Publications

@conference{Ethabet2023b,

title = {L∞ Set-membership Estimation for Continuous-time Switched Linear Systems},

author = {Haifa Ethabet and Leila Dadi and Tarek Raissi and Mohamed Aoun},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182931405\&doi=10.1109%2fIW_MSS59200.2023.10369668\&partnerID=40\&md5=2fb780affae1b8628f3f526c9cabfef7},

doi = {10.1109/IW_MSS59200.2023.10369668},

year  = {2023},

date = {2023-01-01},

journal = {2023 IEEE International Workshop on Mechatronics Systems Supervision, IW_MSS 2023},

abstract = {In this work, we focuses on the problem of designing an interval state estimation for continuous-time Switched Linear Systems (SLS) in the Unknown But Bounded Error (UBBE) context. To do so, we design a new structure of interval observers by introducing weighted matrices not only to give more degrees of design freedom but also to attenuate the conservatism caused by uncertainties. Observer gains are derived from the solution of Linear Matrix Inequalities (LMIs), based on the use of a common Lyapunov function, to ensure cooperativity and stability. An L∞ technique is then introduced to compensate the measurement noise and disturbances' effects and to enhance the precision of interval estimation. Finally, numerical simulations are given, evaluating the proposed methodology and demonstrating its effectiveness. © 2023 IEEE.},

keywords = {Bounded error context, Continous time, Continuous time systems, Continuous-time switched system, Interval observers, Linear matrix inequalities, Linear systems, Lyapunov functions, L∞ technique, matrix, Set-membership estimation, State estimation, Switched linear system, Switched system, Unknown but bounded},

pubstate = {published},

tppubtype = {conference}

}

@conference{Ethabet2023,

title = {L∞ Set-membership Estimation for Continuous-time Switched Linear Systems},

author = {Haifa Ethabet and Leila Dadi and Tarek Raissi and Mohamed Aoun},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182931405\&doi=10.1109%2fIW_MSS59200.2023.10369668\&partnerID=40\&md5=2fb780affae1b8628f3f526c9cabfef7},

doi = {10.1109/IW_MSS59200.2023.10369668},

year  = {2023},

date = {2023-01-01},

journal = {2023 IEEE International Workshop on Mechatronics Systems Supervision, IW_MSS 2023},

abstract = {In this work, we focuses on the problem of designing an interval state estimation for continuous-time Switched Linear Systems (SLS) in the Unknown But Bounded Error (UBBE) context. To do so, we design a new structure of interval observers by introducing weighted matrices not only to give more degrees of design freedom but also to attenuate the conservatism caused by uncertainties. Observer gains are derived from the solution of Linear Matrix Inequalities (LMIs), based on the use of a common Lyapunov function, to ensure cooperativity and stability. An L∞ technique is then introduced to compensate the measurement noise and disturbances' effects and to enhance the precision of interval estimation. Finally, numerical simulations are given, evaluating the proposed methodology and demonstrating its effectiveness. © 2023 IEEE.},

keywords = {Bounded error context, Continous time, Continuous time systems, Continuous-time switched system, Interval observers, Linear matrix inequalities, Linear systems, Lyapunov functions, L∞ technique, matrix, Set-membership estimation, State estimation, Switched linear system, Switched system, Unknown but bounded},

pubstate = {published},

tppubtype = {conference}

}

@conference{Hamdi2013b,

title = {Interval state observer design for fractional systems},

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

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84883066070\&doi=10.1109%2fSSD.2013.6564115\&partnerID=40\&md5=9c0127447bc3a2d3dc3a3d8a2eb194fc},

doi = {10.1109/SSD.2013.6564115},

year  = {2013},

date = {2013-01-01},

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

abstract = {This paper presents a design method for interval state observer for fractional systems in a bounded-error context. A causal observer based on prediction-correction approach is proposed. The prediction part consists on a validated solving of an Initial Value Problem (IVP) for a Fractional Differential Equation (FDE) and the correction part uses set inversion algorithm. A numerical example is presented to show the effectiveness of the proposed design method. © 2013 IEEE.},

note = {Cited by: 2},

keywords = {Bounded errors, Design, Differential equations, Fractional differential equations, Fractional systems, Initial value problems, Interval analysis, observer, Observer-based, Prediction-correction, State estimation, State observer},

