Publications

@conference{Lamouchi20181199b,

title = {Interval Observer Design for Actuator Fault Estimation of Linear Parameter-Varying Systems},

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

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054559690\&doi=10.1016%2fj.ifacol.2018.09.699\&partnerID=40\&md5=de423414de6ba51a4812a058275b7b0a},

doi = {10.1016/j.ifacol.2018.09.699},

year  = {2018},

date = {2018-01-01},

volume = {51},

number = {24},

pages = {1199 \textendash 1204},

abstract = {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},

note = {Cited by: 5; All Open Access, Bronze Open Access, Green Open Access},

keywords = {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},

pubstate = {published},

tppubtype = {conference}

}

@conference{Lamouchi20181199,

title = {Interval Observer Design for Actuator Fault Estimation of Linear Parameter-Varying Systems},

author = {R. Lamouchi and T. Ra\"{i}ssi and M. Amairi and M. Aoun},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054559690\&doi=10.1016%2fj.ifacol.2018.09.699\&partnerID=40\&md5=de423414de6ba51a4812a058275b7b0a},

doi = {10.1016/j.ifacol.2018.09.699},

issn = {24058963},

year  = {2018},

date = {2018-01-01},

volume = {51},

number = {24},

pages = {1199-1204},

publisher = {Elsevier B.V.},

abstract = {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},

note = {cited By 6},

keywords = {Actuators; Linear systems, Discrete time linear parameter varying (LPV) system; External disturbances; Fault estimation; Interval observers; Linear parameter varying systems; Lower and upper bounds; LPV systems; Unknown input observer, Parameter estimation},

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{Salem2016640,

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

author = {T. Salem and M. Chetoui and M. 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},

isbn = {9781467383455},

year  = {2016},

date = {2016-01-01},

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

pages = {640-645},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

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; Differential equations; Estimation; Identification (control systems); Intelligent systems; Linear systems; Monte Carlo methods; Religious buildings, Continuous-time; Fractional differential equations; Fractional differentiation; Instrumental variables; Linear parameter varying models; Linear parameter varying systems; LPV systems; Refined instrumental variables, Parameter estimation},

pubstate = {published},

tppubtype = {conference}

}

@conference{Mohamed2016,

title = {IV parametric identification for MIMO continuous and delayed chemical reactor},

author = {S. B. Mohamed and B. Boussaid and M. N. Abdelkrim and C. Tahri},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84965003766\&doi=10.1109%2fICMIC.2015.7409449\&partnerID=40\&md5=a38d71d882d635ab92f8d956a1106de3},

doi = {10.1109/ICMIC.2015.7409449},

isbn = {9780956715753},

year  = {2016},

date = {2016-01-01},

journal = {Proceedings of 2015 7th International Conference on Modelling, Identification and Control, ICMIC 2015},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {The chemical reactor is a fairly complex industrial system and presents several variables, so an online identification of the process parameters is required to adjust the process control loops. This paper deals with continuous time and delay system identification of the chemical reactor. The identification of linear and multi-input multi-output (MIMO) system is based firstly, on the Least Square/ Instrumental Variable (LS/ IV) method and secondly on the instrumental variable (IV) which the output is corrupted by a non Gaussian white noise sequence. The least square method is perhaps the most commonly used parametric identification method then it is the best known method but it is very biased for a white measurement noise. To remove this bias we use the instrumental variable (IV) method because this approach is less biased. Results obtained indicate that the IV method is performing better than the LS/ IV method then it provides more accurate estimated parameters. © 2015 University of Al Qayrawan, Tunisia.},

note = {cited By 0},

keywords = {Chemical reactors; Continuous time systems; Gaussian noise (electronic); Least squares approximations; MIMO systems; Process control; White noise, Complex industrial systems; Instrumental variable methods; Instrumental variables; Least square methods; Multi-input multi-output system; On-line identification; Parametric identification; Process control loop, Parameter estimation},

pubstate = {published},

tppubtype = {conference}

}

@conference{Mohamed201579,

title = {LS/IV parametric identification for continuous and delay chemical reactor},

