2016 |
Hamdi, Saif Eddine; Amairi, Messaoud; Aoun, Mohamed Orthotopic set-membership parameter estimation of fractional order model Conférence 2016, (Cited by: 7). Résumé | Liens | BibTeX | Étiquettes: Fractional order, Fractional order models, Numerical methods, Orthotopic, Probability distributions, Set membership, Set membership method, Unknown but bounded @conference{Hamdi2016634b,This paper presents a new orthotopic set-membership method for the identification of linear fractional orders systems. This method consists in recursively constructing an outer orthotope that contains all feasible parameters when the probability distribution of the disturbances is unknown but bounded and when the differentiation orders are known. A numerical example shows the effectiveness of the proposed method. © 2016 IEEE. |
Chouki, Rihab; Aribi, Asma; Aoun, Mohamed; Abdelkarim, Mohamed N. Additive fault tolerant control for fractional order model systems Conférence 2016, (Cited by: 6). Résumé | Liens | BibTeX | Étiquettes: Additive faults, Automation, Fault tolerance, Fractional order models, Luenberger observers, Process control, Sensor fault @conference{Chouki2016340b,The additive fault tolerant control (FTC) for the fractional order model is presented, in this paper. Hence, two steps are compulsory in order to design the additive control. The first one being the estimation of the sensor fault amplitude which is realized by using the fractional Luenberger observer and the second one consists in generating the additive fault tolerant control law and then sum it to the nominal control of the fractional order model. © 2015 IEEE. |
Chouki, Rihab; Aribi, Asma; Aoun, Mohamed; Abdelkarim, Mohamed N. Additive fault tolerant control for fractional order model systems Conférence 2016, (Cited by: 6). Résumé | Liens | BibTeX | Étiquettes: Additive faults, Automation, Fault tolerance, Fractional order models, Luenberger observers, Process control, Sensor fault @conference{Chouki2016340,The additive fault tolerant control (FTC) for the fractional order model is presented, in this paper. Hence, two steps are compulsory in order to design the additive control. The first one being the estimation of the sensor fault amplitude which is realized by using the fractional Luenberger observer and the second one consists in generating the additive fault tolerant control law and then sum it to the nominal control of the fractional order model. © 2015 IEEE. |
2014 |
Aribi, Asma; Farges, Christophe; Aoun, Mohamed; Melchior, Pierre; Najar, Slaheddine; Abdelkrim, Mohamed Naceur Fault detection based on fractional order models: Application to diagnosis of thermal systems Article de journal Dans: Communications in Nonlinear Science and Numerical Simulation, vol. 19, no. 10, p. 3679 – 3693, 2014, (Cited by: 28). Résumé | Liens | BibTeX | Étiquettes: Computer simulation, Diagnosis, Diagnosis methods, Fault detection, Fractional model, Fractional operators, Fractional order models, Numerical analysis, Reduced precision, Single input multi outputs, Thermal phenomena, Thermal systems @article{Aribi20143679b,The aim of this paper is to propose diagnosis methods based on fractional order models and to validate their efficiency to detect faults occurring in thermal systems. Indeed, it is first shown that fractional operator allows to derive in a straightforward way fractional models for thermal phenomena. In order to apply classical diagnosis methods, such models could be approximated by integer order models, but at the expense of much higher involved parameters and reduced precision. Thus, two diagnosis methods initially developed for integer order models are here extended to handle fractional order models. The first one is the generalized dynamic parity space method and the second one is the Luenberger diagnosis observer. Proposed methods are then applied to a single-input multi-output thermal testing bench and demonstrate the methods efficiency for detecting faults affecting thermal systems. © 2014 Elsevier B.V. |
Aribi, Asma; Farges, Christophe; Aoun, Mohamed; Melchior, Pierre; Najar, Slaheddine; Abdelkrim, Mohamed Naceur Fault detection based on fractional order models: Application to diagnosis of thermal systems Article de journal Dans: Communications in Nonlinear Science and Numerical Simulation, vol. 19, no. 10, p. 3679 – 3693, 2014, (Cited by: 28). Résumé | Liens | BibTeX | Étiquettes: Computer simulation, Diagnosis, Diagnosis methods, Fault detection, Fractional model, Fractional operators, Fractional order models, Numerical analysis, Reduced precision, Single input multi outputs, Thermal phenomena, Thermal systems @article{Aribi20143679,The aim of this paper is to propose diagnosis methods based on fractional order models and to validate their efficiency to detect faults occurring in thermal systems. Indeed, it is first shown that fractional operator allows to derive in a straightforward way fractional models for thermal phenomena. In order to apply classical diagnosis methods, such models could be approximated by integer order models, but at the expense of much higher involved parameters and reduced precision. Thus, two diagnosis methods initially developed for integer order models are here extended to handle fractional order models. The first one is the generalized dynamic parity space method and the second one is the Luenberger diagnosis observer. Proposed methods are then applied to a single-input multi-output thermal testing bench and demonstrate the methods efficiency for detecting faults affecting thermal systems. © 2014 Elsevier B.V. |
2013 |
Aribi, Asma; Aoun, Mohamed; Farges, Christophe; Najar, Slaheddine; Melchior, Pierre; Abdelkrim, Mohamed Naceur Generalied Fractional Obsevers Scheme to fault detection and isolation Conférence 2013, (Cited by: 8). Résumé | Liens | BibTeX | Étiquettes: Fault detection and isolation, Fault detection and isolation schemes, Fractional order models, Fractional-order systems, Integer order @conference{Aribi2013b,This paper develops a fault detection and isolation scheme for fractional order systems. It is an extension to fractional order models of a scheme developed for integer order models to design Generalized Fractional Observers Scheme (GFOS). Such scheme allows to generate residuals perfectly roboust to disturbances and to isolate faults. Efficiency of the scheme is evaluated on a numerical example. © 2013 IEEE. |
Aribi, Asma; Aoun, Mohamed; Farges, Christophe; Najar, Slaheddine; Melchior, Pierre; Abdelkrim, Mohamed Naceur Generalied Fractional Obsevers Scheme to fault detection and isolation Conférence 2013, (Cited by: 8). Résumé | Liens | BibTeX | Étiquettes: Fault detection and isolation, Fault detection and isolation schemes, Fractional order models, Fractional-order systems, Integer order @conference{Aribi2013,This paper develops a fault detection and isolation scheme for fractional order systems. It is an extension to fractional order models of a scheme developed for integer order models to design Generalized Fractional Observers Scheme (GFOS). Such scheme allows to generate residuals perfectly roboust to disturbances and to isolate faults. Efficiency of the scheme is evaluated on a numerical example. © 2013 IEEE. |
2010 |
Amairi, Messaoud; Najar, Slaheddine; Aoun, Mohamed; Abdelkrim, M. N. Guaranteed output-error identification of fractional order model Conférence vol. 2, 2010, (Cited by: 13). Résumé | Liens | BibTeX | Étiquettes: Fractional differentiation, Fractional model, Fractional order, Fractional order models, Fractional-order systems, Global optimization, Global optimization techniques, Guaranteed convergence, Identification (control systems), Interval analysis, Optimization, System identifications @conference{Amairi2010246b,A global optimization technique for identifying an output-error fractional order model is proposed. The proposed technique use a modified version of Hansen algorithm. It is capable of estimating the fractional orders and the parameters, with guaranteed convergence. The technique is applied to identify a fractional order system in deterministic and stochastic context. © 2010 IEEE. |