Publications

@conference{Rhouma2019219,

title = {State filtering with intermittent unknown input and retransmission delay},

author = {T. Rhouma and J. -Y. Keller and K. Chabir and D. Sauter and M. N. Abdelkrim},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075632832\&doi=10.1109%2fSSD.2019.8893284\&partnerID=40\&md5=18774b1a8900516332fdfe6dd65994de},

doi = {10.1109/SSD.2019.8893284},

isbn = {9781728118208},

year  = {2019},

date = {2019-01-01},

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

pages = {219-224},

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

abstract = {When input information of a physical plant is transmitted to the remote state estimator via unreliable networks, this paper proposes a resilient Kalman filter able to recover the unbiased minimum variance state prediction after a minimum number of successive correct receptions. This goal is reached by encoding input information so that the decoded state model of the plant satisfies a generalized form of output-separability conditions originally used to solve a multiple faults detection and isolation problem. © 2019 IEEE.},

note = {cited By 0},

keywords = {Cryptography; Networked control systems; Packet loss, Kalman filters, Minimum variance; Multiple faults; Physical plants; Retransmissions; State filtering; State modeling; State prediction; Unreliable network},

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 = {M. Aoun and S. Najar and M. Abdelhamid and M. N. 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},

isbn = {9781467315906},

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; Numerical example; Suboptimal filter, Kalman filters, Linear systems; State estimation},

pubstate = {published},

tppubtype = {conference}

}

@conference{Chabir2010249,

title = {Two-stage Kalman filter for simultaneous fault and delay estimation in Networked Control Systems},

author = {K. Chabir and D. Sauter and I. M. Al-Salami and M. N. Abdelkrim},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-77957011966\&doi=10.1109%2fMED.2010.5547676\&partnerID=40\&md5=e596a2272aa407e5afc2a266cb5e429a},

doi = {10.1109/MED.2010.5547676},

isbn = {9781424480920},

year  = {2010},

date = {2010-01-01},

journal = {18th Mediterranean Conference on Control and Automation, MED'10 - Conference Proceedings},

pages = {249-254},

abstract = {Networked Control Systems are becoming very popular nowadays. However, the use of networks in the control loop induces extra difficulties in diagnosis. In this paper we propose a method for estimate fault as well delay induced by the network. Inspired from the Kalman filter previously proposed by Sinopoli et al. and Hsieh et al., two solutions are presented; the augmented and the two-stage Kalman filter. Main attention is paid for the last one. The delay and the fault are integrated as a state variable. It is supposed that the delay and the fault are random variables. In addition, we provide some analytic results to demonstrate the computational advantages of two-stage Kalman filter over augmented ones. An example illustrates the obtained results is given. © 2010 IEEE.},

note = {cited By 7},

keywords = {Computational advantages; Control loop; Delay estimation; Networked control systems; Simultaneous faults; State variables; Two stage Kalman filters, Control system analysis; Control systems; Random variables, Kalman filters},

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}

}