Zobrazeno 1 - 10
of 263
pro vyhledávání: '"Martin, Andrea P."'
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
Autor:
Brouillon, Jean-Sébastien, Martin, Andrea, Lygeros, John, Dörfler, Florian, Trecate, Giancarlo Ferrari
We study control of constrained linear systems with only partial statistical information about the uncertainty affecting the system dynamics and the sensor measurements. Specifically, given a finite collection of disturbance realizations drawn from a
Externí odkaz:
http://arxiv.org/abs/2312.07324
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
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
As we move towards safety-critical cyber-physical systems that operate in non-stationary and uncertain environments, it becomes crucial to close the gap between classical optimal control algorithms and adaptive learning-based methods. In this paper,
Externí odkaz:
http://arxiv.org/abs/2203.00358
As we transition towards the deployment of data-driven controllers for black-box cyberphysical systems, complying with hard safety constraints becomes a primary concern. Two key aspects should be addressed when input-output data are corrupted by nois
Externí odkaz:
http://arxiv.org/abs/2105.10280
Recent work in data-driven control has revived behavioral theory to perform a variety of complex control tasks, by directly plugging libraries of past input-output trajectories into optimal control problems. Despite recent advances, a key aspect rema
Externí odkaz:
http://arxiv.org/abs/2102.13338
Human beings possess the most sophisticated computational machinery in the known universe. We can understand language of rich descriptive power, and communicate in the same environment with astonishing clarity. Two of the many contributors to the int
Externí odkaz:
http://arxiv.org/abs/2012.02038