activity
20182026
most citedOptimal Low-Thrust Orbit Transfers Made Easy: A Direct Approach

23 citations · 88 across the 11 of their papers we have counts for

collaborators

11 papers

eess.SY2026

Data-Driven Robust Predictive Control with Interval Matrix Uncertainty Propagation

Renato Quartullo, Andrea Garulli, Mirko Leomanni

This paper presents a new data-driven robust predictive control law, for linear systems affected by unknown-but-bounded process disturbances. A sequence of input-state data is used…

eess.SY2026

Robust Model Predictive Control for Linear Systems with Interval Matrix Model Uncertainty

Renato Quartullo, Andrea Garulli, Mirko Leomanni

This paper proposes a novel robust Model Predictive Control (MPC) scheme for linear discrete-time systems affected by model uncertainty described by interval matrices. The key feat…

eess.SY2025

Time-Optimal Model Predictive Control for Linear Systems with Multiplicative Uncertainties

Renato Quartullo, Andrea Garulli, Mirko Leomanni

This paper presents a time-optimal Model Predictive Control (MPC) scheme for linear discrete-time systems subject to multiplicative uncertainties represented by interval matrices.…

eess.SY2024★ 2 cited

Almost Global Trajectory Tracking for Quadrotors Using Thrust Direction Control on

Mirko Leomanni, Alberto Dionigi, Francesco Ferrante +2

Many of the existing works on quadrotor control address the trajectory tracking problem by employing a cascade design in which the translational and rotational dynamics are stabili…

cs.RO2023★ 5 cited

Exploring Deep Reinforcement Learning for Robust Target Tracking using Micro Aerial Vehicles

Alberto Dionigi, Mirko Leomanni, Alessandro Saviolo +2

The capability to autonomously track a non-cooperative target is a key technological requirement for micro aerial vehicles. In this paper, we propose an output feedback control sch…

eess.SY2023

Learning-based Parameter Optimization for a Class of Orbital Tracking Control Laws

Gianni Bianchini, Andrea Garulli, Antonio Giannitrapani +2

This paper presents a machine learning approach for tuning the parameters of a family of stabilizing controllers for orbital tracking. An augmented random search algorithm is deplo…

cs.RO2023★ 21 cited

D-VAT: End-to-End Visual Active Tracking for Micro Aerial Vehicles

Alberto Dionigi, Simone Felicioni, Mirko Leomanni +1

Visual active tracking is a growing research topic in robotics due to its key role in applications such as human assistance, disaster recovery, and surveillance. In contrast to pas…

eess.SY2021★ 20 cited

Variable-Horizon Guidance for Autonomous Rendezvous and Docking to a Tumbling Target

Mirko Leomanni, Renato Quartullo, Gianni Bianchini +2

In this paper, the trajectory planning problem for autonomous rendezvous and docking between a controlled spacecraft and a tumbling target is addressed. The use of a variable plann…

math.OC2021★ 23 cited

Optimal Low-Thrust Orbit Transfers Made Easy: A Direct Approach

Mirko Leomanni, Gianni Bianchini, Andrea Garulli +1

The optimization of low-thrust, multi-revolution orbit transfer trajectories is often regarded as a difficult problem in modern astrodynamics. In this paper, a flexible and computa…

eess.SY2020★ 11 cited

Satellite Relative Motion Modeling and Estimation via Nodal Elements

Mirko Leomanni, Andrea Garulli, Antonio Giannitrapani +1

In this paper, a new parametrization of the relative motion between two satellites orbiting a central body is presented. The parametrization is based on the nodal elements: a set o…

eess.SY2018★ 6 cited

An Adaptive Groundtrack Maintenance Scheme for Spacecraft with Electric Propulsion

Mirko Leomanni, Andrea Garulli, Antonio Giannitrapani +1

In this paper, the repeat-groundtrack orbit maintenance problem is addressed for spacecraft driven by electric propulsion. An adaptive solution is proposed, which combines an hyste…