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of 10 511
pro vyhledávání: '"Safe control"'
We investigate the problem of safe control synthesis for systems operating in environments with uncontrollable agents whose dynamics are unknown but coupled with those of the controlled system. This scenario naturally arises in various applications,
Externí odkaz:
http://arxiv.org/abs/2410.15660
Varying dynamics pose a fundamental difficulty when deploying safe control laws in the real world. Safety Index Synthesis (SIS) deeply relies on the system dynamics and once the dynamics change, the previously synthesized safety index becomes invalid
Externí odkaz:
http://arxiv.org/abs/2409.09882
Autor:
Joswig-Jones, Trager, Zhang, Baosen
Grid-interfacing inverters allow renewable resources to be connected to the electric grid and offer fast and programmable control responses. However, inverters are subject to significant physical constraints. One such constraint is a current magnitud
Externí odkaz:
http://arxiv.org/abs/2409.13890
Autor:
Miyaoka, Yuya, Inoue, Masaki
This paper proposes a control-based framework for aligning large language models (LLMs) by leveraging a control barrier function (CBF) to ensure user-desirable text generation. The presented framework applies the safety filter, designed based on the
Externí odkaz:
http://arxiv.org/abs/2408.15625
Safe control of neural network dynamic models (NNDMs) is important to robotics and many applications. However, it remains challenging to compute an optimal safe control in real time for NNDM. To enable real-time computation, we propose to use a sound
Externí odkaz:
http://arxiv.org/abs/2404.13456
Safe control for dynamical systems is critical, yet the presence of unknown dynamics poses significant challenges. In this paper, we present a learning-based control approach for tracking control of a class of high-order systems, operating under the
Externí odkaz:
http://arxiv.org/abs/2405.00822
This paper addresses the design of safety certificates for stochastic systems, with a focus on ensuring long-term safety through fast real-time control. In stochastic environments, set invariance-based methods that restrict the probability of risk ev
Externí odkaz:
http://arxiv.org/abs/2404.16883
Autor:
Mazouz, Rayan, Skovbekk, John, Mathiesen, Frederik Baymler, Frew, Eric, Laurenti, Luca, Lahijanian, Morteza
This paper introduces a method of identifying a maximal set of safe strategies from data for stochastic systems with unknown dynamics using barrier certificates. The first step is learning the dynamics of the system via Gaussian process (GP) regressi
Externí odkaz:
http://arxiv.org/abs/2405.00136
Reinforcement Learning (RL) is a powerful method for controlling dynamic systems, but its learning mechanism can lead to unpredictable actions that undermine the safety of critical systems. Here, we propose RL with Adaptive Regularization (RL-AR), an
Externí odkaz:
http://arxiv.org/abs/2404.15199
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