Zobrazeno 1 - 10
of 130
pro vyhledávání: '"Iannelli, Andrea"'
Autor:
Iannelli, Andrea, Postoyan, Romain
We consider the data-driven stabilization of discrete-time linear time-varying systems. The controller is defined as a linear state-feedback law whose gain is adapted to the plant changes through a data-based event-triggering rule. To do so, we monit
Externí odkaz:
http://arxiv.org/abs/2405.14426
Continuous-time adaptive controllers for systems with a matched uncertainty often comprise an online parameter estimator and a corresponding parameterized controller to cancel the uncertainty. However, such methods are often unimplementable, as they
Externí odkaz:
http://arxiv.org/abs/2404.02023
Model Predictive Control (MPC) can be applied to safety-critical control problems, providing closed-loop safety and performance guarantees. Implementation of MPC controllers requires solving an optimization problem at every sampling instant, which is
Externí odkaz:
http://arxiv.org/abs/2401.07780
Autor:
Song, Bowen, Iannelli, Andrea
The goal of this article is to study fundamental mechanisms behind so-called indirect and direct data-driven control for unknown systems. Specifically, we consider policy iteration applied to the linear quadratic regulator problem. Two iterative proc
Externí odkaz:
http://arxiv.org/abs/2401.06721
Suboptimal methods in optimal control arise due to a limited computational budget, unknown system dynamics, or a short prediction window among other reasons. Although these methods are ubiquitous, their transient performance remains relatively unstud
Externí odkaz:
http://arxiv.org/abs/2312.05607
Data-driven predictive control methods based on the Willems' fundamental lemma have shown great success in recent years. These approaches use receding horizon predictive control with nonparametric data-driven predictors instead of model-based predict
Externí odkaz:
http://arxiv.org/abs/2312.02758
This paper presents a novel approach for the identification of linear time-periodic (LTP) systems in continuous time. This method is based on harmonic modeling and consists in converting any LTP system into an equivalent LTI system with infinite dime
Externí odkaz:
http://arxiv.org/abs/2309.16273
We present an online learning analysis of minimax adaptive control for the case where the uncertainty includes a finite set of linear dynamical systems. Precisely, for each system inside the uncertainty set, we define the model-based regret by compar
Externí odkaz:
http://arxiv.org/abs/2307.07268
Inexact methods for model predictive control (MPC), such as real-time iterative schemes or time-distributed optimization, alleviate the computational burden of exact MPC by providing suboptimal solutions. While the asymptotic stability of such algori
Externí odkaz:
http://arxiv.org/abs/2305.10085
This work presents a dual system-level parameterization (D-SLP) method for closed-loop system identification. The recent system-level synthesis framework parameterizes all stabilizing controllers via linear constraints on closed-loop response functio
Externí odkaz:
http://arxiv.org/abs/2304.02379