11 citations · 14 across the 13 of their papers we have counts for
6 papers · 1 filter
Context-Enriched Performance Boosting via Operator Decomposition
Leonardo Massai, Sebastiano Messina, Nicolas Kirsch +1
Performance Boosting (PB) is a control framework that, for a pre-stabilized system subject to process disturbances, parametrizes the controllers that preserve closed…
Free Parametrization of L_2-Bounded Structured State-Space Controllers for Nonlinear Control with Stability Guarantees
Muhammad Zakwan, Leonardo Massai, Efe C. Balta +1
Designing stabilizing control policies for nonlinear systems while optimizing complex objectives remains a formidable challenge. Neural networks (NNs), despite their expressive pow…
Boosting the transient performance of reference tracking controllers with neural networks
Nicolas Kirsch, Leonardo Massai, Giancarlo Ferrari-Trecate
Reference tracking is a key objective in many control systems, including those characterized by complex nonlinear dynamics. In these settings, traditional control approaches can ef…
L2RU: a Structured State Space Model with prescribed L2-bound
Leonardo Massai, Muhammad Zakwan, Giancarlo Ferrari-Trecate
Structured state-space models (SSMs) have recently emerged as a powerful architecture at the intersection of machine learning and control, featuring layers composed of discrete-tim…
Neural Identification of Feedback-Stabilized Nonlinear Systems
Mahrokh G. Boroujeni, Laura Meroi, Leonardo Massai +2
Neural networks have demonstrated remarkable success in modeling nonlinear dynamical systems. However, identifying these systems from closed-loop experimental data remains a challe…
Unconstrained learning of networked nonlinear systems via free parametrization of stable interconnected operators
Leonardo Massai, Danilo Saccani, Luca Furieri +1
This paper characterizes a new parametrization of nonlinear networked incrementally -bounded operators in discrete time. The distinctive novelty is that our parametrization is…