Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Siriya, Seth"'
We present task-oriented Koopman-based control that utilizes end-to-end reinforcement learning and contrastive encoder to simultaneously learn the Koopman latent embedding, operator, and associated linear controller within an iterative loop. By prior
Externí odkaz:
http://arxiv.org/abs/2309.16077
We consider the problem of adaptive stabilization for discrete-time, multi-dimensional linear systems with bounded control input constraints and unbounded stochastic disturbances, where the parameters of the true system are unknown. To address this c
Externí odkaz:
http://arxiv.org/abs/2304.00569
We propose a certainty-equivalence scheme for adaptive control of scalar linear systems subject to additive, i.i.d. Gaussian disturbances and bounded control input constraints, without requiring prior knowledge of the bounds of the system parameters,
Externí odkaz:
http://arxiv.org/abs/2209.07040
We propose a safety-guaranteed planning and control framework for unmanned surface vessels (USVs), using Gaussian processes (GPs) to learn uncertainties. The uncertainties encountered by USVs, including external disturbances and model mismatches, are
Externí odkaz:
http://arxiv.org/abs/2205.04859
This paper addresses the trajectory planning for multiple autonomous underwater vehicles (AUVs) in strong waves that can disturb the AUVs' trajectory tracking ability and cause obstacle and inter-vehicle collisions. A novel approach based on Hamilton
Externí odkaz:
http://arxiv.org/abs/2011.13505
One spectrum on which robotic control paradigms lie is the degree in which a model of the environment is involved, from methods that are completely model-free such as model-free RL, to methods that require a known model such as optimal control, with
Externí odkaz:
http://arxiv.org/abs/2011.02073