Zobrazeno 1 - 10
of 61
pro vyhledávání: '"Furieri, Luca"'
Publikováno v:
IEEE Open Journal of Control Systems (Volume: 3), 2024
The growing scale and complexity of safety-critical control systems underscore the need to evolve current control architectures aiming for the unparalleled performances achievable through state-of-the-art optimization and machine learning algorithms.
Externí odkaz:
http://arxiv.org/abs/2405.00871
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 build upon the Neural System Level Synthesis (Neur-SLS) framework and in
Externí odkaz:
http://arxiv.org/abs/2404.02820
Autor:
Martin, Andrea, Furieri, Luca
The increasing reliance on numerical methods for controlling dynamical systems and training machine learning models underscores the need to devise algorithms that dependably and efficiently navigate complex optimization landscapes. Classical gradient
Externí odkaz:
http://arxiv.org/abs/2403.09389
This paper characterizes a new parametrization of nonlinear networked incrementally $L_2$-bounded operators in discrete time. The distinctive novelty is that our parametrization is \emph{free} -- that is, a sparse large-scale operator with bounded in
Externí odkaz:
http://arxiv.org/abs/2311.13967
In this work, we focus on the design of optimal controllers that must comply with an information structure. State-of-the-art approaches do so based on the H2 or Hinfty norm to minimize the expected or worst-case cost in the presence of stochastic or
Externí odkaz:
http://arxiv.org/abs/2311.02068
Towards bridging classical optimal control and online learning, regret minimization has recently been proposed as a control design criterion. This competitive paradigm penalizes the loss relative to the optimal control actions chosen by a clairvoyant
Externí odkaz:
http://arxiv.org/abs/2306.14561
We consider control of uncertain linear time-varying stochastic systems from the perspective of regret minimization. Specifically, we focus on the problem of designing a feedback controller that minimizes the loss relative to a clairvoyant optimal po
Externí odkaz:
http://arxiv.org/abs/2304.14835
Autor:
Martinelli, Daniele, Galimberti, Clara Lucía, Manchester, Ian R., Furieri, Luca, Ferrari-Trecate, Giancarlo
In this work, we introduce and study a class of Deep Neural Networks (DNNs) in continuous-time. The proposed architecture stems from the combination of Neural Ordinary Differential Equations (Neural ODEs) with the model structure of recently introduc
Externí odkaz:
http://arxiv.org/abs/2304.02976
We consider control of dynamical systems through the lens of competitive analysis. Most prior work in this area focuses on minimizing regret, that is, the loss relative to an ideal clairvoyant policy that has noncausal access to past, present, and fu
Externí odkaz:
http://arxiv.org/abs/2211.07389
Publikováno v:
In European Journal of Control November 2024 80 Part A
