2023 |
Ethabet, Haifa; Dadi, Leila; Raissi, Tarek; Aoun, Mohamed L∞ Set-membership Estimation for Continuous-time Switched Linear Systems Conférence 2023. Résumé | Liens | BibTeX | Étiquettes: 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 @conference{Ethabet2023b,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. |
Ethabet, Haifa; Dadi, Leila; Raissi, Tarek; Aoun, Mohamed L∞ Set-membership Estimation for Continuous-time Switched Linear Systems Conférence 2023. Résumé | Liens | BibTeX | Étiquettes: 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 @conference{Ethabet2023,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. |
2022 |
Victor, Stéphane; Mayoufi, Abir; Malti, Rachid; Chetoui, Manel; Aoun, Mohamed System identification of MISO fractional systems: Parameter and differentiation order estimation Article de journal Dans: Automatica, vol. 141, 2022, (Cited by: 10). Résumé | Liens | BibTeX | Étiquettes: Continous time, Continuous time systems, Fractional model, Fractional systems, Instrumental variables, Intelligent systems, Monte Carlo methods, Multiple input single output systems, Multiple inputs single outputs, Optimization, Optimization algorithms, Order estimation, Order optimizations, Parameter estimation, Religious buildings, System-identification @article{Victor2022b,This paper deals with continuous-time system identification of multiple-input single-output (MISO) fractional differentiation models. When differentiation orders are assumed to be known, coefficients are estimated using the simplified refined instrumental variable method for continuous-time fractional models extended to the MISO case. For unknown differentiation orders, a two-stage optimization algorithm is proposed with the developed instrumental variable for coefficient estimation and a gradient-based algorithm for differentiation order estimation. A new definition of structured-commensurability (or S-commensurability) is introduced to better cope with differentiation order estimation. Three variants of the algorithm are then proposed: (i) first, all differentiation orders are set as integer multiples of a global S-commensurate order, (ii) then, the differentiation orders are set as integer multiples of a local S-commensurate orders (one S-commensurate order for each subsystem), (iii) finally, all differentiation orders are estimated by releasing the S-commensurability constraint. The first variant has the smallest number of parameters and is used as a good initial hit for the second variant which in turn is used as a good initial hit for the third variant. Such a progressive increase of the number of parameters allows better performance of the optimization algorithm evaluated by Monte Carlo simulation analysis. © 2022 Elsevier Ltd |
Victor, Stéphane; Mayoufi, Abir; Malti, Rachid; Chetoui, Manel; Aoun, Mohamed System identification of MISO fractional systems: Parameter and differentiation order estimation Article de journal Dans: Automatica, vol. 141, 2022, (Cited by: 10). Résumé | Liens | BibTeX | Étiquettes: Continous time, Continuous time systems, Fractional model, Fractional systems, Instrumental variables, Intelligent systems, Monte Carlo methods, Multiple input single output systems, Multiple inputs single outputs, Optimization, Optimization algorithms, Order estimation, Order optimizations, Parameter estimation, Religious buildings, System-identification @article{Victor2022,This paper deals with continuous-time system identification of multiple-input single-output (MISO) fractional differentiation models. When differentiation orders are assumed to be known, coefficients are estimated using the simplified refined instrumental variable method for continuous-time fractional models extended to the MISO case. For unknown differentiation orders, a two-stage optimization algorithm is proposed with the developed instrumental variable for coefficient estimation and a gradient-based algorithm for differentiation order estimation. A new definition of structured-commensurability (or S-commensurability) is introduced to better cope with differentiation order estimation. Three variants of the algorithm are then proposed: (i) first, all differentiation orders are set as integer multiples of a global S-commensurate order, (ii) then, the differentiation orders are set as integer multiples of a local S-commensurate orders (one S-commensurate order for each subsystem), (iii) finally, all differentiation orders are estimated by releasing the S-commensurability constraint. The first variant has the smallest number of parameters and is used as a good initial hit for the second variant which in turn is used as a good initial hit for the third variant. Such a progressive increase of the number of parameters allows better performance of the optimization algorithm evaluated by Monte Carlo simulation analysis. © 2022 Elsevier Ltd |
Publications
2023 |
L∞ Set-membership Estimation for Continuous-time Switched Linear Systems Conférence 2023. |
L∞ Set-membership Estimation for Continuous-time Switched Linear Systems Conférence 2023. |
2022 |
System identification of MISO fractional systems: Parameter and differentiation order estimation Article de journal Dans: Automatica, vol. 141, 2022, (Cited by: 10). |
System identification of MISO fractional systems: Parameter and differentiation order estimation Article de journal Dans: Automatica, vol. 141, 2022, (Cited by: 10). |