {Manel Chetoui and Rachid Malti and Magalie Thomassin and Slaheddine Najar and Mohamed Aoun and Mohamed Naceur Abdelkrim and Alain Oustaloup},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84883101214\&doi=10.1109%2fSSD.2013.6564119\&partnerID=40\&md5=ea605860656fc371483bf9eefe2534d0},

doi = {10.1109/SSD.2013.6564119},

year  = {2013},

date = {2013-01-01},

journal = {2013 10th International Multi-Conference on Systems, Signals and Devices, SSD 2013},

abstract = {The errors-in-variables (EIV) system identification problem concerns the dynamic systems whose discrete input and output are corrupted by additive noises, that can be white, colored and/or mutually correlated. In this paper, a new estimator based on Higher-Order Statistics (fourth-order cumulants) is proposed for continuous-time system identification with fractional models. Under some assumptions on the distributional properties of the noise and noise-free signals, the fractional fourth-order cumulants based least squares (ffocls) estimator gives consistent results. A numerical example illustrates the performance of the proposed method. © 2013 IEEE.},

note = {Cited by: 4},

keywords = {Continuous time systems, Continuous-time, Continuous-time system identification, Distributional property, Errors, Errors in variables, Fourth-order cumulants, Fractional differentiation, Fractional model identification, Higher order statistics, Identification (control systems), System identification problems},

pubstate = {published},

tppubtype = {conference}

}

@conference{Chetoui20121641b,

title = {EIV methods for system identification with fractional models},

author = {Manel Chetoui and Rachid Malti and Magalie Thomassin and Mohamed Aoun and Slaheddine Najar and Alain Oustaloup and Mohamed Naceur Abdelkrim},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84867084248\&doi=10.3182%2f20120711-3-BE-2027.00270\&partnerID=40\&md5=dada6960d43c8fb326f09161d504d824},

doi = {10.3182/20120711-3-BE-2027.00270},

year  = {2012},

date = {2012-01-01},

journal = {IFAC Proceedings Volumes (IFAC-PapersOnline)},

volume = {16},

number = {PART 1},

pages = {1641 \textendash 1646},

abstract = {This paper deals with continuous-time system identification with fractional models in Errors-In-Variables context. Two estimators based on Higher-Order Statistics (third-order cumulants) are proposed. A State Variable Filter approach is extended to fractional orders to compute fractional derivatives of third-order cumulants estimates. The performance of the proposed algorithms is illustrated in a numerical example. Firstly, differentiation orders are fixed and differential equation coefficients are estimated. The consistency of the proposed estimators is evaluated through a study of the tuning parameter and Monte Carlo simulations. Then, the commensurate differentiation order is optimized along with the differential equation coefficients. © 2012 IFAC.},

note = {Cited by: 14},

keywords = {Continuous time systems, Continuous-time, Cumulants, Differential equations, Errors in variables, Fractional SVF, Higher order statistics, Identification (control systems), Iterative, Iterative methods, Least Square, Monte Carlo methods, Religious buildings},

pubstate = {published},

tppubtype = {conference}

}