Publications

@conference{Dabbaghi2023b,

title = {Estimating and enlarging the domain of attraction for a nonlinear system with input saturation},

author = {Boudour Dabbaghi and Faical Hamidi and Houssem Jerbi and Mohamed Aoun},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182924575\&doi=10.1109%2fIW_MSS59200.2023.10368596\&partnerID=40\&md5=672e47539c960bce9ba5743f75dc5c50},

doi = {10.1109/IW_MSS59200.2023.10368596},

year  = {2023},

date = {2023-01-01},

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

abstract = {This paper focuses on the stabilization problem of a nonlinear system subject to actuator saturation. Such that the results are based on the differential algebraic representation and use of a convex hull description subject to the saturation effects. The contribution of this work is to estimate enlarging domain of attraction. Therefore, for find the largess domain of attraction, the block matrix-variable will be chosen. Numerical examples are provided to illustrate the efficiency of this new approach. © 2023 IEEE.},

keywords = {Actuator saturations, Algebra, Algebraic representations, Computational geometry, Convex hull, Differential algebraic, Differential algebraic representation, Domain of attraction, Input saturation, Nonlinear, Nonlinear systems, Stabilization problems},

pubstate = {published},

tppubtype = {conference}

}

@conference{Aloui2023b,

title = {Estimating and enlarging the domain of attraction for polynomial systems using a deep learning tool},

author = {Messaoud Aloui and Faical Hamidi and Houssem Jerbi and Mohamed Aoun},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182927297\&doi=10.1109%2fIW_MSS59200.2023.10369522\&partnerID=40\&md5=244fee6b04ee656e55d354fe1fac648e},

doi = {10.1109/IW_MSS59200.2023.10369522},

year  = {2023},

date = {2023-01-01},

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

abstract = {This Paper deals with the topic of non linear polynomial systems. It explains a way to estimate and enlarge the region of attraction of nonlinear polynomial systems. It provides a deep learning method for estimating the domain of attraction and uses the Particle Swarm Optimization Algorithm to enlarge this domain. Based on an analytic method found in literature, a dataset is generated, used then to train an artificial neural network, which will be an objective function of an optimization algorithm. This method dives an imitation to a previous complicated method, with less complexity and les elapsed time. The benchmark examples show the efficiency of the method and compare results with those obtained with the one using linear matrix inequalities. © 2023 IEEE.},

keywords = {Deep learning, Domain of attraction, Learning systems, Learning tool, Linear matrix inequalities, Linear polynomials, Lyapunov functions, Lyapunov's functions, Neural networks, Non linear, Particle swarm, Particle swarm optimization, Particle swarm optimization (PSO), Polynomial systems, Polynomials, Swarm intelligence, Swarm optimization},

pubstate = {published},

tppubtype = {conference}

}

@conference{Dabbaghi2023,

title = {Estimating and enlarging the domain of attraction for a nonlinear system with input saturation},

author = {Boudour Dabbaghi and Faical Hamidi and Houssem Jerbi and Mohamed Aoun},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182924575\&doi=10.1109%2fIW_MSS59200.2023.10368596\&partnerID=40\&md5=672e47539c960bce9ba5743f75dc5c50},

doi = {10.1109/IW_MSS59200.2023.10368596},

year  = {2023},

date = {2023-01-01},

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

abstract = {This paper focuses on the stabilization problem of a nonlinear system subject to actuator saturation. Such that the results are based on the differential algebraic representation and use of a convex hull description subject to the saturation effects. The contribution of this work is to estimate enlarging domain of attraction. Therefore, for find the largess domain of attraction, the block matrix-variable will be chosen. Numerical examples are provided to illustrate the efficiency of this new approach. © 2023 IEEE.},

keywords = {Actuator saturations, Algebra, Algebraic representations, Computational geometry, Convex hull, Differential algebraic, Differential algebraic representation, Domain of attraction, Input saturation, Nonlinear, Nonlinear systems, Stabilization problems},

pubstate = {published},

tppubtype = {conference}

}

@conference{Aloui2023,

title = {Estimating and enlarging the domain of attraction for polynomial systems using a deep learning tool},

author = {Messaoud Aloui and Faical Hamidi and Houssem Jerbi and Mohamed Aoun},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182927297\&doi=10.1109%2fIW_MSS59200.2023.10369522\&partnerID=40\&md5=244fee6b04ee656e55d354fe1fac648e},

