17 citations · 56 across the 26 of their papers we have counts for
31 papers
A Unified Efficient Gradient-Based Heuristic For Box-Constrained Expectation-Related and Risk-Averse Stochastic Optimization Problems
Mazen Alamir
This paper presents a new algorithm addressing the problem of stochastic optimization where the cost function depends on a vector of uncertain parameters with known statistics. The…
A Nonlinear Model Predictive Control Perspective on Gradient-Based Optimization: A New Efficient, Parameter-Free and Provably Stable Algorithm
Mazen Alamir
This paper discusses some aspects related to gradient-based optimization algorithms with special focus on the requirements associated to their use in the implementation of Nonlinea…
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…
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…
On the use of Anderson acceleration in hierarchical control
Xuan-Huy Pham, Mazen Alamir, François Bonne +1
This paper investigates the use of fixed-point Anderson acceleration method (AA) to a recently proposed hierarchical control framework. Due to its model-free property, the AA-based…
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…
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…
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…
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…
On the use of Data-Driven Cost Function Identification in Parametrized NMPC
Mazen Alamir
In this paper, a framework with complete numerical investigation is proposed regarding the feasibility of constrained Nonlinear Model Predictive Control (NMPC) design using Data-Dr…
A new formulation of Economic Model Predictive Control without terminal constraint
Mazen Alamir, Gabriele Pannochia
In this paper, it is shown that a simple formulation of Economic Model Predictive Control can be used which possesses two features that are generally viewed as mutually exclusive,…
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…
Explicit approximation of stochastic optimal feedback control for combined therapy of cancer
Mazen Alamir
In this paper, a tractable methodology is proposed to approximate stochastic optimal feedback treatment in the context of mixed immuno-chemo therapy of cancer. The method uses a fi…
On the use of supervised clustering in stochastic NMPC design
Mazen Alamir
In this paper, a supervised clustering based-heuristic is proposed for the real-time implementation of approximate solutions to stochastic nonlinear model predictive control framew…
Computing control invariant sets in high dimension is easy
Mirko Fiacchini, Mazen Alamir
In this paper we consider the problem of computing control invariant sets for linear controlled high-dimensional systems with constraints on the input and on the states. Set inclus…
Computing control invariant sets is easy
Mirko Fiacchini, Mazen Alamir
In this paper we consider the problem of computing control invariant sets for linear controlled systems with constraints on the input and on the states. We focus in particular on t…
The pdf-mpc Package: A Free-Matlab-Coder package for Real-Time Nonlinear Model Predictive Control
Mazen Alamir
This paper describes the Parametrized Derivative-Free Model Predictive Control pdf-mpc package, a matlab coder-based set of subroutines that enables a model predictive control prob…
Useful redundancy in parameter and time delay estimation for continuous-time models
Huong Ha, James S. Welsh, Mazen Alamir
This paper proposes an algorithm to estimate the parameters, including time delay, of continuous time systems based on instrumental variable identification methods. To overcome the…
Fixed-point Based Hierarchical MPC Control Design For a Cryogenic Refrigerator
M. Alamir, P. Bonnay, F. Bonne +1
In this paper, a simple and general hierarchical control framework is proposed and validated through the interconnection of the Joule-Thompson and the Brayton cycle stages of a cry…
Stability proof for nonlinear MPC design using monotonically increasing weighting profiles without terminal constraints
Mazen Alamir
In this note, a new formulation of Model Predictive Control (MPC) framework with no stability-related terminal constraint is proposed and its stability is proved under mild standar…
Approximate explicit model predictive control via piecewise nonlinear system identification
Van-Vuong Trinh, Mazen Alamir, Patrick Bonnay
This article presents an identification methodology to capture general relationships, with application to piecewise nonlinear approximations of model predictive control for constra…
Output Feedback Control of a Class of Uncertain Systems Under Control-derivative dependent disturbances
Mazen Alamir, Jean Dobrowolski, Amgad tarek Mohammed
This paper addresses the design of robust dynamic output feedback control for highly uncertain systems in which the unknown disturbance might be excited by the derivative of the co…
A New Contraction-Based NMPC Formulation Without Stability-Related terminal Constraints
Mazen Alamir
Contraction-Based Nonlinear Model Predictive Control (NMPC) formulations are attractive because of the generally short prediction horizons they require and the needless use of term…
A State-Dependent Updating Period For Certified Real-Time Model Predictive Control
Mazen Alamir
In this paper, a state-dependent control updating period framework is proposed that leads to real-time implementable Model Predictive Control with certified practical stability res…