Results 191 to 200 of about 16,805,949 (306)

On the Use of Learning Control for Improved Performance in Robot Control Systems

, 1997
Iterative learning control applied to a simplified model of a robot arm is studied. The iterative learning control input signal is used in combination with conventional feed-back and feed-forward control, and the aim is to let the learning control signal
Norrlöf, Mikael,, Gunnarsson, Svante,
core  

Patients improve, patterns persist: longitudinal stability of RA joint involvement patterns

Arthritis Care &Research, Accepted Article.
Objective Rheumatoid arthritis is a heterogeneous disease. Data‐driven approaches, from synovial histology to joint involvement patterns (JIPs), have sought to define clinically meaningful subgroups. Whether these subgroups represent stable phenotypes or transient disease states remains unclear.
Tjardo Maarseveen, Yasuo Nagafuchi, Kristina Lend, Seyam Alemyar, Herman Kasper Glas, Gerdur Gröndal, Bjorn Gudbjornsson, Espen A. Haavardsholm, Kim Hørslev Petersen, Jon Lampa, Merete Lund Hetland, Dan Nordström, Mike Nurmohamed, Mikkel Østergaard, Anna Rudin, Marte Schrumpf Heiberg, Tuulikki Sokka‐Isler, Till Uhlig, Josien Veris‐van Dieren, Ronald van Vollenhoven, Tom Huizinga, Marcel JT Reinders, Sytske Anne Bergstra, Erik van den Akker, Rachel Knevel   +24 more
wiley   +1 more source

Deep reinforcement learning control unlocks enhanced heat transfer in turbulent convection. [PDF]

Proc Natl Acad Sci U S A
Zhou Z, Zhu X.
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, Huang J, Wang J, Meng F, Gong H, Ping B.   +5 more
europepmc   +1 more source

Iterative learning control: algorithm development and experimental benchmarking

, 2009
This thesis concerns the general area of experimental benchmarking of Iterative Learning Control (ILC) algorithms using two experimental facilities. ILC is an approach which is suitable for applications where the same task is executed repeatedly over the
Cai, Zhonglun
core  

Unsupervised Machine Learning Identifies Shared Biomechanical and Clinical Variation in Individuals with Knee Osteoarthritis and High Body Mass Index

Arthritis Care &Research, EarlyView.
Objective High body mass index (BMI ≥ 27 kg/m2) and aberrant gait biomechanics are primary risk factors for knee osteoarthritis (KOA), yet the specific loading patterns linked to clinical disease features remain unclear. Unsupervised machine learning (ML) was used to identify vertical ground reaction force (vGRF) waveform modes of variation and ...
Ashley N. Buck, J. Elyse Borgert, Hyeon Lee, Liubov Arbeeva, Yvonne M. Golightly, Richard F. Loeser, Leigh F. Callahan, Stephen P. Messier, Brian Pietrosimone, Amanda E. Nelson, James S. Marron   +10 more
wiley   +1 more source

Knowledge-guided self-learning control strategy for mixed vehicle platoons with delays. [PDF]

Nat Commun
Wang J, Wang H, Song J, Chen X, Guo J, Li K, Li X, Huang B.   +7 more
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, Fitzpatrick MN, Triolo RJ, Reyes RD, Quinn RD, Audu M.   +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, Anthony Siming Chen, Guido Herrmann   +2 more
wiley   +1 more source

Reinforcement learning control method for greenhouse vegetable irrigation driven by dynamic clipping and negative incentive mechanism. [PDF]

Front Plant Sci
Tang R, Tang J, Abu Talip MS, Aridas NK, Guan B.   +4 more
europepmc   +1 more source