Automation department seminar
You are cordially invited to attend the ROC group seminar during which there will be 2 presentations:
- 15:30: Lassaad Zaway, ATER UPHF
- 16:15: Hasni Arezki, Post-doc RITMEA
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Le 13/02/2026
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15:30 - 17:00
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Seminar
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Mont Houy Campus
Bâtiment Claudin le Jeune 1
amphi E2
Lassaad Zaway - ATER UPHF
"Development of a technique to control an electric wheelchair using the fusion of EEG signals and facial image processing"
Abstract
This presentation deals with the robust stability analysis of moving horizon estimation (MHE) for a class of nonlinear systems.
New mathematical tools are introduced, enabling the development of new design conditions for optimizing the cost function parameters of the MHE scheme.
These conditions are closely related to the MHE window size and the incremental coefficients of exponential input/output-state stability (i-EIOSS) of the system. To improve MHE robustness while minimizing window size, advanced prediction techniques are proposed. In addition, innovative methods based on linear LMIs are presented to synthesize i-EIOSS coefficients and prediction gains.
The effectiveness of the proposed prediction methods is validated using numerical examples, which highlight their performance improvements.
Hasni Arezki - Post-doc RITMEA
"Advanced robust moving-horizon estimation schemes for nonlinear systems"
Abstract
This presentation deals with the robust stability analysis of moving horizon estimation (MHE) for a class of nonlinear systems.
New mathematical tools are presented, enabling the development of new design conditions to optimize the cost function parameters of the MHE scheme.
These conditions are closely related to the MHE window size and the incremental coefficients of exponential input/output-state stability (i-EIOSS) of the system. In order to improve the robustness of MHE while minimizing the window size, advanced prediction techniques are proposed.
In addition, innovative methods based on linear LMI are presented to synthesize i-EIOSS coefficients and prediction gains.
The effectiveness of the proposed prediction methods is validated using numerical examples, which highlight their performance improvements.