2 citations · 2 across the 7 of their papers we have counts for
7 papers
Stability-Preserving Online Adaptation of Neural Closed-loop Maps
Danilo Saccani, Luca Furieri, Giancarlo Ferrari-Trecate
The growing complexity of modern control tasks calls for controllers that can react online as objectives and disturbances change, while preserving closed-loop stability. Recent app…
Safety-Aware Performance Boosting for Constrained Nonlinear Systems
Danilo Saccani, Haoming Shen, Luca Furieri +1
We study a control architecture for nonlinear constrained systems that integrates a performance-boosting (PB) controller with a scheduled Predictive Safety Filter (PSF). The PSF ac…
Learning stabilising policies for constrained nonlinear systems
Daniele Ravasio, Danilo Saccani, Marcello Farina +1
This work proposes a two-layered control scheme for constrained nonlinear systems represented by a class of recurrent neural networks and affected by additive disturbances. In part…
Constrained Performance Boosting Control for Nonlinear Systems
Gianluca Giacomelli, Danilo Saccani, Siep Weiland +2
We present the Alternating Direction Method of Multipliers (ADMM) for Performance Boosting (PB), an approach for designing neural controllers for stable nonlinear systems subject t…
MAD: A Magnitude And Direction Policy Parametrization for Stability Constrained Reinforcement Learning
Luca Furieri, Sucheth Shenoy, Danilo Saccani +2
We introduce magnitude and direction (MAD) policies, a policy parameterization for reinforcement learning (RL) that preserves Lp closed-loop stability for nonlinear dynamical syste…
Optimal distributed control with stability guarantees by training a network of neural closed-loop maps
Danilo Saccani, Leonardo Massai, Luca Furieri +1
This paper proposes a novel approach to improve the performance of distributed nonlinear control systems while preserving stability by leveraging Deep Neural Networks (DNNs). We bu…
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…