2017 |
Yakoub, Z.; Chetoui, M.; Amairi, M.; Aoun, M. 2017, (Cited by: 0). Résumé | Liens | BibTeX | Étiquettes: Automation, Closed loops, Direct approach, Fractional differentiation, Identification (control systems), indirect approach, Least Square, Least squares approximations, Process control @conference{Yakoub2017271b, This paper deals with the fractional closed-loop system identification. A comparison between the direct and the indirect approach is processed. The fractional order bias eliminated least squares method is used to identify the fractional closed-loop transfer function. This method is founded on the ordinary least squares method and the state variable filter. A numerical example is treated to show the efficiency of each approach and discuss results. © 2016 IEEE. |
2015 |
Yakoub, Z.; Chetoui, M.; Amairi, M.; Aoun, M. 2015, (Cited by: 2). Résumé | Liens | BibTeX | Étiquettes: Algorithms, Closed loop systems, Closed loops, Continuous time systems, Continuous-time, Direct approach, Fractional differentiation, Identification (control systems), Least Square, Least squares approximations, Nonlinear programming, Numerical methods, Optimization, Religious buildings, State-variable filters @conference{Yakoub2015e, The paper deals with the continuous-time fractional closed-loop system identification in a noisy output context. Both coefficients and fractional orders of the process are estimated using the direct approach. The proposed method is based on the least squares technique and the state variable filter. It is an extension of the bias eliminated least squares method to the fractional systems. It is combined to a nonlinear optimization algorithm in order to estimate both coefficients and fractional orders of the fractional process. A numerical example is presented to illustrate the performances of the proposed methods. © 2015 IEEE. |
Publications
2017 |
2017, (Cited by: 0). |
2015 |
2015, (Cited by: 2). |