113 citations · 138 across the 10 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…