2022 |
Lamouchi, Rihab; Amairi, Messaoud; Raissi, Tarek; Aoun, Mohamed Robust Fault Detection based on Zonotopic Observers for Linear Parameter Varying Systems Conférence 2022, (Cited by: 1). Résumé | Liens | BibTeX | Étiquettes: Discrete time, Fault detection, Faults detection, Linear parameter varying systems, Linear systems, Measurement Noise, Numerical methods, Performance, Robust fault detection, Sensors faults, Uncertainty, Unknown but bounded, Zonotopes @conference{Lamouchi2022773b,In this paper, zonotopic fault detection methodology is proposed for a class of discrete-Time Linear Parameter Varying (LPV) systems with sensor faults. The disturbances and measurement noise are assumed to be unknown but bounded by zonotope. First, a fault detection observer is designed based on L? performance to attenuate the effects of the uncertainties and to improve the accuracy of the proposed residual framers. Then, the fault sensitivity is taken into account by measuring H-performance and zonotopic residual evaluation is presented. Finally, the effectiveness of the proposed method is demonstrated by a numerical example. © 2022 IEEE. |
Yakoub, Zaineb; Naifar, Omar; Amairi, Messaoud; Chetoui, Manel; Aoun, Mohamed; Makhlouf, Abdellatif Ben A Bias-Corrected Method for Fractional Linear Parameter Varying Systems Article de journal Dans: Mathematical Problems in Engineering, vol. 2022, 2022, (Cited by: 1; All Open Access, Gold Open Access). Résumé | Liens | BibTeX | Étiquettes: Bias correction, Correction techniques, Fractional model, Fractional order, Identification algorithms, LeastSquare algorithm, Linear parameter varying systems, Linear programming, Linear systems, Nelder-Mead simplex methods, Performance, Reliable results @article{Yakoub2022e,This paper proposes an identification algorithm for the fractional Linear Parameter Varying (LPV) system considering noisy scheduling and output measurements. A bias correction technique is provided in order to compensate for the bias caused by the least squares algorithm. This approach was created to estimate either coefficients or fractional-order differentiation, and it has been proven to produce unbiased and reliable results. The suggested method’s performance is assessed by the identification of two fractional models and was compared with Nelder-Mead Simplex method. © 2022 Zaineb Yakoub et al. |
2020 |
Gasmi, Noussaiba; Boutayeb, Mohamed; Thabet, Assem; Aoun, Mohamed; Frej, Ghazi Bel Haj Robust sliding window observer-based controller design for Lipschitz discrete-time systems Conférence vol. 53, no. 2, 2020, (Cited by: 1; All Open Access, Bronze Open Access). Résumé | Liens | BibTeX | Étiquettes: Controllers, Digital control systems, Discrete – time systems, Discrete time control systems, H ∞ criterion, Lipschitz, Lipschitz non-linearity, Observer-based, Observer-based controllers, Observer-based stabilization design, Performance, Sliding Window, Sliding window approach, Stabilization, Uncertain systems @conference{Gasmi20205970b,The aim of this paper is to develop a new observer-based stabilization strategy for a class of Lipschitz uncertain systems. This new strategy improves the performances of existing methods and ensures better convergence conditions. The observer and the controller are enriched with sliding windows of measurements and estimated states, respectively. This technique allows to increase the number of decision variables and thus get less restrictive and more general LMI conditions. The established sufficient stability conditions are in the form of Bilinear Matrix Inequality (BMI). The obtained constraint is transformed, through a useful approach, to a more suitable one easily tractable by standard software algorithms. Numerical example is given to illustrate the performances of the proposed approach. Copyright © 2020 The Authors. This is an open access article under the CC BY-NC-ND license |
Gasmi, Noussaiba; Boutayeb, Mohamed; Thabet, Assem; Aoun, Mohamed; Frej, Ghazi Bel Haj Robust sliding window observer-based controller design for Lipschitz discrete-time systems Conférence vol. 53, no. 2, 2020, (Cited by: 1; All Open Access, Bronze Open Access). Résumé | Liens | BibTeX | Étiquettes: Controllers, Digital control systems, Discrete – time systems, Discrete time control systems, H ∞ criterion, Lipschitz, Lipschitz non-linearity, Observer-based, Observer-based controllers, Observer-based stabilization design, Performance, Sliding Window, Sliding window approach, Stabilization, Uncertain systems @conference{Gasmi20205970,The aim of this paper is to develop a new observer-based stabilization strategy for a class of Lipschitz uncertain systems. This new strategy improves the performances of existing methods and ensures better convergence conditions. The observer and the controller are enriched with sliding windows of measurements and estimated states, respectively. This technique allows to increase the number of decision variables and thus get less restrictive and more general LMI conditions. The established sufficient stability conditions are in the form of Bilinear Matrix Inequality (BMI). The obtained constraint is transformed, through a useful approach, to a more suitable one easily tractable by standard software algorithms. Numerical example is given to illustrate the performances of the proposed approach. Copyright © 2020 The Authors. This is an open access article under the CC BY-NC-ND license |
Publications
2022 |
Robust Fault Detection based on Zonotopic Observers for Linear Parameter Varying Systems Conférence 2022, (Cited by: 1). |
A Bias-Corrected Method for Fractional Linear Parameter Varying Systems Article de journal Dans: Mathematical Problems in Engineering, vol. 2022, 2022, (Cited by: 1; All Open Access, Gold Open Access). |
2020 |
Robust sliding window observer-based controller design for Lipschitz discrete-time systems Conférence vol. 53, no. 2, 2020, (Cited by: 1; All Open Access, Bronze Open Access). |
Robust sliding window observer-based controller design for Lipschitz discrete-time systems Conférence vol. 53, no. 2, 2020, (Cited by: 1; All Open Access, Bronze Open Access). |