author = {S. B. Mohamed and B. Boussaid and N. Abdelkrim and C. Tahri},

editor = {Chaabane M. Aitouch A. Mehdi D.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962518106\&doi=10.1109%2fICoSC.2015.7152784\&partnerID=40\&md5=98cf9d2ee9483c6a37380c11ddcbb124},

doi = {10.1109/ICoSC.2015.7152784},

isbn = {9781479983186},

year  = {2015},

date = {2015-01-01},

journal = {2015 4th International Conference on Systems and Control, ICSC 2015},

pages = {79-84},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Because of the dependence of the parameters in the chemical reactor industries to the raw material used in processes, an online identification of the process parameters is necessary to adjust the process control loops. This paper deals with continuous time and delay system identification of the chemical reactor. The identification of linear and single inputsingle output (SISO) system is based on the least square (LS) method, in which the output is corrupted by a non Gaussian white noise sequence. The least square method is perhaps the most commonly used parametric identification method, but it is biased even for a white measurement noise. To remove this bias we use the instrumental variable (IV) method. Results obtained indicate that the proposed algorithm provides more accurate estimated parameters. © 2015 IEEE.},

note = {cited By 1},

keywords = {Chemical reactors; Continuous time systems; Gaussian noise (electronic); Identification (control systems); Least squares approximations; Process control; White noise, Instrumental variable methods; Instrumental variables; Least Square; Least square methods; On-line identification; Parametric identification; Process control loop; Sampled data, Parameter estimation},

pubstate = {published},

tppubtype = {conference}

}

@conference{Amairi2012c,

title = {Set membership parameter estimation of linear fractional systems using parallelotopes},

author = {Messaoud Amairi and Mohamed Aoun and Slaheddine Najar and Mohamed Naceur Abdelkrim},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84861590137\&doi=10.1109%2fSSD.2012.6198103\&partnerID=40\&md5=95504e546a599f4ffcb7e8bcb5a29a2e},

doi = {10.1109/SSD.2012.6198103},

year  = {2012},

date = {2012-01-01},

journal = {International Multi-Conference on Systems, Signals and Devices, SSD 2012 - Summary Proceedings},

abstract = {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.},

note = {Cited by: 11},

keywords = {Algorithms, Fractional model, Fractional systems, Parameter estimation, Set-membership, Time domain},

pubstate = {published},

tppubtype = {conference}

}

@conference{Amairi2012,

title = {Set membership parameter estimation of linear fractional systems using parallelotopes},

author = {M. Amairi and M. Aoun and S. Najar and M. N. Abdelkrim},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84861590137\&doi=10.1109%2fSSD.2012.6198103\&partnerID=40\&md5=95504e546a599f4ffcb7e8bcb5a29a2e},

doi = {10.1109/SSD.2012.6198103},

isbn = {9781467315906},

year  = {2012},

date = {2012-01-01},

journal = {International Multi-Conference on Systems, Signals and Devices, SSD 2012 - Summary Proceedings},

abstract = {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.},

note = {cited By 10},

keywords = {Algorithms, Fractional model; Fractional systems; Set-membership; Time domain, Parameter estimation},

pubstate = {published},

tppubtype = {conference}

}

@conference{Aoun2011e,

title = {An ellipsoidal set-membership parameter estimation of fractional orders systems},

author = {M. Aoun and M. Amairi and Z. Lassoued and S. Najar and M. N. Abdelkrim},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79957881899\&doi=10.1109%2fSSD.2011.5767416\&partnerID=40\&md5=f4a561276b178fc18bfc9dea1dc69866},

doi = {10.1109/SSD.2011.5767416},

year  = {2011},

date = {2011-01-01},

journal = {International Multi-Conference on Systems, Signals and Devices, SSD'11 - Summary Proceedings},

abstract = {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.},

note = {Cited by: 4},

keywords = {Algorithms, Fogel-Huang algorithm, Fractional differentiation, Fractional systems, Identification (control systems), Numerical methods, OBE, Parameter estimation, Probability distributions, Set-membership, system identification},

pubstate = {published},

tppubtype = {conference}

}