Zobrazeno 1 - 10
of 1 130
pro vyhledávání: '"Lygeros, John"'
Ensuring safety is a key aspect in sequential decision making problems, such as robotics or process control. The complexity of the underlying systems often makes finding the optimal decision challenging, especially when the safety-critical system is
Externí odkaz:
http://arxiv.org/abs/2409.18000
In this paper, we consider the problem of predicting unknown targets from data. We propose Online Residual Learning (ORL), a method that combines online adaptation with offline-trained predictions. At a lower level, we employ multiple offline predict
Externí odkaz:
http://arxiv.org/abs/2409.04069
Autor:
Kavas, Baris, Balta, Efe C., Tucker, Michael R., Krishnadas, Raamadaas, Rupenyan, Alisa, Lygeros, John, Bambach, Markus
Open-loop control of laser powder bed fusion (LPBF) additive manufacturing (AM) has enabled the production of complex, high-criticality parts for various industries. This method relies on static parameter sets from extensive experimentation and simul
Externí odkaz:
http://arxiv.org/abs/2406.19096
We present a novel approach for the control of uncertain, linear time-invariant systems, which are perturbed by potentially unbounded, additive disturbances. We propose a \emph{doubly robust} data-driven state-feedback controller to ensure reliable p
Externí odkaz:
http://arxiv.org/abs/2405.18142
This work studies discrete-time discounted Markov decision processes with continuous state and action spaces and addresses the inverse problem of inferring a cost function from observed optimal behavior. We first consider the case in which we have ac
Externí odkaz:
http://arxiv.org/abs/2405.15509
Designing policies for a network of agents is typically done by formulating an optimization problem where each agent has access to state measurements of all the other agents in the network. Such policy designs with centralized information exchange re
Externí odkaz:
http://arxiv.org/abs/2405.00148
Autor:
Aboudonia, Ahmed, Lygeros, John
We propose a novel adaptive learning-based model predictive control (MPC) scheme for interconnected systems which can be decomposed into several smaller dynamically coupled subsystems with uncertain coupling. The proposed scheme is mainly divided int
Externí odkaz:
http://arxiv.org/abs/2404.16514
In this paper, we consider the problem of reference tracking in uncertain nonlinear systems. A neural State-Space Model (NSSM) is used to approximate the nonlinear system, where a deep encoder network learns the nonlinearity from data, and a state-sp
Externí odkaz:
http://arxiv.org/abs/2404.12097
In this study, we present a novel swarm-based approach for generating optimized stress-aligned trajectories for 3D printing applications. The method utilizes swarming dynamics to simulate the motion of virtual agents along the stress produced in a lo
Externí odkaz:
http://arxiv.org/abs/2404.10686
We consider inexact policy iteration methods for large-scale infinite-horizon discounted MDPs with finite spaces, a variant of policy iteration where the policy evaluation step is implemented inexactly using an iterative solver for linear systems. In
Externí odkaz:
http://arxiv.org/abs/2404.06136