Arthritis Care &Research, Accepted Article.Biomedical research involving United States Veterans continues to advance healthcare beyond the Veterans Health Administration. This is particularly true in rheumatoid arthritis (RA), where Veteran‐centric research has uncovered novel insights into pathogenesis, risk factors, and disease manifestations, informing clinical care and research across both ...
Austin M. Wheeler +20 more
wiley +1 more source
Software-defined self-learning control system for industrial robots by using reinforcement learning. [PDF]
Sci RepMoon J, Kim M, Lee T, Um J.
europepmc +1 more source
The Driving Waveform Design Method of Power-Law Fluid Piezoelectric Printing Based on Iterative Learning Control. [PDF]
Sensors (Basel), 2022 Peng J +5 more
europepmc +1 more source
A Q‐Learning Algorithm to Solve the Two‐Player Zero‐Sum Game Problem for Nonlinear Systems
International Journal of Adaptive Control and Signal Processing, Volume 39, Issue 3, Page 566-581, March 2025.A Q‐learning algorithm to solve the two‐player zero‐sum game problem for nonlinear systems. ABSTRACT This paper deals with the two‐player zero‐sum game problem, which is a bounded L2$$ {L}_2 $$‐gain robust control problem. Finding an analytical solution to the complex Hamilton‐Jacobi‐Issacs (HJI) equation is a challenging task.
Afreen Islam +2 more
wiley +1 more source
PPO-Based Reinforcement Learning Control of a Flapping-Wing Robot with a Bio-Inspired Sensing and Actuation Feather Unit. [PDF]
Sensors (Basel)Hussain S, Messaoudi M, Imran M, Tang D.
europepmc +1 more source
Biologically Inspired Optimal Terminal Iterative Learning Control for the Swing Phase of Gait in a Hybrid Neuroprosthesis: A Modeling Study. [PDF]
Bioengineering (Basel), 2022 Makowski NS +5 more
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dynoGP: Deep Gaussian Processes for Dynamic System Identification
International Journal of Adaptive Control and Signal Processing, EarlyView.This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli +2 more
wiley +1 more source
Iterative learning control of neuronal firing based on FHN and HR models. [PDF]
PLoS OneYuan C, Wang X, Li X, Zhao Y.
europepmc +1 more source
Dynamic Input Deep Learning Control of Artificial Avatars in a Multi-Agent Joint Motor Task. [PDF]
Front Robot AI, 2021 Lombardi M, Liuzza D, di Bernardo M.
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Advanced Engineering Materials, EarlyView.Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt +8 more
wiley +1 more source