activity
20162024
most citedSet Membership identification of linear systems with guaranteed simulation accuracy

37 citations · 42 across the 9 of their papers we have counts for

collaborators
Showing 2020Show all

6 papers · 1 filter

eess.SY2020

Hierarchical routing control in discrete manufacturing plants via model predictive path allocation and greedy path following

Lorenzo Fagiano, Marko Tanaskovic, Lenin Cucas Mallitasig +2

The problem of real-time control and optimization of components' routing in discrete manufacturing plants, where distinct items must undergo a sequence of jobs, is considered. This…

math.OC2020

SMGO: A Set Membership Approach to Data-Driven Global Optimization

Lorenzo Sabug, Fredy Ruiz, Lorenzo Fagiano

Many science and engineering applications feature non-convex optimization problems where the objective function can not be handled analytically, i.e. it is a black box. Examples in…

math.OC2020

Data-driven filtering for linear systems using Set Membership multistep predictors

Marco Lauricella, Lorenzo Fagiano

This paper presents a novel data-driven, direct filtering approach for unknown linear time-invariant systems affected by unknown-but-bounded measurement noise. The proposed techniq…

eess.SY2020

Control of a Rigid Wing Pumping Airborne Wind Energy System in all Operational Phases

Davide Todeschini, Lorenzo Fagiano, Claudio Micheli +1

The control design of an airborne wind energy system with rigid aircraft, vertical take-off and landing, and pumping operation is described. A hierarchical control structure is imp…

eess.SY2020

LiDAR-Based Navigation of Tethered Drone Formations in an Unknown Environment

Michele Bolognini, Lorenzo Fagiano

The problem of navigating a formation of interconnected tethered drones, named STEM (System of TEthered Multicopters), in an unknown environment is considered. The tethers feed ele…

math.OC2020★ 37 cited

Set Membership identification of linear systems with guaranteed simulation accuracy

Marco Lauricella, Lorenzo Fagiano

The problem of model identification for linear systems is considered, using a finite set of sampled data affected by a bounded measurement noise, with unknown bound. The objective…