Publications

@conference{Mayoufi2020446b,

title = {A comparison between two methods for MISO fractional models estimation},

author = {Abir Mayoufi and Manel Chetoui and Stephans Victor and Mohamed Aoun and Rachid Malti},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103003953\&doi=10.1109%2fSSD49366.2020.9364187\&partnerID=40\&md5=b9525866e8a24d2426d03414f5810e22},

doi = {10.1109/SSD49366.2020.9364187},

year  = {2020},

date = {2020-01-01},

journal = {Proceedings of the 17th International Multi-Conference on Systems, Signals and Devices, SSD 2020},

pages = {446 \textendash 451},

abstract = {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.},

note = {Cited by: 0},

keywords = {Comparative studies, Fractional model, Fractional order, Instrumental variables, Linear coefficients, Monte Carlo methods, Multiple input single output systems, Numerical methods, Output errors},

pubstate = {published},

tppubtype = {conference}

}

@conference{Achnib201734b,

title = {A comparative study of the output and the state feedback predictive control},

author = {Asma Achnib and Manel Chetoui and Patrick Lanusse and Mohamed Aoun},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85024379902\&doi=10.1109%2fSTA.2016.7952029\&partnerID=40\&md5=779b88cd1e20f8d1c6c3614eb10555e4},

doi = {10.1109/STA.2016.7952029},

year  = {2017},

date = {2017-01-01},

journal = {2016 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, STA 2016 - Proceedings},

pages = {34 \textendash 41},

abstract = {In this paper the effectiveness of predictive control in the problems of tracking and control is studied. The principle and the implementation of the algorithm of this control are presented. In this work, two approaches using different system models are developed: The output feedback and the state feedback predictive control. The performances and the robustness of both approaches are assessed with the help of two examples. © 2016 IEEE.},

note = {Cited by: 1},

keywords = {Automation, Comparative studies, Feedback, Model predictive control, Output feedback, Predictive control, Process control, Robustness (control systems), State feedback, State feedback predictive control, System models},

pubstate = {published},

tppubtype = {conference}

}