2022 |
Jerbi, Houssem; Dabbagui, Boudour; Hamidi, Faical; Aoun, Mohamad; Bouazzi, Yassine; Aoun, Sondess Ben Computing the Domain of Attraction using Numerical Techniques Conférence 2022, (Cited by: 0). Résumé | Liens | BibTeX | Étiquettes: 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 @conference{Jerbi2022b,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. |
Jerbi, Houssem; Dabbagui, Boudour; Hamidi, Faical; Aoun, Mohamad; Bouazzi, Yassine; Aoun, Sondess Ben Computing the Domain of Attraction using Numerical Techniques Conférence 2022, (Cited by: 0). Résumé | Liens | BibTeX | Étiquettes: 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 @conference{Jerbi2022,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. |
2018 |
Hamdi, Saif Eddine; Amairi, Messaoud; Aoun, Mohamed Recursive set-membership parameter estimation of fractional systems using orthotopic approach Article de journal Dans: Transactions of the Institute of Measurement and Control, vol. 40, no. 15, p. 4185 – 4197, 2018, (Cited by: 5). Résumé | Liens | BibTeX | Étiquettes: Bounded error context, Bounded errors, Errors, Fractional systems, Fractional-order systems, Iterative algorithm, Iterative methods, Monte Carlo methods, Order estimation, Parameter estimation, Set membership approach, Unknown but bounded @article{Hamdi20184185b,In this paper, set-membership parameter estimation of linear fractional-order systems is addressed for the case of unknown-but-bounded equation error. In such bounded-error context with a-priori known noise bounds, the main goal is to characterize the set of all feasible parameters. This characterization is performed using an orthotopic strategy adapted for fractional system parameter estimation. In the case of a fractional commensurate system, an iterative algorithm is proposed to deal with commensurate-order estimation. The performances of the proposed algorithm are illustrated by a numerical example via a Monte Carlo simulation. © The Author(s) 2018. |
2012 |
Chetoui, Manel; Malti, Rachid; Thomassin, Magalie; Aoun, Mohamed; Najar, Slaheddine; Oustaloup, Alain; Abdelkrim, Mohamed Naceur EIV methods for system identification with fractional models Conférence vol. 16, no. PART 1, 2012, (Cited by: 14). Résumé | Liens | BibTeX | Étiquettes: Continuous time systems, Continuous-time, Cumulants, Differential equations, Errors in variables, Fractional SVF, Higher order statistics, Identification (control systems), Iterative, Iterative methods, Least Square, Monte Carlo methods, Religious buildings @conference{Chetoui20121641b,This paper deals with continuous-time system identification with fractional models in Errors-In-Variables context. Two estimators based on Higher-Order Statistics (third-order cumulants) are proposed. A State Variable Filter approach is extended to fractional orders to compute fractional derivatives of third-order cumulants estimates. The performance of the proposed algorithms is illustrated in a numerical example. Firstly, differentiation orders are fixed and differential equation coefficients are estimated. The consistency of the proposed estimators is evaluated through a study of the tuning parameter and Monte Carlo simulations. Then, the commensurate differentiation order is optimized along with the differential equation coefficients. © 2012 IFAC. |
Chetoui, Manel; Malti, Rachid; Thomassin, Magalie; Aoun, Mohamed; Najar, Slaheddine; Oustaloup, Alain; Abdelkrim, Mohamed Naceur EIV methods for system identification with fractional models Conférence vol. 16, no. PART 1, 2012, (Cited by: 14). Résumé | Liens | BibTeX | Étiquettes: Continuous time systems, Continuous-time, Cumulants, Differential equations, Errors in variables, Fractional SVF, Higher order statistics, Identification (control systems), Iterative, Iterative methods, Least Square, Monte Carlo methods, Religious buildings @conference{Chetoui20121641,This paper deals with continuous-time system identification with fractional models in Errors-In-Variables context. Two estimators based on Higher-Order Statistics (third-order cumulants) are proposed. A State Variable Filter approach is extended to fractional orders to compute fractional derivatives of third-order cumulants estimates. The performance of the proposed algorithms is illustrated in a numerical example. Firstly, differentiation orders are fixed and differential equation coefficients are estimated. The consistency of the proposed estimators is evaluated through a study of the tuning parameter and Monte Carlo simulations. Then, the commensurate differentiation order is optimized along with the differential equation coefficients. © 2012 IFAC. |
Publications
2022 |
Computing the Domain of Attraction using Numerical Techniques Conférence 2022, (Cited by: 0). |
Computing the Domain of Attraction using Numerical Techniques Conférence 2022, (Cited by: 0). |
2018 |
Recursive set-membership parameter estimation of fractional systems using orthotopic approach Article de journal Dans: Transactions of the Institute of Measurement and Control, vol. 40, no. 15, p. 4185 – 4197, 2018, (Cited by: 5). |
2012 |
EIV methods for system identification with fractional models Conférence vol. 16, no. PART 1, 2012, (Cited by: 14). |
EIV methods for system identification with fractional models Conférence vol. 16, no. PART 1, 2012, (Cited by: 14). |