pubstate = {published},

tppubtype = {conference}

}

@conference{Aoun2012b,

title = {Continuous fractional Kalman filter},

author = {Mohamed Aoun and Slaheddine Najar and Moufida Abdelhamid and Mohamed Naceur Abdelkrim},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84861605131\&doi=10.1109%2fSSD.2012.6198068\&partnerID=40\&md5=3b5a3d80fe7b60e0ca04ee80eddf2959},

doi = {10.1109/SSD.2012.6198068},

year  = {2012},

date = {2012-01-01},

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

abstract = {This paper develops a new Kalman filter for linear systems described with continuous time fractional model. It extends the classical Kalman filter to deals with fractional differentiation. It is called continuous fractional Kalman Filter. The algorithm of the new filter is detailed and a suboptimal filter can be deduced. A numerical example illustrates the state estimation of a fractional model with the new filter. © 2012 IEEE.},

note = {Cited by: 4},

keywords = {Continuous time, Fractional differentiation, Fractional model, Kalman filters, Linear systems, Numerical example, State estimation, Suboptimal filter},

pubstate = {published},

tppubtype = {conference}

}

@conference{Aoun2012,

title = {Continuous fractional Kalman filter},

author = {Mohamed Aoun and Slaheddine Najar and Moufida Abdelhamid and Mohamed Naceur Abdelkrim},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84861605131\&doi=10.1109%2fSSD.2012.6198068\&partnerID=40\&md5=3b5a3d80fe7b60e0ca04ee80eddf2959},

doi = {10.1109/SSD.2012.6198068},

year  = {2012},

date = {2012-01-01},

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

abstract = {This paper develops a new Kalman filter for linear systems described with continuous time fractional model. It extends the classical Kalman filter to deals with fractional differentiation. It is called continuous fractional Kalman Filter. The algorithm of the new filter is detailed and a suboptimal filter can be deduced. A numerical example illustrates the state estimation of a fractional model with the new filter. © 2012 IEEE.},

note = {Cited by: 4},

keywords = {Continuous time, Fractional differentiation, Fractional model, Kalman filters, Linear systems, Numerical example, State estimation, Suboptimal filter},

pubstate = {published},

tppubtype = {conference}

}

@conference{Abdelhamid2009b,

title = {Discrete fractional Kalman filter},

author = {Moufida Abdelhamid and Mohamed Aoun and Slaheddine Najar and Mohamed Naceur Abdelhamid},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79955725313\&doi=10.3182%2f20090921-3-TR-3005.00090\&partnerID=40\&md5=13ac1eb2213053d7b1a48169500ba641},

doi = {10.3182/20090921-3-TR-3005.00090},

year  = {2009},

date = {2009-01-01},

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

volume = {2},

number = {PART 1},

abstract = {This paper presents a generalization of the classical discrete time Kalman filter algorithm to the case of the fractional systems. Motivations for the use of this filter are given and the algorithm is detailed. The document also shows a simple numerical example of linear state estimation. Copyright © 2007 International Federation of Automatic Control.},

note = {Cited by: 20; All Open Access, Bronze Open Access},

keywords = {Algorithms, Discrete time control systems, Discrete-time Kalman filters, Fractional differentiation, Fractional Kalman filter, Fractional kalman filters, Fractional systems, Intelligent control, Kalman filters, Linear state estimation, Numerical example, Signal processing, State estimation},

pubstate = {published},

tppubtype = {conference}

}

@conference{Abdelhamid2009,

title = {Discrete fractional Kalman filter},

author = {Moufida Abdelhamid and Mohamed Aoun and Slaheddine Najar and Mohamed Naceur Abdelhamid},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79955725313\&doi=10.3182%2f20090921-3-TR-3005.00090\&partnerID=40\&md5=13ac1eb2213053d7b1a48169500ba641},

doi = {10.3182/20090921-3-TR-3005.00090},

year  = {2009},

date = {2009-01-01},

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

volume = {2},

number = {PART 1},

abstract = {This paper presents a generalization of the classical discrete time Kalman filter algorithm to the case of the fractional systems. Motivations for the use of this filter are given and the algorithm is detailed. The document also shows a simple numerical example of linear state estimation. Copyright © 2007 International Federation of Automatic Control.},

note = {Cited by: 20; All Open Access, Bronze Open Access},

keywords = {Algorithms, Discrete time control systems, Discrete-time Kalman filters, Fractional differentiation, Fractional Kalman filter, Fractional kalman filters, Fractional systems, Intelligent control, Kalman filters, Linear state estimation, Numerical example, Signal processing, State estimation},

pubstate = {published},

tppubtype = {conference}

}