activity
20202026
most citedToward simple "in silico" experiments for drugs administration in some cancer treatments

10 citations · 10 across the 5 of their papers we have counts for

collaborators

7 papers

eess.SY2026

Linear Stochastic Systems with i.i.d. uncertainties: Exact Covariance Characterization, Stability Analysis and State-feedback Design

Kaouther Moussa, Dimitri Peaucelle, Yohei Hosoe +1

This paper studies linear discrete-time systems affected by independent and identically distributed (i.i.d.) multiplicative uncertainties and additive noise. It establishes the mai…

eess.SP2025

How is remifentanil dosed without dedicated indicator?

Bob Aubouin-Pairault, Mazen Alamir, Benjamin Meyer +2

This study investigates the paradigm of intraoperative analgesic dosage using a data-driven approach based on retrospective clinical data. Remifentanil, an analgesic widely used du…

eess.SY2025

MPC for tracking for anesthesia dynamics

Maxim Raymond, Kaouther Moussa, Mirko Fiacchini +1

In this paper, an MPC for tracking formulation is proposed for the control of anesthesia dynamics. It seamlessly enables the optimization of the steady-states pair that is not uniq…

eess.SY2025

Covariance Stabilization for a class of Stochastic Discrete-time Linear Systems using the S-Variable Approach

Kaouther Moussa, Dimitri Peaucelle

This paper deals with the problem of covariance stabilization for a class of linear stochastic discrete-time systems in the Stochastic Model Predictive Control (SMPC) framework. Th…

eess.SY2025

Covariance Propagation and Stabilization for Tube-Based Stochastic MPC under Parametric and Additive Uncertainties

Kaouther Moussa, Mirko Fiacchini

This work addresses an SMPC-oriented characterization of the error covariance dynamics for linear discrete-time systems subject to both additive and parametric stochastic uncertain…

eess.SY2021★ 10 cited

Toward simple "in silico" experiments for drugs administration in some cancer treatments

Michel Fliess, Cédric Join, Kaouther Moussa +2

We present some "in silico" experiments to design combined chemo- and immunotherapy treatment schedules. We introduce a new framework by combining flatness-based control, which is…

eess.SY2020

Probabilistically Certified Region of Attraction of a Tumor Growth Model with Combined Chemo- and Immunotherapy

Kaouther Moussa, Mirko Fiacchini, Mazen Alamir

This paper deals with the estimation of regions of attraction (RoAs) under parametric uncertainties for a cancer growth model with combined therapies. We propose a framework of pro…