Results 71 to 80 of about 36,602 (258)

PPO‐Based Reinforcement Learning for the Semi‐Active Vibration Control of MDOF Platform

AI &Innovation, EarlyView.
ABSTRACT Aiming at the coupled vibration problem of a multi‐degree‐of‐freedom (MDOF) vibration isolation platform under eccentric excitation, this paper proposes a semi‐active vibration control strategy based on Proximal Policy Optimization (PPO) ‐based reinforcement learning (PPO RL).
Wei Huang, Jian Xu
wiley   +1 more source

Transfer Learning for LQR Control

CoRR
In this paper, we study a transfer learning framework for Linear Quadratic Regulator (LQR) control, where (i) the dynamics of the system of interest (target system) are unknown and only a short trajectory of impulse responses from the target system is provided, and (ii) impulse responses are available from $N$ source systems with different dynamics. We
Taosha Guo, Fabio Pasqualetti
openaire   +2 more sources

Sparse Wide-Area Control of Power Systems using Data-driven Reinforcement Learning

, 2018
In this paper we present an online wide-area oscillation damping control (WAC) design for uncertain models of power systems using ideas from reinforcement learning.
Chakrabortty, Aranya, Dizche, Amirhassan Fallah, Duel-Hallen, Alexandra   +2 more
core   +1 more source

Risk‐aware safe reinforcement learning for control of stochastic linear systems

Asian Journal of Control, EarlyView.
Abstract This paper presents a risk‐aware safe reinforcement learning (RL) control design for stochastic discrete‐time linear systems. Rather than using a safety certifier to myopically intervene with the RL controller, a risk‐informed safe controller is also learned besides the RL controller, and the RL and safe controllers are combined together ...
Babak Esmaeili, Nariman Niknejad, Hamidreza Modares   +2 more
wiley   +1 more source

Constrained LQR using online decomposition techniques [PDF]

2016 IEEE 55th Conference on Decision and Control (CDC), 2016
This paper presents an algorithm to solve the infinite horizon constrained linear quadratic regulator (CLQR) problem using operator splitting methods. First, the CLQR problem is reformulated as a (finite-time) model predictive control (MPC) problem without terminal constraints.
Laura Ferranti, Giorgos Stathopoulos, Colin N. Jones, Tamás Keviczky   +3 more
openaire   +2 more sources

Performance improvement of discrete‐time linear‐quadratic regulators applied to uncertain linear systems using the Tikhonov regularization method

Asian Journal of Control, EarlyView.
Abstract The linear‐quadratic regulator (LQR) problem of optimal control of an uncertain discrete‐time linear system (DTLS) is revisited in this paper from the perspective of Tikhonov regularization. We show that an optimally chosen regularization parameter reduces, compared to the classical LQR, the values of a scalar error function, as well as the ...
Fernando Pazos, Amit Bhaya
wiley   +1 more source

Policy Evaluation in Distributional LQR

IEEE Transactions on Automatic Control
Distributional reinforcement learning (DRL) enhances the understanding of the effects of the randomness in the environment by letting agents learn the distribution of a random return, rather than its expected value as in standard RL. At the same time, a main challenge in DRL is that policy evaluation in DRL typically relies on the representation of the
Zifan Wang 0002, Yulong Gao 0001, Siyi Wang, Michael M. Zavlanos, Alessandro Abate, Karl Henrik Johansson   +5 more
openaire   +3 more sources

Another Short and Elementary Proof of Strong Subadditivity of Quantum Entropy

, 2006
A short and elementary proof of the joint convexity of relative entropy is presented, using nothing beyond linear algebra. The key ingredients are an easily verified integral representation and the strategy used to prove the Cauchy-Schwarz inequality ...
Araki, Araki, Choi, E Stinespring, King, King, Lesniewski, Lieb, Lieb, Lieb, Lindblad, Mary Beth Ruskai, Nielsen, Ohya, Paulsen, Petz, Petz, Ruskai, Uhlmann, von Neumann, von Neumann, Wehrl, Yuen   +22 more
core   +1 more source

Adaptive Neural Network Feedforward Control for Dynamically Substructured Systems [PDF]

, 2014
(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or ...
Li, G, Na, J, Ren, X, Stoten, DP
core   +1 more source

Point and Risk estImation Using an enSemble of Models for Nowcasting: PRISM‐Now

Journal of Forecasting, EarlyView.
ABSTRACT We propose PRISM‐Now, a novel ensemble forecasting system for near‐term GDP projection. Recognizing that relevant economic information evolves over time, we treat forecasts from multiple base models as draws from a mixture distribution of “good” and “bad” estimates, whose composition changes continuously and cannot be identified ex ante.
Beomseok Seo, Hyungbae Cho, Dongjae Lee
wiley   +1 more source