News
Forschungsaufenthalt und Oberseminar-Vortrag von Lokman Rachid Melhani „Modelling, Estimation, and Nonlinear Model Predictive Control of Pandemic Events“
Montag, der 19. Januar 2026 um 10:00 Uhr
Am Montag, dem 19. Januar 2026 um 10:00 Uhr spricht im Seminarraum S 78, Gebäude NW II.
Herr M.Sc. Lokman Rachid Melhani [en]
Nationales Doktorandenkolleg “Dottorato di Ricerca Nazionale in Medicina di Precisione” [it]
unter Betreuung von Prof. Antonino Sferlazza (Dipartimento di Ingegneria / Department of Engineering)
Dipartimento “Medicina di Precisione in Area Medica, Chirurgica e Critica” [it]
Università degli Studi di Palermo [en], Palermo, Italien
(Gast am Lehrstuhl für Angewandte Mathematik
bei Herrn Prof. Dr. Lars Grüne)
im Rahmen des
Oberseminars "Numerische Mathematik, Optimierung und Dynamische Systeme"
über das Thema
„Modelling, Estimation, and Nonlinear Model Predictive Control of Pandemic Events“.
Lokman Rachid Melhani ist vom 2. Januar - 31. August 2026 Gast des Lehrstuhls für einen Forschungsaufenthalt.
In dieser Zeit ist er im Büro 3.2.01.544 erreichbar.
Seine wissenschaftlichen Arbeitsfelder liegen im Bereich
nichlineare Systeme, Entwurf von Beobachtern, Verfahren zur Zustandsschätzung (wie z.B. Kalman-Filter), Kontrollsysteme und
mathematische Modellierung komplexer dynamischer Systeme sowie neuerdings die Anwendung von nichtlinearer modellprädiktiver
Regelung zur Modellierung und Schätzung von Pandemien.
ABSTRACT:
In this talk, I will present a control-oriented framework for pandemic management combining nonlinear epidemiological modelling,
state estimation, and optimal control.
Starting from a nine-compartment model, I will show how partial epidemic measurements can be fused using an Extended Kalman Filter to reconstruct unmeasured states. Based on these estimates, I will discuss the formulation of the pandemic mitigation problem as a constrained optimal control problem. A comparison is made between classical NMPC and an Economic MPC formulation, where the objective directly penalizes health and economic costs rather than tracking a reference trajectory.
Finally, I will outline ongoing and future research directions, including ENMPC with feasibility constraints and economic performance guarantees.
Starting from a nine-compartment model, I will show how partial epidemic measurements can be fused using an Extended Kalman Filter to reconstruct unmeasured states. Based on these estimates, I will discuss the formulation of the pandemic mitigation problem as a constrained optimal control problem. A comparison is made between classical NMPC and an Economic MPC formulation, where the objective directly penalizes health and economic costs rather than tracking a reference trajectory.
Finally, I will outline ongoing and future research directions, including ENMPC with feasibility constraints and economic performance guarantees.