doi = {10.1109/IW_MSS59200.2023.10369522},

year  = {2023},

date = {2023-01-01},

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

abstract = {This Paper deals with the topic of non linear polynomial systems. It explains a way to estimate and enlarge the region of attraction of nonlinear polynomial systems. It provides a deep learning method for estimating the domain of attraction and uses the Particle Swarm Optimization Algorithm to enlarge this domain. Based on an analytic method found in literature, a dataset is generated, used then to train an artificial neural network, which will be an objective function of an optimization algorithm. This method dives an imitation to a previous complicated method, with less complexity and les elapsed time. The benchmark examples show the efficiency of the method and compare results with those obtained with the one using linear matrix inequalities. © 2023 IEEE.},

keywords = {Deep learning, Domain of attraction, Learning systems, Learning tool, Linear matrix inequalities, Linear polynomials, Lyapunov functions, Lyapunov's functions, Neural networks, Non linear, Particle swarm, Particle swarm optimization, Particle swarm optimization (PSO), Polynomial systems, Polynomials, Swarm intelligence, Swarm optimization},

pubstate = {published},

tppubtype = {conference}

}

@conference{Jerbi2022b,

title = {Computing the Domain of Attraction using Numerical Techniques},

author = {Houssem Jerbi and Boudour Dabbagui and Faical Hamidi and Mohamad Aoun and Yassine Bouazzi and Sondess Ben Aoun},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134079276\&doi=10.1109%2fICAAID51067.2022.9799499\&partnerID=40\&md5=74612f4dcc28a8c7f1c2bfe63a8c27d5},

doi = {10.1109/ICAAID51067.2022.9799499},

year  = {2022},

date = {2022-01-01},

journal = {Proceedings - 2022 4th International Conference on Applied Automation and Industrial Diagnostics, ICAAID 2022},

abstract = {Stability analysis of controlled nonlinear systems is a problem of fundamental importance in system engineering. This paper elaborates an explicit numerical technique to maximize a quadratic Lyapunov function for the class of polynomial nonlinear models. Using the computed Lyapunov function an enlarged subsets of the basin of attraction of an asymptotically stable equilibrium can be computed in an iterative analytical way. We mainly use the Carleman linearization technique that converts a nonlinear autonomous system of finite dimension into an equivalent linear infinite dimension one. We implement the sampling technique as a numerical tool allowing the maximization of estimated regions of attraction. An example is given to demonstrate the efficiency of the proposed approach. The numerical study analysis of the designed scheme is led using the Matlab software environment. © 2022 IEEE.},

note = {Cited by: 0},

keywords = {Asymptotically stable equilibrium, Basins of attraction, Carleman linearization, Domain of attraction, Iterative methods, Linearization, Lyapunov functions, Lyapunov's functions, Lypaunov functions, MATLAB, Non-linear modelling, Nonlinear systems, Numerical methods, Numerical techniques, Quadratic lyapunov function, Stability analyze, System stability},

pubstate = {published},

tppubtype = {conference}

}

@conference{Jerbi2022,

title = {Computing the Domain of Attraction using Numerical Techniques},

author = {Houssem Jerbi and Boudour Dabbagui and Faical Hamidi and Mohamad Aoun and Yassine Bouazzi and Sondess Ben Aoun},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134079276\&doi=10.1109%2fICAAID51067.2022.9799499\&partnerID=40\&md5=74612f4dcc28a8c7f1c2bfe63a8c27d5},

doi = {10.1109/ICAAID51067.2022.9799499},

year  = {2022},

date = {2022-01-01},

journal = {Proceedings - 2022 4th International Conference on Applied Automation and Industrial Diagnostics, ICAAID 2022},

abstract = {Stability analysis of controlled nonlinear systems is a problem of fundamental importance in system engineering. This paper elaborates an explicit numerical technique to maximize a quadratic Lyapunov function for the class of polynomial nonlinear models. Using the computed Lyapunov function an enlarged subsets of the basin of attraction of an asymptotically stable equilibrium can be computed in an iterative analytical way. We mainly use the Carleman linearization technique that converts a nonlinear autonomous system of finite dimension into an equivalent linear infinite dimension one. We implement the sampling technique as a numerical tool allowing the maximization of estimated regions of attraction. An example is given to demonstrate the efficiency of the proposed approach. The numerical study analysis of the designed scheme is led using the Matlab software environment. © 2022 IEEE.},

note = {Cited by: 0},

keywords = {Asymptotically stable equilibrium, Basins of attraction, Carleman linearization, Domain of attraction, Iterative methods, Linearization, Lyapunov functions, Lyapunov's functions, Lypaunov functions, MATLAB, Non-linear modelling, Nonlinear systems, Numerical methods, Numerical techniques, Quadratic lyapunov function, Stability analyze, System stability},

pubstate = {published},

tppubtype = {conference}

}