activity
20202022
most citedA Heuristic for Dynamic Output Predictive Control Design for Uncertain Nonlinear Systems

2 citations · 5 across the 6 of their papers we have counts for

collaborators

9 papers

eess.SY20221 cited

Learning-Based sensitivity analysis and feedback design for drug delivery of mixed therapy of cancer in the presence of high model uncertainties

Mazen Alamir

In this paper, a methodology is proposed that enables to analyze the sensitivity of the outcome of a therapy to unavoidable high dispersion of the patient specific parameters on on…

eess.SY2022

Investigation of fast-NMPC and deep learning approach in fixed-point-based hierarchical control

Xuan-Huy Pham, Mazen Alamir, François Bonne

This paper explores some variations of a hierarchical control framework that has been recently proposed. The framework is dedicated to control a network of interconnected subsystem…

eess.SY20212 cited

A generic fixed-point iteration-based hierarchical control design: Application to a cryogenic process

Xuan-Huy Pham, Mazen Alamir, François Bonne +1

This paper presents an extension of a recently proposed hierarchical control framework applied to a cryogenic system. While in the previous work, each sub-system in the decompositi…

eess.SY20212 cited

A Heuristic for Dynamic Output Predictive Control Design for Uncertain Nonlinear Systems

Mazen Alamir

In this paper, a simple heuristic is proposed for the design of uncertainty aware predictive controllers for nonlinear models involving uncertain parameters. The method relies on M…

stat.AP2020

The Ockham's razor applied to COVID-19 model fitting French data

Mirko Fiacchini, Mazen Alamir

This paper presents a data-based simple model for fitting the available data of the Covid-19 pandemic evolution in France. The time series concerning the 13 regions of mainland Fra…

eess.SY2020

Partial Extended Observability Certification and Optimal Design of Moving Horizon Estimators Under Uncertainties

Mazen Alamir

This paper addresses the observability analysis and the optimal design of observation parameters in the presence of noisy measurements and parametric uncertainties. The main underl…