14 citations · 34 across the 47 of their papers we have counts for
49 papers
An Accelerated Proximal Gradient Method for Bures-Wasserstein Space
Jakob Nylöf, Sloan Nietert, Julien Pallage +3
We study composite optimization over the Bures--Wasserstein (BW) space of Gaussian measures. Our main observation is that a useful class of BW problems can be lifted into the Eucli…
Optimality of Affine Policies in Distributionally Robust Linear-Quadratic Control with Temporally Correlated Noise
Jakob Nylöf, Daniel Kuhn, John Lygeros +1
We study finite-horizon distributionally robust linear-quadratic control with disturbances that can be arbitrarily correlated in time. The ambiguity set is modeled as a single 2-Wa…
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…
Sinkhorn Ambiguity Sets for Distributionally Robust Control: Convexity, Weak Compactness, and Tractability
Riccardo Cescon, Andrea Martin, Giancarlo Ferrari-Trecate
Classical stochastic control assumes perfect knowledge of the uncertainty affecting the plant. In practice, however, such information is often incomplete. To address this limitatio…
Data-Driven Optimal Distributed Controller Synthesis via Spatial Regret
Vaibhav Gupta, Daniele Martinelli, Giancarlo Ferrari-Trecate +2
In this paper, we present a novel method for synthesising an optimal distributed spatial regret controller using experimentally obtained frequency-response data. Spatial regret pro…
Controller Design for Structured State-space Models via Contraction Theory
Muhammad Zakwan, Vaibhav Gupta, Alireza Karimi +2
This paper presents an indirect data-driven output feedback controller synthesis for nonlinear systems, leveraging Structured State-space Models (SSMs) as surrogate models. SSMs ha…
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…
Resilient AFE Drive Control using Neural Networks with Tracking Guarantees
Nicolas Kirsch, Catalin Arghir, Silvia Mastellone +1
Industrial installations across several sectors have seen a dramatic increase in productivity, accuracy and efficiency over the last decade due to expanded utilization of medium vo…
PAC-Bayesian Optimal Control with Stability and Generalization Guarantees
Mahrokh Ghoddousi Boroujeni, Clara Lucía Galimberti, Andreas Krause +1
Stochastic Nonlinear Optimal Control (SNOC) seeks to minimize a cost function that accounts for random disturbances acting on a nonlinear dynamical system. Since the expectation ov…
A graph-informed regret metric for optimal distributed control
Daniele Martinelli, Andrea Martin, Giancarlo Ferrari-Trecate +1
We consider the optimal control of large-scale systems using distributed controllers whose network topology mirrors the coupling graph between subsystems. In this work, we introduc…
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…
On the Global Optimality of Linear Policies for Sinkhorn Distributionally Robust Linear Quadratic Control
Riccardo Cescon, Andrea Martin, Giancarlo Ferrari-Trecate
The Linear Quadratic Gaussian (LQG) regulator is a cornerstone of optimal control theory, yet its performance can degrade significantly when the noise distributions deviate from th…
En Route Path-planning for Partially Occupied Vehicles in Ride-pooling Systems
Pengbo Zhu, Giancarlo Ferrari-Trecate, Nikolas Geroliminis
Ride-pooling services, such as UberPool and Lyft Shared Saver, enable a single vehicle to serve multiple customers within one shared trip. Efficient path-planning algorithms are cr…
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…
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…
Data-driven Distributionally Robust Control Based on Sinkhorn Ambiguity Sets
Riccardo Cescon, Andrea Martin, Giancarlo Ferrari-Trecate
As the complexity of modern control systems increases, it becomes challenging to derive an accurate model of the uncertainty that affects their dynamics. Wasserstein Distributional…
Parametrizations of All Stable Closed-loop Responses: From Theory to Neural Network Control Design
Clara Lucía Galimberti, Luca Furieri, Giancarlo Ferrari-Trecate
The complexity of modern control systems necessitates architectures that achieve high performance while ensuring robust stability, particularly for nonlinear systems. In this work,…
Contractive Dynamical Imitation Policies for Efficient Out-of-Sample Recovery
Amin Abyaneh, Mahrokh G. Boroujeni, Hsiu-Chin Lin +1
Imitation learning is a data-driven approach to learning policies from expert behavior, but it is prone to unreliable outcomes in out-of-sample (OOS) regions. While previous resear…
Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach
Muhammad Zakwan, Giancarlo Ferrari-Trecate
The control of large-scale cyber-physical systems requires optimal distributed policies relying solely on limited communication with neighboring agents. However, computing stabiliz…