113 citations · 138 across the 10 of their papers we have counts for
13 papers
Bounded Linear Programs for Data-Driven Optimal Control via Moment-Matching
Andrea Martinelli, Lucia Pezzetti, Niklas Schmid +2
Linear programming (LP) formulations offer a conceptually elegant approach to infinite-horizon, model-free nonlinear optimal control in continuous spaces. However, in addition to t…
Dissipativity-Based Data-Driven Decentralized Control of Interconnected Systems
Taiki Nakano, Ahmed Aboudonia, Jaap Eising +3
We propose data-driven decentralized control algorithms for stabilizing interconnected discrete-time linear time-invariant systems. We first derive a data-driven condition to synth…
Interconnection of (Q,S,R)-Dissipative Systems in Discrete Time
Andrea Martinelli, Ahmed Aboudonia, John Lygeros
Discrete-time systems cannot be passive unless there is a direct feedthrough from the input to the output. For passivity-based control to be exploited nevertheless, some authors in…
Data-driven optimal control via linear programming: boundedness guarantees
Lucia Falconi, Andrea Martinelli, John Lygeros
The linear programming (LP) approach is, together with value iteration and policy iteration, one of the three fundamental methods to solve optimal control problems in a dynamic pro…
Reconfigurable Plug-and-play Distributed Model Predictive Control for Reference Tracking
Ahmed Aboudonia, Andrea Martinelli, Nicolas Hoischen +1
A plug-and-play model predictive control (PnP MPC) scheme is proposed for varying-topology networks to track piecewise constant references. The proposed scheme allows subsystems to…
Data-Driven Optimal Control of Affine Systems: A Linear Programming Perspective
Andrea Martinelli, Matilde Gargiani, Marina Draskovic +1
In this letter, we discuss the problem of optimal control for affine systems in the context of data-driven linear programming. First, we introduce a unified framework for the fixed…
PAGE-PG: A Simple and Loopless Variance-Reduced Policy Gradient Method with Probabilistic Gradient Estimation
Matilde Gargiani, Andrea Zanelli, Andrea Martinelli +2
Despite their success, policy gradient methods suffer from high variance of the gradient estimate, which can result in unsatisfactory sample complexity. Recently, numerous variance…
Parallel and Flexible Dynamic Programming via the Randomized Mini-Batch Operator
Matilde Gargiani, Andrea Martinelli, Max Ruts Martinez +1
The Bellman operator constitutes the foundation of dynamic programming (DP). An alternative is presented by the Gauss-Seidel operator, whose evaluation, differently from that of th…
On the Synthesis of Bellman Inequalities for Data-Driven Optimal Control
Andrea Martinelli, Matilde Gargiani, John Lygeros
In the context of the linear programming (LP) approach to data-driven control, one assumes that the dynamical system is unknown but can be observed indirectly through data on its e…
Passivity-based Decentralized Control for Discrete-time Large-scale Systems
Ahmed Aboudonia, Andrea Martinelli, John Lygeros
Passivity theory has recently contributed to developing decentralized control schemes for large-scale systems. Many decentralized passivity-based control schemes are designed in co…
Control of Networked Systems by Clustering: The Degree of Freedom Concept
Andrea Martinelli, John Lygeros
We address the problem of local flux redistribution in networked systems. The aim is to detect a suitable cluster which is able to locally adsorb a disturbance by means of an appro…
Data-driven optimal control with a relaxed linear program
Andrea Martinelli, Matilde Gargiani, John Lygeros
The linear programming (LP) approach has a long history in the theory of approximate dynamic programming. When it comes to computation, however, the LP approach often suffers from…
Hierarchical Plug-and-Play Voltage/Current Controller of DC Microgrid Clusters with Grid-Forming/Feeding Converters: Line-independent Primary Stabilization and Leader-based Distributed Secondary Regulation
Renke Han, Michele Tucci, Raffaele Soloperto +3
Considering the structure of dc Microgrids (MGs) composed of grid-forming/feeding converters, a hierarchical Plug-and-Play (PnP) voltage/current control architecture for MG cluster…